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
This study introduces a fund recommendation system based on the ε-greedy algorithm and an incremental learning framework.This model simulates the interaction process when customers browse the web-pages of fund produc...This study introduces a fund recommendation system based on the ε-greedy algorithm and an incremental learning framework.This model simulates the interaction process when customers browse the web-pages of fund products.Customers click on their preferred fund products when visiting a fund recommendation web-page.The system collects customer click sequences to continually estimate and update their utility function.The system generates product lists using the ε-greedy algorithm,where each product on the list has the probability of 1-ε of being selected as an exploitation strategy,and the probability of ε is chosen as the exploration strategy.We perform a series of numerical tests to evaluate the estimation performance with different values of ε.展开更多
According to demand and function of the e-commerce recommendation system demand, this paper analyze and design e-commerce and personalized recommendation, design and complete different system functions in different sy...According to demand and function of the e-commerce recommendation system demand, this paper analyze and design e-commerce and personalized recommendation, design and complete different system functions in different system level; then design in detail system process from the front and back office systems, and in detail descript the key data in the database and several tables. Finally, the paper respectively tests several main modules of onstage system and the backstage system. The paper designed electronic commerce recommendation based on personalized recommendation system, it can complete the basic function of the electronic commerce system, also can be personalized commodity recommendation for different users, the user data information and the user＇ s shopping records.展开更多
To promote information service ability of digital libraries, a browsing and searching personalized recommendation framework based on the use of ontology is described, where the advantages of ontology are exploited in ...To promote information service ability of digital libraries, a browsing and searching personalized recommendation framework based on the use of ontology is described, where the advantages of ontology are exploited in different parts of the retrieval cycle including query-based relevance measures, semantic user preference representation and automatic update, and personalized result ranking. Both the usage and information resources can be exploited to extract useful knowledge from the way users interact with a digital library. Through combination and mapping between the extracted knowledge and domain ontology, semantic content retrieval between queries and documents can be utilized. Furthermore, ontology-based conceptual vector of user preference can be applied in personalized recommendation feedback.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
With time going on, the fact that pace of life becomes faster make more and more customers pay more attention to of clothing. In order to survive and develop better and to attract more customers, enterprisesmust have ...With time going on, the fact that pace of life becomes faster make more and more customers pay more attention to of clothing. In order to survive and develop better and to attract more customers, enterprisesmust have the ability to provide the personalized recommendations and the implementation of differentiated business strategy. This text aims to make enterprises understand the customers＇ personalized requirement by using the data processed though questionnaire and rough set theory. And enterprises can provide production and marketing auxiliary decision-making effectively. The feasibility and practicality of rough set theory is verified through the personalized recommendationseases.展开更多
Abstract： Taking the basic data and the log data of the various businesses of the automation integrated management system of the library in Jinan University as the research object this paper analyzes the internal rel...Abstract： Taking the basic data and the log data of the various businesses of the automation integrated management system of the library in Jinan University as the research object this paper analyzes the internal relationship between books and between the books and the readers, and designs a personalized book recommendation algorithm, the BookSimValue, on the basis of the user collaborative filteringtechnology. The experimental results show that the recommended book information produced by this algorithm can effectively help the readers to solve the problem of the book information overload, which can bring great convenience to the readers and effectively save the time of the readers＇ selection of the books, thus effectively improving the utilization of the library resources and the service levels.展开更多
A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such...A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such as casual and athletic styles,and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences,this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling,termed as PSGNet.Firstly,a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly,a personal style prediction module extracts user style preferences by analyzing historical data.Then,to address the limitations of single-modal representations and enhance fashion compatibility,both fashion images and text data are leveraged to extract multi-modal features.Finally,PSGNet integrates these components through Bayesian personalized ranking(BPR)to unify the personal style and fashion compatibility,where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.展开更多
This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data a...This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.展开更多
Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products ...Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.展开更多
The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively...The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these aspects.Although the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and security.We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data.To avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation.Compared to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative characteristics.Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy.展开更多
With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personaliz...With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.展开更多
Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current research.In this paper,a recommender system combining popularity and novelty(RSCPN)based on ...Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current research.In this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is proposed.The edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users.RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender system.We verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.展开更多
Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and c...Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.展开更多
