The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These system...The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These systems,particularly in IoT networks,rely on numerous interconnected devices that handle time-sensitive data for critical applications.In related approaches,trusted communication and reliable device interaction have been overlooked,thereby lowering security when sharing sensitive IoT data.Moreover,it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment.In this research,an Artificial Intelligence(AI)recommended fault-tolerant framework is proposed that leverages blockchain technology,aiming to enhance device trustworthiness and ensure data privacy.In addition,the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing,thereby enabling seamless transmission in smart cities integrated with lightweight computing.To evaluate dynamic network conditions,the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services.Using simulations,the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics,demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.展开更多
Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or items.Collaborative filtering(CF)is a widely used personalization technique that lever...Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or items.Collaborative filtering(CF)is a widely used personalization technique that leverages user-item interactions to generate recommendations.However,it struggles with challenges like the cold-start problem,scalability issues,and data sparsity.To address these limitations,we develop a Graph Convolutional Networks(GCNs)model that captures the complex network of interactions between users and items,identifying subtle patterns that traditional methods may overlook.We integrate this GCNs model into a federated learning(FL)framework,enabling themodel to learn fromdecentralized datasets.This not only significantly enhances user privacy—a significant improvement over conventionalmodels but also reassures users about the safety of their data.Additionally,by securely incorporating demographic information,our approach further personalizes recommendations and mitigates the coldstart issue without compromising user data.We validate our RSs model using the openMovieLens dataset and evaluate its performance across six key metrics:Precision,Recall,Area Under the Receiver Operating Characteristic Curve(ROC-AUC),F1 Score,Normalized Discounted Cumulative Gain(NDCG),and Mean Reciprocal Rank(MRR).The experimental results demonstrate significant enhancements in recommendation quality,underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems.展开更多
The exponential growth of over-the-top(OTT)entertainment has fueled a surge in content consumption across diverse formats,especially in regional Indian languages.With the Indian film industry producing over 1500 films...The exponential growth of over-the-top(OTT)entertainment has fueled a surge in content consumption across diverse formats,especially in regional Indian languages.With the Indian film industry producing over 1500 films annually in more than 20 languages,personalized recommendations are essential to highlight relevant content.To overcome the limitations of traditional recommender systems-such as static latent vectors,poor handling of cold-start scenarios,and the absence of uncertainty modeling-we propose a deep Collaborative Neural Generative Embedding(C-NGE)model.C-NGE dynamically learns user and item representations by integrating rating information and metadata features in a unified neural framework.It uses metadata as sampled noise and applies the reparameterization trick to capture latent patterns better and support predictions for new users or items without retraining.We evaluate CNGE on the Indian Regional Movies(IRM)dataset,along with MovieLens 100 K and 1 M.Results show that our model consistently outperforms several existing methods,and its extensibility allows for incorporating additional signals like user reviews and multimodal data to enhance recommendation quality.展开更多
A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated ...A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as bank-ing, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the us-age of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be ap-plied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced re-search on Blockchain and recommendation systems. We aim at building a se-cure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recom-mendation system with acceptable performance.展开更多
Gamification in education enables for the holistic optimization of the learning process,empowering learners to ameliorate their digital,cognitive,emotional and social skills,via their active experimentation with game ...Gamification in education enables for the holistic optimization of the learning process,empowering learners to ameliorate their digital,cognitive,emotional and social skills,via their active experimentation with game design elements,accompanying pertinent pedagogical objectives of interest.This paper focuses on a cross-platform,innovative,gamified,educational learning system product,funded by the Hellenic Republic Ministry of Development and Investments:howlearn.By applying gamification techniques,in 3D virtual environments,within which,learners fulfil STEAM(Science,Technology,Engineering,Arts and Mathematics)-related Experiments(Simulations,Virtual Labs,Interactive Storytelling Scenarios,Decision Making Case Studies),howlearn covers learners’subject material,while,simultaneously,functioning,as an Authoring Gamification Tool and as a Game Metrics Repository;users’metrics are being,dynamically,analyzed,through Machine Learning Algorithms.Consequently,the System learns from the data and learners receive Personalized Feedback Report Dashboards of their overall performance,weaknesses,interests and general class competency.A Custom Recommendation System(Collaborative Filtering,Content-Based Filtering)then supplies suggestions,representing the best matches between Experiments and learners,while also focusing on the reinforcement of the learning weaknesses of the latter.Ultimately,by optimizing the Accuracy,Performance and Predictive capability of the Personalized Feedback Report,we provide learners with scientifically valid performance assessments and educational recommendations,thence intensifying sustainable,learner-centered education.展开更多
