Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequen...Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).展开更多
In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face sig...In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face significant challenges due to data sparsity,algorithm scalability,and the difficulty of adapting to dynamic user preferences.These limitations hinder the ability of systems to provide highly accurate and personalised recommendations.To address these challenges,this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm(GOA),termed LCGOA,to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment.By combining the K-means algorithm with the enhanced GOA,which incorporates a Lévy flight mechanism and multi-strategy co-evolution,our method overcomes the centroid sensitivity issue,a key limitation in traditional clustering techniques.Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy,offering more relevant content to users and driving greater customer satisfaction and business growth.展开更多
During the final proofing stage of the paper,the wrong version of Fig.2 was accidently used when replacing it with a high-resolution version.The star and circle marks were missing in the published version.
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery...Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery.We rst derive the projection formulas for a vector onto the feasible sets.The centralized circumcentered-reection method is designed to solve the convex feasibility problem.Some numerical experiments demonstrate the feasibility and e ectiveness of the proposed algorithm,showing superior performance compared to conventional alternating projection methods.展开更多
Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The t...Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.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
The personalization in knowledge portals and knowledge management systems is mainly performed based on users' explicitly specified categories and keywords. The explicit specification approach requires users' p...The personalization in knowledge portals and knowledge management systems is mainly performed based on users' explicitly specified categories and keywords. The explicit specification approach requires users' participation to start personalization services, and has limitation to adapt changes of users' preference. This paper suggests two implicit personalization approaches: automatic user category assignment method and automatic keyword profile generation method. The performances of the implicit personalization approaches are compared with traditional personalization approach using an Internet news site experiment. The result of the experiment shows that the suggested personalization approaches provide sufficient recommendation effectiveness with lessening users' unwanted involvement in personalization process.展开更多
Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as ...Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.展开更多
In the most current Web Services recommendation methods,rating data from service users is rare and the accuracy of the recommendation results cannot be effectively guaranteed.To address this problem,this paper firstly...In the most current Web Services recommendation methods,rating data from service users is rare and the accuracy of the recommendation results cannot be effectively guaranteed.To address this problem,this paper firstly presents a new web service recommendation framework.Based on the proposed framework,a duplex feedback based web service recommendation method( DFBWSRM) is then elaborated,which includes both implicit and explicit feedback data for the calculation of similarities of user preferences during the finding,binding and rating of services.A coordinated recommendation algorithm is also listed in detail.The simulation results demonstrate that the proposed method can satisfyingly increase the accuracy of recommendation results and better meet the requirements of service users.展开更多
With constant deepening of the reform and opening-up,national economic system has changed from planned economy to market economy,and rural survey and statistics remain in a difficult transition period. In this period,...With constant deepening of the reform and opening-up,national economic system has changed from planned economy to market economy,and rural survey and statistics remain in a difficult transition period. In this period,China needs transforming original statistical mode according to market economic system. All levels of government should report and submit a lot and increasing statistical information. Besides,in this period,townships,villages and counties are faced with old and new conflicts. These conflicts perplex implementation of rural statistics and survey and development of rural statistical undertaking,and also cause researches and thinking of reform of rural statistical and survey methods.展开更多
User-analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user rec- ommendation algorithm that tak...User-analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user rec- ommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidi- mensional algorithm is more accurate than a traditional recom- mendation algorithm.展开更多