This study innovatively built an intelligent analysis platform for learning behavior,which deeply integrated the cutting-edge technology of big data and Artificial Intelligence(AI),\mined and analyzed students’learni...This study innovatively built an intelligent analysis platform for learning behavior,which deeply integrated the cutting-edge technology of big data and Artificial Intelligence(AI),\mined and analyzed students’learning data,and realized the personalized customization of learning resources and the accurate matching of intelligent learning partners.With the help of advanced algorithms and multi-dimensional data fusion strategies,the platform not only promotes positive interaction and collaboration in the learning environment but also provides teachers with comprehensive and in-depth students’learning portraits,which provides solid support for the implementation of precision education and the personalized adjustment of teaching strategies.In this study,a recommender system based on user similarity evaluation and a collaborative filtering mechanism is carefully designed,and its technical architecture and implementation process are described in detail.展开更多
Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing le...Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing learners’potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners.Combining similar learners’learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations.This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners’cognitive states.Design/methodology/approach-Firstly,this approach constructs e-learning environments’knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors,and the knowledge graph attention network(KGAT)is used to train the graph embedding model of the knowledge graph.Secondly,a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners’cognitive states.Then,the learners’actual cognitive states,obtained by the cognitive diagnosis model(CDM),are innovatively incorporated into graph matching for acquiring similar subgraphs.Finally,the personalized recommendation results are ranked according to learners’interaction probability on similar exercises.Findings-First,the proposed method has superior exercise recommendation performance.Experiments demonstrate that,compared to the existing approach,the proposed approach has an increase rate of 3.21%,3.32%,3.27%and 0.38%in precision,recall,F1 score and HR@10,respectively,in the large-scale graph data scenario.Second,aggregating high-level semantic information from the knowledge network helps explore learners’potential preferences.Finally,the fine-grained scoring mechanism based on learners’exercise completion quality can better reflect the actual mastery levels of learners,which improves the accuracy of modeling their cognitive states.Originality/value-First,an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion.The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data.Second,this approach provides a flexible and adaptable framework.In it,the CDM can be replaced to explore for better accuracy of cognitive evaluation.Third,KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph.Finally,a score reevaluation method is designed to analyze learners’learning behavior for accurately modeling their cognitive states.展开更多
Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business intelligence.Recently,deep neural network-based sequential recommendatio...Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business intelligence.Recently,deep neural network-based sequential recommendation models gained considerable attention.However,existing approaches pay litle attention to users'dynamically evolving interests,which are influenced by product attributes,especially product category.To overcome these challenges,we propose a dynamic personalized recommendation model:DynaPR.Specifically,we first embed product information and attribute information into a unified data space.Then,we exploit long short-term memory(LsTM)networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM networks.Finally,similarity values between users are measured through pairwise interest features,and personalized recommendation lists are generated.A series of experiments reveal the superiority of the proposed method compared withotheradvanced methods.展开更多
Travelling is a critical component of daily life.With new technology,personalized travel route recommendations are possible and have become a new research area.A personalized travel route recommendation refers to plan...Travelling is a critical component of daily life.With new technology,personalized travel route recommendations are possible and have become a new research area.A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations,based on the road networks and users’travel preferences.In this paper,we define users’travel behaviours from their historical Global Positioning System(GPS)trajectories and propose two personalized travel route recommendation methods–collaborative travel route recommendation(CTRR)and an extended version of CTRR(CTRR+).Both methods consider users’personal travel preferences based on their historical GPS trajectories.In this paper,we first estimate users’travel behaviour frequencies by using collaborative filtering technique.A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model.The CTRR+method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability.This paper also conducts some case studies based on a real GPS trajectory data set from Beijing,China.The experimental results show that the proposed CTRR and CTRR+methods achieve better results for travel route recommendations compared with the shortest distance path method.展开更多
Recent years have witnessed a growing trend of Web services on the Interact. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web serv...Recent years have witnessed a growing trend of Web services on the Interact. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services （e.g., func- tional and non-functional properties） but largely ignore users＇ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation （TSR） to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.展开更多
Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information explosion.Especially,personalized recommendation models based on g...Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information explosion.Especially,personalized recommendation models based on graph structure have advanced greatly in predicting user preferences.However,geographical region entities that reflect the geographical context of the items is not being utilized in previous works,leaving room for the improvement of personalized recommendation.This study proposes a region-aware neural graph collaborative filtering(RA-NGCF)model,which introduces the geographical regions for improving the prediction of user preference.The approach first characterizes the relationships between items and users with a user-item-region graph.And,a neural network model for the region-aware graph is derived to capture the higher-order interaction among users,items,and regions.Finally,the model fuses region and item vectors to infer user preferences.Experiments on real-world dataset results show that introducing region entities improves the accuracy of personalized recommendations.This study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.展开更多
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This research was supported by National Key R&D Program of China under No.2022YFA1004000National Natural Science Foundation of China under No.11991023 and 12371324.
