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
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
Nowadays,the personalized recommendation has become a research hotspot for addressing information overload.Despite this,generating effective recommendations from sparse data remains a challenge.Recently,auxiliary info...Nowadays,the personalized recommendation has become a research hotspot for addressing information overload.Despite this,generating effective recommendations from sparse data remains a challenge.Recently,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited expressiveness.Due to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite popular.However,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model scalability.To address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature representations.Specifically,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the input.Second,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating matrix.The output rating information is used for recommendation prediction.Extensive experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.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
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
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
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
The existing methods using social information can alleviate the data sparsity issue in collaborative filtering recommendation,but they do not fully tap the complex and diverse user relationships,so it is difficult to ...The existing methods using social information can alleviate the data sparsity issue in collaborative filtering recommendation,but they do not fully tap the complex and diverse user relationships,so it is difficult to obtain an accurate modeling representation of the user.To solve this,we propose a multirelationship aware personalized recommendation(MrAPR)model,which aggregates the various relationships between social users from two aspects of the user’s personal information and interaction sequence.Based on the comprehensive and accurate relationship graphs established,the graph neural network and attention network are used to adaptively distinguish the importance of different relationships and improve the aggregation reliability of multiple relationships.The MrAPR model better describes the characteristics of user interest and can be compatible with the existing sequence recommendation methods.The experimental results on two real-world datasets clearly show the effectiveness of the MrAPR model.展开更多
The advent of the digital age has profoundly changed consumers’mindsets and habits.The rapid development of e-commerce and the widespread use of mobile applications have created enormous demand for individual recomme...The advent of the digital age has profoundly changed consumers’mindsets and habits.The rapid development of e-commerce and the widespread use of mobile applications have created enormous demand for individual recommendation systems based on mass data.This system not only increases the convenience of purchases and conversions but also alters the purchasing behavior of consumers,leading them to make choices subconsciously.Potential risks associated with large-scale data sharing and usage have heightened consumer concerns regarding privacy,thereby weakening the foundational trust in platforms and deterring them from shopping.Additionally,the rapid growth of e-commerce in the digital age,coupled with changing market circumstances,has intensified psychological pressure on consumers,making their decision-making processes more complex and difficult.Furthermore,the program will explore issues related to improving customer experience,developing individual marketing strategies,and designing customer loyalty plans.It will also address questions of privacy in a digital environment,the dilemmas of excessive or disruptive consumption behavior,and the complexity and diversity of consumer behavior in the face of digital change.The objective of this study is to develop a fear study that will enable a better understanding of the impact of online shopping on consumer behavior and provide a strategic guide for retailers to meet the challenges and opportunities presented by the digital age.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
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
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
Mobile edge computing(MEC)is an emerging technolohgy that extends cloud computing to the edge of a network.MEC has been applied to a variety of services.Specially,MEC can help to reduce network delay and improve the s...Mobile edge computing(MEC)is an emerging technolohgy that extends cloud computing to the edge of a network.MEC has been applied to a variety of services.Specially,MEC can help to reduce network delay and improve the service quality of recommendation systems.In a MEC-based recommendation system,users’rating data are collected and analyzed by the edge servers.If the servers behave dishonestly or break down,users’privacy may be disclosed.To solve this issue,we design a recommendation framework that applies local differential privacy(LDP)to collaborative filtering.In the proposed framework,users’rating data are perturbed to satisfy LDP and then released to the edge servers.The edge servers perform partial computing task by using the perturbed data.The cloud computing center computes the similarity between items by using the computing results generated by edge servers.We propose a data perturbation method to protect user’s original rating values,where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation.And to enhance the protection of privacy,we propose two methods to protect both users’rating values and rating behaviors.Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.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
User profiles representing users’preferences and interests play an important role in many applications of personalized recommendation.With the rapid growth of social platforms,there is a critical need for efficient s...User profiles representing users’preferences and interests play an important role in many applications of personalized recommendation.With the rapid growth of social platforms,there is a critical need for efficient solutions to learn user profiles from the information they shared on social platforms so as to improve the quality of recommendation services.The problem of user profile learning is significantly challenging due to difficulty in handling data from multiple sources,in different formats and often associated with uncertainty.In this paper,we introduce an integrated approach that combines advanced Machine Learning techniques with evidential reasoning based on Dempster-Shafer theory of evidence for user profiling and recommendation.The developed methods for user profile learning and multi-criteria collaborative filtering are demonstrated with experimental results and analysis that show the effectiveness and practicality of the integrated approach.A proposal for extending multi-criteria recommendation systems by incorporating user profiles learned from different sources of data into the recommendation process so as to provide better recommendation capabilities is also highlighted.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