Modern marketing strategies have transformed through the combined power of Artificial Intelligence(AI)and Business Intelligence(BI)which improve customer segmentation and personalize marketing activities.This research...Modern marketing strategies have transformed through the combined power of Artificial Intelligence(AI)and Business Intelligence(BI)which improve customer segmentation and personalize marketing activities.This research examines how AI recommendation systems alongside BI tools influence marketing performance through customer interaction and conversion metrics.The research shows how AI and BI technologies produce effective marketing initiatives by analyzing consumer behavior data from transaction histories,browsing patterns,and social media activities.The study shows major enhancements in essential performance metrics including click-through rates and conversion rates with increased customer satisfaction when businesses implement AI-based systems over traditional marketing techniques.The research indicates that businesses using BI tools to implement AI-based customer segmentation achieve better conversion rates across different consumer demographics.Organizations that utilize both AI and BI systems can develop market advantages by improving customer targeting methods and enhancing their advertising approaches.The study offers important information that helps businesses boost their marketing performance while keeping pace with changing consumer behaviors in a competitive environment.展开更多
Increasing amounts of financial data demand sophisticated analytics to develop sound recommendation models.This article discusses combining time series analysis and association rule mining for big data in Hadoop and S...Increasing amounts of financial data demand sophisticated analytics to develop sound recommendation models.This article discusses combining time series analysis and association rule mining for big data in Hadoop and Spark to enrich financial product recommendation engines.The paper is an integrated analysis of two types of prediction algorithms:AutoRegressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)networks to forecast user behavior and demand for financial services in the future from transactional history.The ARIMA model is used as the default while the LSTM model is used to represent non-linear dependencies and give a more dynamic forecast.association rule mining–in particular the Apriori algorithm–is used to find latent patterns and relationships between user transactions and financial products.This article illustrates how time series forecasting and association rule mining can be merged to bring a more useful financial recommendation.The hybrid approach,which combines both approaches,proves to increase user interaction and recommendation accuracy by 20%compared to the previous systems,according to experiments.The paper emphasises the possibilities of using big data in the construction of scalable,individualized financial recommendation systems.展开更多
Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the cor...Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.展开更多
Cyber Physical Social Intelligence(CPSI)emphasizes the integration of social information and artificial system information from virtual spaces,enabling Social Intelligence Recommendation Systems(SIRS)to make intellige...Cyber Physical Social Intelligence(CPSI)emphasizes the integration of social information and artificial system information from virtual spaces,enabling Social Intelligence Recommendation Systems(SIRS)to make intelligent decisions and optimizations based on more comprehensive data,thereby enhancing the accuracy of recommendations and user experience.However,as the combined application of CPSI and SIRS becomes increasingly widespread,they also face the risk of shilling attacks.Traditional shilling attacks are limited in terms of low stealthiness,specificity to certain systems,and generation of unrealistic fake profiles.In this paper,we propose a black-box attack method,Real User Preference Generative Adversarial Networks(RUP-GAN),based on adversarial learning.RUP-GAN optimizes the authenticity of user profiles and enhances the hit rate of target items within users’top-k recommendation lists.Through experiments conducted on real-world datasets,it has been proved that RUP-GAN surpasses baseline shilling attack methods in attack effectiveness,transferability,and invisibility.Our proposed model can effectively mitigate the risks posed by shilling attacks,and provide valuable insights for the defense research of CPSI and SIRS.展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model′s interpretability and accuracy.This paper introduces an end-to-end d...Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model′s interpretability and accuracy.This paper introduces an end-to-end deep learning model,named representation-enhanced knowledge graph convolutional networks(RKGCN),which dynamically analyses each user′s preferences and makes a recommendation of suitable items.It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs.RKGCN is able to offer more personalized and relevant recommendations in three different scenarios.The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies,books,and music.展开更多
This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems,addressing the lack of depth and precision in existing literature.It proposes a two-pronged approach:a thorough anal...This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems,addressing the lack of depth and precision in existing literature.It proposes a two-pronged approach:a thorough analysis of current algorithms and a novel,hierarchical taxonomy for precise categorization.The taxonomy is based on a tri-level hierarchy,starting with the methodology category and narrowing down to specific techniques.Such a framework allows for a structured and comprehensive classification of algorithms,assisting researchers in understanding the interrelationships among diverse algorithms and techniques.Covering a wide range of algorithms,this taxonomy first categorizes algorithms into four main analysis types:user and item similarity based methods,hybrid and combined approaches,deep learning and algorithmic methods,and mathematical modeling methods,with further subdivisions into sub-categories and techniques.The paper incorporates both empirical and experimental evaluations to differentiate between the techniques.The empirical evaluation ranks the techniques based on four criteria.The experimental assessments rank the algorithms that belong to the same category,sub-category,technique,and sub-technique.Also,the paper illuminates the future prospects of big data techniques in recommendation systems,underscoring potential advancements and opportunities for further research in this fields.展开更多
Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been ...Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.展开更多
Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used i...Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.展开更多
To accommodate the tremendous increase of mobile data traffic,cache-enabled device-to-device(D2D)communication has been taken as a promising technique to release the heavy burden of cellular networks since popular con...To accommodate the tremendous increase of mobile data traffic,cache-enabled device-to-device(D2D)communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers.As a result,cellular traffic can be offloaded and an enhanced system performance can be attainable.However,due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users,the requested contents are most likely not be proactively cached,inducing lower cache hit ratio.Recommendation system,on the other hand,is able to reshape users’request schema,mitigating the heterogeneity to some extent,and hence it can boost the gain of edge caching.In this paper,the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated,taking into account the constraints on the cache capacity budget and the total number of recommended files per user,in which the contents are sharing between the users that trust each other.The minimization problem is an integer non-convex and non-linear programming,which is in general NP-hard.Therewith,we propose a timeefficient joint recommendation and caching decision scheme.Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies.展开更多
Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive...Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.展开更多
The recommendation system(RS)on the strength of Graph Neural Networks(GNN)perceives a user-item interaction graph after collecting all items the user has interacted with.Afterward the RS performs neighborhood aggregat...The recommendation system(RS)on the strength of Graph Neural Networks(GNN)perceives a user-item interaction graph after collecting all items the user has interacted with.Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession.However,user preferences are dynamic.With the passage of time and some trend guidance,users may generate some short-term preferences,which are more likely to lead to user-item interactions.A GNN recommendation based on long-and short-term preference(LSGNN)is proposed to address the above problems.LSGNN consists of four modules,using a GNN combined with the attention mechanism to extract long-term preference features,using Bidirectional Encoder Representation from Transformers(BERT)and the attention mechanism combined with Bi-Directional Gated Recurrent Unit(Bi-GRU)to extract short-term preference features,using Convolutional Neural Network(CNN)combined with the attention mechanism to add title and description representations of items,finally inner-producing long-term and short-term preference features as well as features of items to achieve recommendations.In experiments conducted on five publicly available datasets from Amazon,LSGNN is superior to state-of-the-art personalized recommendation techniques.展开更多
Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classification of this R...Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classification of this RS research is limited and needs to be improved.The previous method did notfind any user reviews within a time,so it gets poor accuracy and doesn’tfilter the irre-levant comments efficiently.The Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s problem.So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately.Thefirst step is to collect the data based on the transactional reviews of social media.The second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the dataset.Extract the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the features.Finally,In the classification method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the system.The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.展开更多
Attackers inject the designed adversarial sample into the target recommendation system to achieve illegal goals,seriously affecting the security and reliability of the recommendation system.It is difficult for attacke...Attackers inject the designed adversarial sample into the target recommendation system to achieve illegal goals,seriously affecting the security and reliability of the recommendation system.It is difficult for attackers to obtain detailed knowledge of the target model in actual scenarios,so using gradient optimization to generate adversarial samples in the local surrogate model has become an effective black‐box attack strategy.However,these methods suffer from gradients falling into local minima,limiting the transferability of the adversarial samples.This reduces the attack's effectiveness and often ignores the imperceptibility of the generated adversarial samples.To address these challenges,we propose a novel attack algorithm called PGMRS‐KL that combines pre‐gradient‐guided momentum gradient optimization strategy and fake user generation constrained by Kullback‐Leibler divergence.Specifically,the algorithm combines the accumulated gradient direction with the previous step's gradient direction to iteratively update the adversarial samples.It uses KL loss to minimize the distribution distance between fake and real user data,achieving high transferability and imperceptibility of the adversarial samples.Experimental results demonstrate the superiority of our approach over state‐of‐the‐art gradient‐based attack algorithms in terms of attack transferability and the generation of imperceptible fake user data.展开更多
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of consider...In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.展开更多
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-02152).