There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses.To solve the practical problem,we firstly give a series of normalization models for defining the key attributes of ...There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses.To solve the practical problem,we firstly give a series of normalization models for defining the key attributes of teachers’professional foundation,course difficulty coefficient,and comprehensive evaluation of teaching.Then,we define a partial weight function to calculate the key attributes,and obtain the partial recommendation values.Next,we construct a highly sparse Teaching Recommendation Factorization Machines(TRFMs)model,which takes the 5-tuples relation including teacher,course,teachers’professional foundation,course difficulty,teaching evaluation as the feature vector,and take partial recommendation value as the recommendation label.Finally,we design a novel Top-N excellent teacher recommendation algorithm based on TRFMs by course classification on the highly sparse dataset.Experimental results show that the proposed TRFMs and recommendation algorithm can accurately realize the recommendation of excellent teachers on a highly sparse historical teaching dataset.The recommendation accuracy is superior to that of the three-dimensional tensor decomposition model algorithm which also solves sparse datasets.The proposed method can be used as a new recommendation method applied to the teaching arrangements in all kinds of schools,which can effectively improve the teaching quality.展开更多
We search a variety of things over the Internet in our daily lives, and numerous search engines are available to get us more relevant results. With the rapid technological advancement, the internet has become a major ...We search a variety of things over the Internet in our daily lives, and numerous search engines are available to get us more relevant results. With the rapid technological advancement, the internet has become a major source of obtaining information. Further, the advent of the Web2.0 era has led to an increased interaction between the user and the website. It has become challenging to provide information to users as per their interests. Because of copyright restrictions, most of existing research studies are confronting the lack of availability of the content of candidates recommending articles. The content of such articles is not always available freely and hence leads to inadequate recommendation results. Moreover, various research studies base recommendation on user profiles. Therefore, their recommendation needs a significant number of registered users in the system. In recent years, research work proves that Knowledge graphs have yielded better in generating quality recommendation results and alleviating sparsity and cold start issues. Network embedding techniques try to learn high quality feature vectors automatically from network structures, enabling vector-based measurers of node relatedness. Keeping the strength of Network embedding techniques, the proposed citation-based recommendation approach makes use of heterogeneous network embedding in generating recommendation results. The novelty of this paper is in exploiting the performance of a network embedding approach i.e., matapath2vec to generate paper recommendations. Unlike existing approaches, the proposed method has the capability of learning low-dimensional latent representation of nodes (i.e., research papers) in a network. We apply metapath2vec on a knowledge network built by the ACL Anthology Network (all about NLP) and use the node relatedness to generate item (research article) recommendations.展开更多
The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit law...The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.展开更多
In recommendation system,sparse data and cold-start user have always been a challenging problem.Using a linear upper confidence bound(UCB) bandit approach as the item selection strategy based on the user historical ra...In recommendation system,sparse data and cold-start user have always been a challenging problem.Using a linear upper confidence bound(UCB) bandit approach as the item selection strategy based on the user historical ratings and user-item context,we model the recommendation problem as a multi-arm bandit(MAB)problem in this paper.Enabling the engine to recommend while it learns,we adopt probabilistic matrix factorization(PMF) in this strategy learning phase after observing the payoff.In particular,we propose a new approach to get the upper bound statistics out of latent feature matrix.In the experiment,we use two public datasets(Netfilx and MovieLens) to evaluate our proposed model.The model shows good results especially on cold-start users.展开更多
Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
Aiming at the personalized movie recommendation problem,a recommendation algorithm in-tegrating manifold learning and ensemble learning is studied.In this work,manifold learning is used to reduce the dimension of data...Aiming at the personalized movie recommendation problem,a recommendation algorithm in-tegrating manifold learning and ensemble learning is studied.In this work,manifold learning is used to reduce the dimension of data so that both time and space complexities of the model are mitigated.Meanwhile,gradient boosting decision tree(GBDT)is used to train the target user profile prediction model.Based on the recommendation results,Bayesian optimization algorithm is applied to optimize the recommendation model,which can effectively improve the prediction accuracy.The experimental results show that the proposed algorithm can improve the accuracy of movie recommendation.展开更多