文摘This study introduces a fund recommendation system based on the ε-greedy algorithm and an incremental learning framework.This model simulates the interaction process when customers browse the web-pages of fund products.Customers click on their preferred fund products when visiting a fund recommendation web-page.The system collects customer click sequences to continually estimate and update their utility function.The system generates product lists using the ε-greedy algorithm,where each product on the list has the probability of 1-ε of being selected as an exploitation strategy,and the probability of ε is chosen as the exploration strategy.We perform a series of numerical tests to evaluate the estimation performance with different values of ε.
文摘According to demand and function of the e-commerce recommendation system demand, this paper analyze and design e-commerce and personalized recommendation, design and complete different system functions in different system level; then design in detail system process from the front and back office systems, and in detail descript the key data in the database and several tables. Finally, the paper respectively tests several main modules of onstage system and the backstage system. The paper designed electronic commerce recommendation based on personalized recommendation system, it can complete the basic function of the electronic commerce system, also can be personalized commodity recommendation for different users, the user data information and the user＇ s shopping records.
基金The Young Teachers Scientific Research Foundation(YTSRF) of Nanjing University of Science and Technology in the Year of2005-2006.
文摘To promote information service ability of digital libraries, a browsing and searching personalized recommendation framework based on the use of ontology is described, where the advantages of ontology are exploited in different parts of the retrieval cycle including query-based relevance measures, semantic user preference representation and automatic update, and personalized result ranking. Both the usage and information resources can be exploited to extract useful knowledge from the way users interact with a digital library. Through combination and mapping between the extracted knowledge and domain ontology, semantic content retrieval between queries and documents can be utilized. Furthermore, ontology-based conceptual vector of user preference can be applied in personalized recommendation feedback.
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.
基金This work is supported by the National Natural Science Foundation of China （No. 71301100）, Innovation Program of Shanghai Municipal Education Commission（No. 14YZ140 and No. ZZGJD12036）, Innovation Program of ShanghaiUniversity of Engineering Science （NO. E1-0903-15-01143, Title： 15KY0354Research on personalizedrecommendafionof clothing based on Data Mining） and Doctorate Foundation of Shanghai（No. 11692191400）.
文摘With time going on, the fact that pace of life becomes faster make more and more customers pay more attention to of clothing. In order to survive and develop better and to attract more customers, enterprisesmust have the ability to provide the personalized recommendations and the implementation of differentiated business strategy. This text aims to make enterprises understand the customers＇ personalized requirement by using the data processed though questionnaire and rough set theory. And enterprises can provide production and marketing auxiliary decision-making effectively. The feasibility and practicality of rough set theory is verified through the personalized recommendationseases.
文摘Abstract： Taking the basic data and the log data of the various businesses of the automation integrated management system of the library in Jinan University as the research object this paper analyzes the internal relationship between books and between the books and the readers, and designs a personalized book recommendation algorithm, the BookSimValue, on the basis of the user collaborative filteringtechnology. The experimental results show that the recommended book information produced by this algorithm can effectively help the readers to solve the problem of the book information overload, which can bring great convenience to the readers and effectively save the time of the readers＇ selection of the books, thus effectively improving the utilization of the library resources and the service levels.