文摘The Internet of Things(IoT)and cloud computing have significantly contributed to the development of smart cities,enabling real-time monitoring,intelligent decision-making,and efficient resource management.These systems,particularly in IoT networks,rely on numerous interconnected devices that handle time-sensitive data for critical applications.In related approaches,trusted communication and reliable device interaction have been overlooked,thereby lowering security when sharing sensitive IoT data.Moreover,it incurs additional energy consumption and overhead while addressing potential threats in the dynamic environment.In this research,an Artificial Intelligence(AI)recommended fault-tolerant framework is proposed that leverages blockchain technology,aiming to enhance device trustworthiness and ensure data privacy.In addition,the intelligence of the proposed framework enables more authentic and authorized device involvement in data routing,thereby enabling seamless transmission in smart cities integrated with lightweight computing.To evaluate dynamic network conditions,the proposed framework offers a timely decision-making system to ensure robust delivery of IoT-assisted services.Using simulations,the efficacy of the proposed framework is validated by comparing it with existing approaches across various network metrics,demonstrating remarkable performance while achieving energy efficiency and optimizing network resources.
基金funded by Soonchunhyang University,Grant Numbers 20241422BK21 FOUR(Fostering Outstanding Universities for Research,Grant Number 5199990914048).
文摘Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or items.Collaborative filtering(CF)is a widely used personalization technique that leverages user-item interactions to generate recommendations.However,it struggles with challenges like the cold-start problem,scalability issues,and data sparsity.To address these limitations,we develop a Graph Convolutional Networks(GCNs)model that captures the complex network of interactions between users and items,identifying subtle patterns that traditional methods may overlook.We integrate this GCNs model into a federated learning(FL)framework,enabling themodel to learn fromdecentralized datasets.This not only significantly enhances user privacy—a significant improvement over conventionalmodels but also reassures users about the safety of their data.Additionally,by securely incorporating demographic information,our approach further personalizes recommendations and mitigates the coldstart issue without compromising user data.We validate our RSs model using the openMovieLens dataset and evaluate its performance across six key metrics:Precision,Recall,Area Under the Receiver Operating Characteristic Curve(ROC-AUC),F1 Score,Normalized Discounted Cumulative Gain(NDCG),and Mean Reciprocal Rank(MRR).The experimental results demonstrate significant enhancements in recommendation quality,underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems.
文摘The exponential growth of over-the-top(OTT)entertainment has fueled a surge in content consumption across diverse formats,especially in regional Indian languages.With the Indian film industry producing over 1500 films annually in more than 20 languages,personalized recommendations are essential to highlight relevant content.To overcome the limitations of traditional recommender systems-such as static latent vectors,poor handling of cold-start scenarios,and the absence of uncertainty modeling-we propose a deep Collaborative Neural Generative Embedding(C-NGE)model.C-NGE dynamically learns user and item representations by integrating rating information and metadata features in a unified neural framework.It uses metadata as sampled noise and applies the reparameterization trick to capture latent patterns better and support predictions for new users or items without retraining.We evaluate CNGE on the Indian Regional Movies(IRM)dataset,along with MovieLens 100 K and 1 M.Results show that our model consistently outperforms several existing methods,and its extensibility allows for incorporating additional signals like user reviews and multimodal data to enhance recommendation quality.