To analyze the differences in the transport and distribution of different types of proppants and to address issues such as the short effective support of proppant and poor placement in hydraulically intersecting fract...To analyze the differences in the transport and distribution of different types of proppants and to address issues such as the short effective support of proppant and poor placement in hydraulically intersecting fractures,this study considered the combined impact of geological-engineering factors on conductivity.Using reservoir production parameters and the discrete elementmethod,multispherical proppants were constructed.Additionally,a 3D fracture model,based on the specified conditions of the L block,employed coupled(Computational Fluid Dynamics)CFD-DEM(Discrete ElementMethod)for joint simulations to quantitatively analyze the transport and placement patterns of multispherical proppants in intersecting fractures.Results indicate that turbulent kinetic energy is an intrinsic factor affecting proppant transport.Moreover,the efficiency of placement and migration distance of low-sphericity quartz sand constructed by the DEM in the main fracture are significantly reduced compared to spherical ceramic proppants,with a 27.7%decrease in the volume fraction of the fracture surface,subsequently affecting the placement concentration and damaging fracture conductivity.Compared to small-angle fractures,controlling artificial and natural fractures to expand at angles of 45°to 60°increases the effective support length by approximately 20.6%.During hydraulic fracturing of gas wells,ensuring the fracture support area and post-closure conductivity can be achieved by controlling the sphericity of proppants and adjusting the perforation direction to control the direction of artificial fractures.展开更多
The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profile...The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term interests.However,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior sequences.Consequently,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their interests.This paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)framework.This study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention mechanism.Additionally,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user interests.Experimental results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance indicators.This advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.展开更多
基金upported by the Major Project of Guangzhou National Laboratory(GZNL2024A01002)National Key Plan for Scientific Research and Development of China(2022YFC2303802)+1 种基金National Natural Science Foundation of China(32170651&32370700)Hunan Provincial Natural Science Foundation of China(2024JJ2015).
文摘Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).
基金Natural Science Research Project of Education Department of Anhui Province of China,Grant/Award Number:2023AH051020Key Project of Anhui Province's Science and Technology Innovation Tackle Plan,Grant/Award Number:202423k09020040+3 种基金National Key Research and Development Program of China,Grant/Award Number:2023YFD1802200Natural Science Foundation of Anhui Province,Grant/Award Number:2308085MF21National Natural Science Foundation of China,Grant/Award Numbers:32472007,62301006,62306008University Synergy Innovation Program of Anhui Province,Grant/Award Number:GXXT-2022-046。
文摘In the era of big data,personalised recommendation systems are essential for enhancing user engagement and driving business growth.However,traditional recommendation algorithms,such as collaborative filtering,face significant challenges due to data sparsity,algorithm scalability,and the difficulty of adapting to dynamic user preferences.These limitations hinder the ability of systems to provide highly accurate and personalised recommendations.To address these challenges,this paper proposes a clustering-based recommendation method that integrates an enhanced Grasshopper Optimisation Algorithm(GOA),termed LCGOA,to improve the accuracy and efficiency of recommendation systems by optimising cluster centroids in a dynamic environment.By combining the K-means algorithm with the enhanced GOA,which incorporates a Lévy flight mechanism and multi-strategy co-evolution,our method overcomes the centroid sensitivity issue,a key limitation in traditional clustering techniques.Experimental results across multiple datasets show that the proposed LCGOA-based method significantly outperforms conventional recommendation algorithms in terms of recommendation accuracy,offering more relevant content to users and driving greater customer satisfaction and business growth.
文摘During the final proofing stage of the paper,the wrong version of Fig.2 was accidently used when replacing it with a high-resolution version.The star and circle marks were missing in the published version.
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
基金Supported by the Natural Science Foundation of Guangxi Province(Grant Nos.2023GXNSFAA026067,2024GXN SFAA010521)the National Natural Science Foundation of China(Nos.12361079,12201149,12261026).
文摘Convex feasibility problems are widely used in image reconstruction,sparse signal recovery,and other areas.This paper is devoted to considering a class of convex feasibility problem arising from sparse signal recovery.We rst derive the projection formulas for a vector onto the feasible sets.The centralized circumcentered-reection method is designed to solve the convex feasibility problem.Some numerical experiments demonstrate the feasibility and e ectiveness of the proposed algorithm,showing superior performance compared to conventional alternating projection methods.
基金Supported by the National Natural Science Foundation of China under Grant No.51975138the High-Tech Ship Scientific Research Project from the Ministry of Industry and Information Technology under Grant No.CJ05N20the National Defense Basic Research Project under Grant No.JCKY2023604C006.
文摘Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.
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.