基金Shanghai Frontier Science Research Center for Modern Textiles,Donghua University,ChinaOpen Project of Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment,Zhengzhou University of Light Industry,China(No.IM202303)National Key Research and Development Program of China(No.2019YFB1706300)。
文摘A personalized outfit recommendation has emerged as a hot research topic in the fashion domain.However,existing recommendations do not fully exploit user style preferences.Typically,users prefer particular styles such as casual and athletic styles,and consider attributes like color and texture when selecting outfits.To achieve personalized outfit recommendations in line with user style preferences,this paper proposes a personal style guided outfit recommendation with multi-modal fashion compatibility modeling,termed as PSGNet.Firstly,a style classifier is designed to categorize fashion images of various clothing types and attributes into distinct style categories.Secondly,a personal style prediction module extracts user style preferences by analyzing historical data.Then,to address the limitations of single-modal representations and enhance fashion compatibility,both fashion images and text data are leveraged to extract multi-modal features.Finally,PSGNet integrates these components through Bayesian personalized ranking(BPR)to unify the personal style and fashion compatibility,where the former is used as personal style features and guides the output of the personalized outfit recommendation tailored to the target user.Extensive experiments on large-scale datasets demonstrate that the proposed model is efficient on the personalized outfit recommendation.
基金This work is supported by the Ministry of Education of Humanities and Social Science projects in China(No.20YJCZH124)Guangdong Province Education and Teaching Reform Project No.640:Research on the Teaching Practice and Application of Online Peer Assessment Methods in the Context of Artificial Intelligence.
文摘This study proposes a learner profile framework based on multi-feature fusion,aiming to enhance the precision of personalized learning recommendations by integrating learners’static attributes(e.g.,demographic data and historical academic performance)with dynamic behavioral patterns(e.g.,real-time interactions and evolving interests over time).The research employs Term Frequency-Inverse Document Frequency(TF-IDF)for semantic feature extraction,integrates the Analytic Hierarchy Process(AHP)for feature weighting,and introduces a time decay function inspired by Newton’s law of cooling to dynamically model changes in learners’interests.Empirical results demonstrate that this framework effectively captures the dynamic evolution of learners’behaviors and provides context-aware learning resource recommendations.The study introduces a novel paradigm for learner modeling in educational technology,combining methodological innovation with a scalable technical architecture,thereby laying a foundation for the development of adaptive learning systems.
基金Supported bythe Hunan Teaching Reformand Re-search Project of Colleges and Universities (2003-B72) the HunanBoard of Review on Philosophic and Social Scientific Pay-off Project(0406035) the Hunan Soft Science Research Project(04ZH6005)
文摘Content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.
基金the National Natural Science Foundation of Chinaunder Grant No.61772280by the China Special Fund for Meteorological Research in the Public Interestunder Grant GYHY201306070by the Jiangsu Province Innovation and Entrepreneurship TrainingProgram for College Students under Grant No.201910300122Y.
文摘The application field of the Internet of Things(IoT)involves all aspects,and its application in the fields of industry,agriculture,environment,transportation,logistics,security and other infrastructure has effectively promoted the intelligent development of these aspects.Although the IoT has gradually grown in recent years,there are still many problems that need to be overcome in terms of technology,management,cost,policy,and security.We need to constantly weigh the benefits of trusting IoT products and the risk of leaking private data.To avoid the leakage and loss of various user data,this paper developed a hybrid algorithm of kernel function and random perturbation method based on the algorithm of non-negative matrix factorization,which realizes personalized recommendation and solves the problem of user privacy data protection in the process of personalized recommendation.Compared to non-negative matrix factorization privacy-preserving algorithm,the new algorithm does not need to know the detailed information of the data,only need to know the connection between each data;and the new algorithm can process the data points with negative characteristics.Experiments show that the new algorithm can produce recommendation results with certain accuracy under the premise of preserving users’personal privacy.