文摘A blockchain is a digitized, decentralized, public ledger of all cryptocurrency transactions. The blockchain is transforming industries by enabling innovative business practices. Its revolutionary power has permeated areas such as bank-ing, financing, trading, manufacturing, supply chain management, healthcare, and government. Blockchain and the Internet of Things (BIOT) apply the us-age of blockchain in the inter-IOT communication system, therefore, security and privacy factors are achievable. The integration of blockchain technology and IoT creates modern decentralized systems. The BIOT models can be ap-plied by various industries including e-commerce to promote decentralization, scalability, and security. This research calls for innovative and advanced re-search on Blockchain and recommendation systems. We aim at building a se-cure and trust-based system using the advantages of blockchain-supported secure multiparty computation by adding smart contracts with the main blockchain protocol. Combining the recommendation systems and blockchain technology allows online activities to be more secure and private. A system is constructed for enterprises to collaboratively create a secure database and host a steadily updated model using smart contract systems. Learning case studies include a model to recommend movies to users. The accuracy of models is evaluated by an incentive mechanism that offers a fully trust-based recom-mendation system with acceptable performance.
文摘Gamification in education enables for the holistic optimization of the learning process,empowering learners to ameliorate their digital,cognitive,emotional and social skills,via their active experimentation with game design elements,accompanying pertinent pedagogical objectives of interest.This paper focuses on a cross-platform,innovative,gamified,educational learning system product,funded by the Hellenic Republic Ministry of Development and Investments:howlearn.By applying gamification techniques,in 3D virtual environments,within which,learners fulfil STEAM(Science,Technology,Engineering,Arts and Mathematics)-related Experiments(Simulations,Virtual Labs,Interactive Storytelling Scenarios,Decision Making Case Studies),howlearn covers learners’subject material,while,simultaneously,functioning,as an Authoring Gamification Tool and as a Game Metrics Repository;users’metrics are being,dynamically,analyzed,through Machine Learning Algorithms.Consequently,the System learns from the data and learners receive Personalized Feedback Report Dashboards of their overall performance,weaknesses,interests and general class competency.A Custom Recommendation System(Collaborative Filtering,Content-Based Filtering)then supplies suggestions,representing the best matches between Experiments and learners,while also focusing on the reinforcement of the learning weaknesses of the latter.Ultimately,by optimizing the Accuracy,Performance and Predictive capability of the Personalized Feedback Report,we provide learners with scientifically valid performance assessments and educational recommendations,thence intensifying sustainable,learner-centered education.
文摘Modern marketing strategies have transformed through the combined power of Artificial Intelligence(AI)and Business Intelligence(BI)which improve customer segmentation and personalize marketing activities.This research examines how AI recommendation systems alongside BI tools influence marketing performance through customer interaction and conversion metrics.The research shows how AI and BI technologies produce effective marketing initiatives by analyzing consumer behavior data from transaction histories,browsing patterns,and social media activities.The study shows major enhancements in essential performance metrics including click-through rates and conversion rates with increased customer satisfaction when businesses implement AI-based systems over traditional marketing techniques.The research indicates that businesses using BI tools to implement AI-based customer segmentation achieve better conversion rates across different consumer demographics.Organizations that utilize both AI and BI systems can develop market advantages by improving customer targeting methods and enhancing their advertising approaches.The study offers important information that helps businesses boost their marketing performance while keeping pace with changing consumer behaviors in a competitive environment.
文摘Increasing amounts of financial data demand sophisticated analytics to develop sound recommendation models.This article discusses combining time series analysis and association rule mining for big data in Hadoop and Spark to enrich financial product recommendation engines.The paper is an integrated analysis of two types of prediction algorithms:AutoRegressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)networks to forecast user behavior and demand for financial services in the future from transactional history.The ARIMA model is used as the default while the LSTM model is used to represent non-linear dependencies and give a more dynamic forecast.association rule mining–in particular the Apriori algorithm–is used to find latent patterns and relationships between user transactions and financial products.This article illustrates how time series forecasting and association rule mining can be merged to bring a more useful financial recommendation.The hybrid approach,which combines both approaches,proves to increase user interaction and recommendation accuracy by 20%compared to the previous systems,according to experiments.The paper emphasises the possibilities of using big data in the construction of scalable,individualized financial recommendation systems.