文摘The personalization in knowledge portals and knowledge management systems is mainly performed based on users' explicitly specified categories and keywords. The explicit specification approach requires users' participation to start personalization services, and has limitation to adapt changes of users' preference. This paper suggests two implicit personalization approaches: automatic user category assignment method and automatic keyword profile generation method. The performances of the implicit personalization approaches are compared with traditional personalization approach using an Internet news site experiment. The result of the experiment shows that the suggested personalization approaches provide sufficient recommendation effectiveness with lessening users' unwanted involvement in personalization process.
基金supported by the Natural Science Foundation of Ningxia Province(No.2023AAC03316)the Ningxia Hui Autonomous Region Education Department Higher Edu-cation Key Scientific Research Project(No.NYG2022051)the North Minzu University Graduate Innovation Project(YCX23146).
文摘Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.
基金Sponored by the National Natural Science Foundation of China(Grant No.61201163)the Natural Science Fundation of Jiangsu Province(Grant No.BK2011072)
文摘In the most current Web Services recommendation methods,rating data from service users is rare and the accuracy of the recommendation results cannot be effectively guaranteed.To address this problem,this paper firstly presents a new web service recommendation framework.Based on the proposed framework,a duplex feedback based web service recommendation method( DFBWSRM) is then elaborated,which includes both implicit and explicit feedback data for the calculation of similarities of user preferences during the finding,binding and rating of services.A coordinated recommendation algorithm is also listed in detail.The simulation results demonstrate that the proposed method can satisfyingly increase the accuracy of recommendation results and better meet the requirements of service users.
基金Supported by Project of Business Management Cultivation Discipline in Commerce Department of Rongchang Campus,Southwest University
文摘With constant deepening of the reform and opening-up,national economic system has changed from planned economy to market economy,and rural survey and statistics remain in a difficult transition period. In this period,China needs transforming original statistical mode according to market economic system. All levels of government should report and submit a lot and increasing statistical information. Besides,in this period,townships,villages and counties are faced with old and new conflicts. These conflicts perplex implementation of rural statistics and survey and development of rural statistical undertaking,and also cause researches and thinking of reform of rural statistical and survey methods.
文摘User-analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user rec- ommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidi- mensional algorithm is more accurate than a traditional recom- mendation algorithm.
基金This work was supported by the Planning Subject for the 13th Five-Year Plan of Hunan Provincial Educational Sciences under Grant XJK17BXX006,author D.Y,http://ghkt.hntky.com/.
文摘There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses.To solve the practical problem,we firstly give a series of normalization models for defining the key attributes of teachers’professional foundation,course difficulty coefficient,and comprehensive evaluation of teaching.Then,we define a partial weight function to calculate the key attributes,and obtain the partial recommendation values.Next,we construct a highly sparse Teaching Recommendation Factorization Machines(TRFMs)model,which takes the 5-tuples relation including teacher,course,teachers’professional foundation,course difficulty,teaching evaluation as the feature vector,and take partial recommendation value as the recommendation label.Finally,we design a novel Top-N excellent teacher recommendation algorithm based on TRFMs by course classification on the highly sparse dataset.Experimental results show that the proposed TRFMs and recommendation algorithm can accurately realize the recommendation of excellent teachers on a highly sparse historical teaching dataset.The recommendation accuracy is superior to that of the three-dimensional tensor decomposition model algorithm which also solves sparse datasets.The proposed method can be used as a new recommendation method applied to the teaching arrangements in all kinds of schools,which can effectively improve the teaching quality.