基金This work was supported by the College of Computer and Information Sciences,Prince Sultan University,Saudi Arabia.
文摘With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.
基金Project funded by the National Science Foundation of China under Grant(Nos.61462091,61672020,U1803263,61866039,61662085)by the Data Driven Software Engineering innovation team of Yunnan province(No.2017HC012)+2 种基金by Scientific Research Foundation Project of Yunnan Education Department(No.2019J0008,2019J0010)by China Postdoctoral Science Foundation(Nos.2013M542560,2015T81129)A Project of Shandong Province Higher Educational Science and Technology Program(No.J16LN61).
文摘Personalized recommendation algorithms,which are effective means to solve information overload,are popular topics in current research.In this paper,a recommender system combining popularity and novelty(RSCPN)based on one-mode projection of weighted bipartite network is proposed.The edge between a user and item is weighted with the item’s rating,and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users.RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender system.We verify and compare the accuracy,diversity and novelty of the proposed model with those of other models,and results show that RSCPN is feasible.
文摘Location based social networks（ LBSNs） provide location specific data generated from smart phone into online social networks thus people can share their points of interest（ POIs）. POI collections are complex and can be influenced by various factors,such as user preferences,social relationships and geographical influence. Therefore,recommending new locations in LBSNs requires to take all these factors into consideration. However,one problem is how to determine optimal weights of influencing factors in an algorithm in which these factors are combined. The user similarity can be obtained from the user check-in data,or from the user friend information,or based on the different geographical influences on each user＇s check-in activities. In this paper,we propose an algorithm that calculates the user similarity based on check-in records and social relationships,using a proposed weighting function to adjust the weights of these two kinds of similarities based on the geographical distance between users. In addition,a non-parametric density estimation method is applied to predict the unique geographical influence on each user by getting the density probability plot of the distance between every pair of user＇s check-in locations. Experimental results,using foursquare datasets,have shown that comparisons between the proposed algorithm and the other five baseline recommendation algorithms in LBSNs demonstrate that our proposed algorithm is superior in accuracy and recall,furthermore solving the sparsity problem.
文摘This study innovatively built an intelligent analysis platform for learning behavior,which deeply integrated the cutting-edge technology of big data and Artificial Intelligence(AI),\mined and analyzed students’learning data,and realized the personalized customization of learning resources and the accurate matching of intelligent learning partners.With the help of advanced algorithms and multi-dimensional data fusion strategies,the platform not only promotes positive interaction and collaboration in the learning environment but also provides teachers with comprehensive and in-depth students’learning portraits,which provides solid support for the implementation of precision education and the personalized adjustment of teaching strategies.In this study,a recommender system based on user similarity evaluation and a collaborative filtering mechanism is carefully designed,and its technical architecture and implementation process are described in detail.
文摘Purpose-Recently,the number of online learners and learning resources has increased dramatically,and the knowledge network generated in the e-learning platform is getting vaster and more complex than ever.Analyzing learners’potential preferences by aggregating high-level semantic information from this network and accurately modeling their cognitive states is crucial for identifying similar learners.Combining similar learners’learning records helps recommend suitable exercises to improve the effectiveness of exercise recommendations.This article tackles the challenging problem of how to aggregate high-level semantic information in a huge graph and accurately model learners’cognitive states.Design/methodology/approach-Firstly,this approach constructs e-learning environments’knowledge graphs by integrating the difficulty of exercises and characteristics of answering behaviors,and the knowledge graph attention network(KGAT)is used to train the graph embedding model of the knowledge graph.Secondly,a score reevaluation method is designed based on the coefficient of completion quality to help accurately model learners’cognitive states.Then,the learners’actual cognitive states,obtained by the cognitive diagnosis model(CDM),are innovatively incorporated into graph matching for acquiring similar subgraphs.Finally,the personalized recommendation results are ranked according to learners’interaction probability on similar exercises.Findings-First,the proposed method has superior exercise recommendation performance.Experiments demonstrate that,compared to the existing approach,the proposed approach has an increase rate of 3.21%,3.32%,3.27%and 0.38%in precision,recall,F1 score and HR@10,respectively,in the large-scale graph data scenario.Second,aggregating high-level semantic information from the knowledge network helps explore learners’potential preferences.Finally,the fine-grained scoring mechanism based on learners’exercise completion quality can better reflect the actual mastery levels of learners,which improves the accuracy of modeling their cognitive states.Originality/value-First,an approach to personalized exercise recommendation is proposed via knowledge enhancement and fuzzy cognitive fusion.The experiments demonstrate the effectiveness and feasibility of this approach in a scenario with large-scale graph data.Second,this approach provides a flexible and adaptable framework.In it,the CDM can be replaced to explore for better accuracy of cognitive evaluation.Third,KGAT is employed to embed the knowledge graph in e-learning environments for aggregating high-level semantic information from the graph.Finally,a score reevaluation method is designed to analyze learners’learning behavior for accurately modeling their cognitive states.