文摘Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.
基金supported by the Program of National Natural Science Foundation of China(Nos.62472151,U24A20255,62422205,62272149,and U23A20317)the Program of Natural Science Foundation of Hunan(No.2023JJ30102)+4 种基金the Science and Technology Innovation Program of Hunan Province(No.2022RC1038)the Natural Science Foundation of Hunan Province(No.2023JJ40162)the Shenzhen Science and Technology Program(No.JCYJ20210324141208020)the Natural Science Foundation of Hunan Province(Nos.2023JJ10016 and 2023JJ30083)the Yuelu Mountain Industrial Innovation Center Project(No.2023YCII0118).
文摘Cyber Physical Social Intelligence(CPSI)emphasizes the integration of social information and artificial system information from virtual spaces,enabling Social Intelligence Recommendation Systems(SIRS)to make intelligent decisions and optimizations based on more comprehensive data,thereby enhancing the accuracy of recommendations and user experience.However,as the combined application of CPSI and SIRS becomes increasingly widespread,they also face the risk of shilling attacks.Traditional shilling attacks are limited in terms of low stealthiness,specificity to certain systems,and generation of unrealistic fake profiles.In this paper,we propose a black-box attack method,Real User Preference Generative Adversarial Networks(RUP-GAN),based on adversarial learning.RUP-GAN optimizes the authenticity of user profiles and enhances the hit rate of target items within users’top-k recommendation lists.Through experiments conducted on real-world datasets,it has been proved that RUP-GAN surpasses baseline shilling attack methods in attack effectiveness,transferability,and invisibility.Our proposed model can effectively mitigate the risks posed by shilling attacks,and provide valuable insights for the defense research of CPSI and SIRS.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
文摘Using knowledge graphs to assist deep learning models in making recommendation decisions has recently been proven to effectively improve the model′s interpretability and accuracy.This paper introduces an end-to-end deep learning model,named representation-enhanced knowledge graph convolutional networks(RKGCN),which dynamically analyses each user′s preferences and makes a recommendation of suitable items.It combines knowledge graphs on both the item side and user side to enrich their representations to maximize the utilization of the abundant information in knowledge graphs.RKGCN is able to offer more personalized and relevant recommendations in three different scenarios.The experimental results show the superior effectiveness of our model over 5 baseline models on three real-world datasets including movies,books,and music.
文摘This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems,addressing the lack of depth and precision in existing literature.It proposes a two-pronged approach:a thorough analysis of current algorithms and a novel,hierarchical taxonomy for precise categorization.The taxonomy is based on a tri-level hierarchy,starting with the methodology category and narrowing down to specific techniques.Such a framework allows for a structured and comprehensive classification of algorithms,assisting researchers in understanding the interrelationships among diverse algorithms and techniques.Covering a wide range of algorithms,this taxonomy first categorizes algorithms into four main analysis types:user and item similarity based methods,hybrid and combined approaches,deep learning and algorithmic methods,and mathematical modeling methods,with further subdivisions into sub-categories and techniques.The paper incorporates both empirical and experimental evaluations to differentiate between the techniques.The empirical evaluation ranks the techniques based on four criteria.The experimental assessments rank the algorithms that belong to the same category,sub-category,technique,and sub-technique.Also,the paper illuminates the future prospects of big data techniques in recommendation systems,underscoring potential advancements and opportunities for further research in this fields.
基金This work was supported by the Kyonggi University Research Grant 2022.
文摘Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.
基金funded by the Directorate General of Research and Development,Ministry of Higher Education,Science and Technology of the Republic of Indonesia,with grant number 2.6.63/UN32.14.1/LT/2025.