文摘We search a variety of things over the Internet in our daily lives, and numerous search engines are available to get us more relevant results. With the rapid technological advancement, the internet has become a major source of obtaining information. Further, the advent of the Web2.0 era has led to an increased interaction between the user and the website. It has become challenging to provide information to users as per their interests. Because of copyright restrictions, most of existing research studies are confronting the lack of availability of the content of candidates recommending articles. The content of such articles is not always available freely and hence leads to inadequate recommendation results. Moreover, various research studies base recommendation on user profiles. Therefore, their recommendation needs a significant number of registered users in the system. In recent years, research work proves that Knowledge graphs have yielded better in generating quality recommendation results and alleviating sparsity and cold start issues. Network embedding techniques try to learn high quality feature vectors automatically from network structures, enabling vector-based measurers of node relatedness. Keeping the strength of Network embedding techniques, the proposed citation-based recommendation approach makes use of heterogeneous network embedding in generating recommendation results. The novelty of this paper is in exploiting the performance of a network embedding approach i.e., matapath2vec to generate paper recommendations. Unlike existing approaches, the proposed method has the capability of learning low-dimensional latent representation of nodes (i.e., research papers) in a network. We apply metapath2vec on a knowledge network built by the ACL Anthology Network (all about NLP) and use the node relatedness to generate item (research article) recommendations.
基金supported by the Science and Technology Innovation 2030-“New Generation Artificial Intelligence”Major Project(No.2021ZD0113604)China Agriculture Research System of MOF and MARA(No.CARS-23-D07)。
文摘The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.
文摘In recommendation system,sparse data and cold-start user have always been a challenging problem.Using a linear upper confidence bound(UCB) bandit approach as the item selection strategy based on the user historical ratings and user-item context,we model the recommendation problem as a multi-arm bandit(MAB)problem in this paper.Enabling the engine to recommend while it learns,we adopt probabilistic matrix factorization(PMF) in this strategy learning phase after observing the payoff.In particular,we propose a new approach to get the upper bound statistics out of latent feature matrix.In the experiment,we use two public datasets(Netfilx and MovieLens) to evaluate our proposed model.The model shows good results especially on cold-start users.
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
基金Supported by the Educational Commission of Liaoning Province of China(No.LQGD2017027).
文摘Aiming at the personalized movie recommendation problem,a recommendation algorithm in-tegrating manifold learning and ensemble learning is studied.In this work,manifold learning is used to reduce the dimension of data so that both time and space complexities of the model are mitigated.Meanwhile,gradient boosting decision tree(GBDT)is used to train the target user profile prediction model.Based on the recommendation results,Bayesian optimization algorithm is applied to optimize the recommendation model,which can effectively improve the prediction accuracy.The experimental results show that the proposed algorithm can improve the accuracy of movie recommendation.
基金funded by the project of the Major Scientific and Technological Projects of CNOOC in the 14th Five-Year Plan(No.KJGG2022-0701)the CNOOC Research Institute(No.2020PFS-03).
文摘To analyze the differences in the transport and distribution of different types of proppants and to address issues such as the short effective support of proppant and poor placement in hydraulically intersecting fractures,this study considered the combined impact of geological-engineering factors on conductivity.Using reservoir production parameters and the discrete elementmethod,multispherical proppants were constructed.Additionally,a 3D fracture model,based on the specified conditions of the L block,employed coupled(Computational Fluid Dynamics)CFD-DEM(Discrete ElementMethod)for joint simulations to quantitatively analyze the transport and placement patterns of multispherical proppants in intersecting fractures.Results indicate that turbulent kinetic energy is an intrinsic factor affecting proppant transport.Moreover,the efficiency of placement and migration distance of low-sphericity quartz sand constructed by the DEM in the main fracture are significantly reduced compared to spherical ceramic proppants,with a 27.7%decrease in the volume fraction of the fracture surface,subsequently affecting the placement concentration and damaging fracture conductivity.Compared to small-angle fractures,controlling artificial and natural fractures to expand at angles of 45°to 60°increases the effective support length by approximately 20.6%.During hydraulic fracturing of gas wells,ensuring the fracture support area and post-closure conductivity can be achieved by controlling the sphericity of proppants and adjusting the perforation direction to control the direction of artificial fractures.
基金supported by National Natural Science Foundation of China(62072416)Key Research and Development Special Project of Henan Province(221111210500)Key TechnologiesR&DProgram of Henan rovince(232102211053,242102211071).
文摘The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term interests.However,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior sequences.Consequently,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their interests.This paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)framework.This study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention mechanism.Additionally,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user interests.Experimental results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance indicators.This advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.