文摘Personalized recommendation plays a critical role in providing decision-making support for product and service analysis in the field of business intelligence.Recently,deep neural network-based sequential recommendation models gained considerable attention.However,existing approaches pay litle attention to users'dynamically evolving interests,which are influenced by product attributes,especially product category.To overcome these challenges,we propose a dynamic personalized recommendation model:DynaPR.Specifically,we first embed product information and attribute information into a unified data space.Then,we exploit long short-term memory(LsTM)networks to characterize sequential behavior over multiple time periods and seize evolving interests by hierarchical LSTM networks.Finally,similarity values between users are measured through pairwise interest features,and personalized recommendation lists are generated.A series of experiments reveal the superiority of the proposed method compared withotheradvanced methods.
基金the Natural Sciences and Engineering Research Council of Canada Discovery Grant to Xin Wang,and the National Natural Science Foundation of China[grant number 11271351]to Jun Luo.
文摘Travelling is a critical component of daily life.With new technology,personalized travel route recommendations are possible and have become a new research area.A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations,based on the road networks and users’travel preferences.In this paper,we define users’travel behaviours from their historical Global Positioning System(GPS)trajectories and propose two personalized travel route recommendation methods–collaborative travel route recommendation(CTRR)and an extended version of CTRR(CTRR+).Both methods consider users’personal travel preferences based on their historical GPS trajectories.In this paper,we first estimate users’travel behaviour frequencies by using collaborative filtering technique.A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model.The CTRR+method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability.This paper also conducts some case studies based on a real GPS trajectory data set from Beijing,China.The experimental results show that the proposed CTRR and CTRR+methods achieve better results for travel route recommendations compared with the shortest distance path method.
基金supported in part by the National Key Technology Research and Development Program of China under Grant No.2013BAD19B10the National Natural Science Foundation of China under Grant No.61170033
文摘Recent years have witnessed a growing trend of Web services on the Interact. There is a great need of effective service recommendation mechanisms. Existing methods mainly focus on the properties of individual Web services （e.g., func- tional and non-functional properties） but largely ignore users＇ views on services, thus failing to provide personalized service recommendations. In this paper, we study the trust relationships between users and Web services using network modeling and analysis techniques. Based on the findings and the service network model we build, we then propose a collaborative filtering algorithm called Trust-Based Service Recommendation （TSR） to provide personalized service recommendations. This systematic approach for service network modeling and analysis can also be used for other service recommendation studies.
基金supported in part by the National Natural Science Foundation of China(NSFC)[grant number 42071382,61972365].
文摘Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information explosion.Especially,personalized recommendation models based on graph structure have advanced greatly in predicting user preferences.However,geographical region entities that reflect the geographical context of the items is not being utilized in previous works,leaving room for the improvement of personalized recommendation.This study proposes a region-aware neural graph collaborative filtering(RA-NGCF)model,which introduces the geographical regions for improving the prediction of user preference.The approach first characterizes the relationships between items and users with a user-item-region graph.And,a neural network model for the region-aware graph is derived to capture the higher-order interaction among users,items,and regions.Finally,the model fuses region and item vectors to infer user preferences.Experiments on real-world dataset results show that introducing region entities improves the accuracy of personalized recommendations.This study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.