文摘Recommendation systems are an integral and indispensable part of every digital platform,as they can suggest content or items to users based on their respective needs.Collaborative filtering is a technique often used in various studies,which produces recommendations by analyzing similarities between users and items based on their behavior.Although often used,traditional collaborative filtering techniques still face the main challenge of sparsity.Sparsity problems occur when the data in the system is sparse,meaning that only a portion of users provide feedback on some items,resulting in inaccurate recommendations generated by the system.To overcome this problem,we developed aHybrid Collaborative Filtering model based onMatrix Factorization andGradient Boosting(HCF-MFGB),a new hybrid approach.Our proposed model integrates SVD++,the XGBoost ensemble learning algorithm,and utilizes user demographic data and meta items.We utilize information,both explicitly and implicitly,to learn user preference patterns using SVD++.The XGBoost algorithm is used to create hundreds of decision trees incrementally,thereby improving model accuracy.Meanwhile,user demographic and meta-item data are clustered using the K-Means Clustering algorithm to capture similarities in user and item characteristics.This combination is designed to improve rating prediction accuracy by reducing reliance on minimal explicit rating data,while addressing sparsity issues in movie recommendation systems.The results of experiments on the MovieLens 100K,MovieLens 1M,and CiaoDVD datasets show significant improvements,outperforming various other baselinemodels in terms of RMSE and MAE.On theMovieLens 100K dataset,the HCF-MFGB model obtained an RMSE value of 0.853 and an MAE value of 0.674.On theMovieLens 1M dataset,the HCF-MFGB model obtained an RMSE value of 0.763 and an MAE value of 0.61.On the CiaoDCD dataset,the HCF-MFGB model achieved an RMSE value of 0.718 and an MAE value of 0.495.These results confirm a significant improvement in movie recommendation accuracy with the proposed approach.
基金supported in part by the grant from the Research Grants Council of the Hong Kong Special Administrative Region,China(Project Reference No.UGC/FDS16/E09/21)in part by the Hong Kong President’s Advisory Committee on Research and Development(PACRD)under Project No.2020/1.6,in part by the National Natural Science Foundation of China(NSFC)under Grants No.61971239 and No.92067201+1 种基金in part by Jiangsu Provincial Key Research and Development Program under grant No.BE2020084-4in part by Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant KYCX200714.
文摘To accommodate the tremendous increase of mobile data traffic,cache-enabled device-to-device(D2D)communication has been taken as a promising technique to release the heavy burden of cellular networks since popular contents can be pre-fetched at user devices and shared among subscribers.As a result,cellular traffic can be offloaded and an enhanced system performance can be attainable.However,due to the limited cache capacity of mobile devices and the heterogeneous preferences among different users,the requested contents are most likely not be proactively cached,inducing lower cache hit ratio.Recommendation system,on the other hand,is able to reshape users’request schema,mitigating the heterogeneity to some extent,and hence it can boost the gain of edge caching.In this paper,the cost minimization problem for the social-aware cache-enabled D2D networks with recommendation consideration is investigated,taking into account the constraints on the cache capacity budget and the total number of recommended files per user,in which the contents are sharing between the users that trust each other.The minimization problem is an integer non-convex and non-linear programming,which is in general NP-hard.Therewith,we propose a timeefficient joint recommendation and caching decision scheme.Extensive simulation results show that the proposed scheme converges quickly and significantly reduces the average cost when compared with various benchmark strategies.
文摘Traditional e-commerce recommendation systems often struggle with dynamic user preferences and a vast array of products,leading to suboptimal user experiences.To address this,our study presents a Personalized Adaptive Multi-Product Recommendation System(PAMR)leveraging transfer learning and Bi-GRU(Bidirectional Gated Recurrent Units).Using a large dataset of user reviews from Amazon and Flipkart,we employ transfer learning with pre-trained models(AlexNet,GoogleNet,ResNet-50)to extract high-level attributes from product data,ensuring effective feature representation even with limited data.Bi-GRU captures both spatial and sequential dependencies in user-item interactions.The innovation of this study lies in the innovative feature fusion technique that combines the strengths of multiple transfer learning models,and the integration of an attention mechanism within the Bi-GRU framework to prioritize relevant features.Our approach addresses the classic recommendation systems that often face challenges such as cold start along with data sparsity difficulties,by utilizing robust user and item representations.The model demonstrated an accuracy of up to 96.9%,with precision and an F1-score of 96.2%and 96.97%,respectively,on the Amazon dataset,significantly outperforming the baselines and marking a considerable advancement over traditional configurations.This study highlights the effectiveness of combining transfer learning with Bi-GRU for scalable and adaptive recommendation systems,providing a versatile solution for real-world applications.
基金supported by the National Natural Science Foundation of China under Grant 61762031the Science and Technology Major Project of Guangxi Province under Grant AA19046004+2 种基金the Natural Science Foundation of Guangxi under Grant 2021JJA170130the Innovation Project of Guangxi Graduate Education under Grant YCSW2022326the Research Project of Guangxi Philosophy and Social Science Planning under Grant 21FGL040。
文摘The recommendation system(RS)on the strength of Graph Neural Networks(GNN)perceives a user-item interaction graph after collecting all items the user has interacted with.Afterward the RS performs neighborhood aggregation on the graph to generate long-term preference representations for the user in quick succession.However,user preferences are dynamic.With the passage of time and some trend guidance,users may generate some short-term preferences,which are more likely to lead to user-item interactions.A GNN recommendation based on long-and short-term preference(LSGNN)is proposed to address the above problems.LSGNN consists of four modules,using a GNN combined with the attention mechanism to extract long-term preference features,using Bidirectional Encoder Representation from Transformers(BERT)and the attention mechanism combined with Bi-Directional Gated Recurrent Unit(Bi-GRU)to extract short-term preference features,using Convolutional Neural Network(CNN)combined with the attention mechanism to add title and description representations of items,finally inner-producing long-term and short-term preference features as well as features of items to achieve recommendations.In experiments conducted on five publicly available datasets from Amazon,LSGNN is superior to state-of-the-art personalized recommendation techniques.
文摘Nowadays,review systems have been developed with social media Recommendation systems(RS).Although research on RS social media is increas-ing year by year,the comprehensive literature review and classification of this RS research is limited and needs to be improved.The previous method did notfind any user reviews within a time,so it gets poor accuracy and doesn’tfilter the irre-levant comments efficiently.The Recursive Neural Network-based Trust Recom-mender System(RNN-TRS)is proposed to overcome this method’s problem.So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately.Thefirst step is to collect the data based on the transactional reviews of social media.The second step is pre-processing using Imbalanced Col-laborative Filtering(ICF)to remove the null values from the dataset.Extract the features from the pre-processing step using the Maximum Support Grade Scale(MSGS)to extract the maximum number of scaling features in the dataset and grade the weights(length,count,etc.).In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax acti-vation function for calculating the average weights of the features.Finally,In the classification method,the Recursive Neural Network-based Trust Recommender System(RNN-TRS)for User reviews based on the Positive and negative scores is analysed by the system.The simulation results improve the predicting accuracy and reduce time complexity better than previous methods.
基金The National Natural Science Foundation of China (61876001)Opening Foundation of State Key Laboratory of Cognitive Intelligence,Opening Foundation of State Key Laboratory of Cognitive Intelligence(iED2022-006)Scientific Research Planning Project of Anhui Province (2022AH050072)
文摘Attackers inject the designed adversarial sample into the target recommendation system to achieve illegal goals,seriously affecting the security and reliability of the recommendation system.It is difficult for attackers to obtain detailed knowledge of the target model in actual scenarios,so using gradient optimization to generate adversarial samples in the local surrogate model has become an effective black‐box attack strategy.However,these methods suffer from gradients falling into local minima,limiting the transferability of the adversarial samples.This reduces the attack's effectiveness and often ignores the imperceptibility of the generated adversarial samples.To address these challenges,we propose a novel attack algorithm called PGMRS‐KL that combines pre‐gradient‐guided momentum gradient optimization strategy and fake user generation constrained by Kullback‐Leibler divergence.Specifically,the algorithm combines the accumulated gradient direction with the previous step's gradient direction to iteratively update the adversarial samples.It uses KL loss to minimize the distribution distance between fake and real user data,achieving high transferability and imperceptibility of the adversarial samples.Experimental results demonstrate the superiority of our approach over state‐of‐the‐art gradient‐based attack algorithms in terms of attack transferability and the generation of imperceptible fake user data.
基金Supported by the National Natural Science Foun-dation of China (60573095)
文摘In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users＇ ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.