Virtual Landslide Disaster environments are important for multilevel simulation,analysis and decision-making about Landslide Disasters.However,in the existing related studies,complex disaster scene objects and relatio...Virtual Landslide Disaster environments are important for multilevel simulation,analysis and decision-making about Landslide Disasters.However,in the existing related studies,complex disaster scene objects and relationships are not deeply analyzed,and the scene contents are fixed,which is not conducive to meeting multilevel visualization task requirements for diverse users.To resolve the above issues,a construction method for Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural networks is proposed in this paper.The characteristics of relationships among users,scenes and data were first discussed in detail;then,a knowledge graph of virtual Landslide Disaster environments was established to clarify the complex relationships among disaster scene objects,and a Deep Neural network was introduced to mine the user history information and the relationships among object entities in the knowledge graph.Therefore,a personalized Landslide Disaster scene data recommendation mechanism was proposed.Finally,a prototype system was developed,and an experimental analysis was conducted.The experimental results show that the method can be used to recommend intelligently appropriate disaster information and scene data to diverse users.The recommendation accuracy stabilizes above 80%–a level able to effectively support The Construction of Personalized Virtual Landslide Disaster environments.展开更多
In the matrix factorization(MF)based collaborative filtering recommendation method,the most critical part is to deal with the interaction between the features of users and items.The mainstream approach is to use the i...In the matrix factorization(MF)based collaborative filtering recommendation method,the most critical part is to deal with the interaction between the features of users and items.The mainstream approach is to use the inner product for MF to describe the user-item relationship.However,as a shallow model,MF has its limitations in describing the relationship between data.In addition,when the size of the data is large,the performance of MF is often poor due to data sparsity and noise.This paper presents a model called PIDC,short for potential interaction data clustering based deep learning recommendation.First,it uses classifiers to filter and cluster recommended items to solve the problem of sparse training data.Second,it combines MF and multi-layer perceptron(MLP)to optimize the prediction effect,and the limitation of inner product on the model expression ability is eliminated.The proposed model PIDC is tested on two datasets.The experimental results show that compared with the existing benchmark algorithm,the model improved the recommendation effect.展开更多
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.
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.
基金National Natural Science Foundation of China(Grant Nos.61906060,62076217,and 62120106008)National Key R&D Program of China(No.2016YFC0801406)Natural Science Foundation of the Jiangsu Higher Education Institutions(No.20KJB520007).
文摘Nowadays,the personalized recommendation has become a research hotspot for addressing information overload.Despite this,generating effective recommendations from sparse data remains a challenge.Recently,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited expressiveness.Due to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite popular.However,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model scalability.To address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature representations.Specifically,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the input.Second,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating matrix.The output rating information is used for recommendation prediction.Extensive experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.
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.
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.
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supported by the National Key R&D Program of China under Grant No.2020YFB1710200the National Natural Science Foundation of China under Grant No.61872105 and No.62072136.
文摘The existing methods using social information can alleviate the data sparsity issue in collaborative filtering recommendation,but they do not fully tap the complex and diverse user relationships,so it is difficult to obtain an accurate modeling representation of the user.To solve this,we propose a multirelationship aware personalized recommendation(MrAPR)model,which aggregates the various relationships between social users from two aspects of the user’s personal information and interaction sequence.Based on the comprehensive and accurate relationship graphs established,the graph neural network and attention network are used to adaptively distinguish the importance of different relationships and improve the aggregation reliability of multiple relationships.The MrAPR model better describes the characteristics of user interest and can be compatible with the existing sequence recommendation methods.The experimental results on two real-world datasets clearly show the effectiveness of the MrAPR model.
文摘The advent of the digital age has profoundly changed consumers’mindsets and habits.The rapid development of e-commerce and the widespread use of mobile applications have created enormous demand for individual recommendation systems based on mass data.This system not only increases the convenience of purchases and conversions but also alters the purchasing behavior of consumers,leading them to make choices subconsciously.Potential risks associated with large-scale data sharing and usage have heightened consumer concerns regarding privacy,thereby weakening the foundational trust in platforms and deterring them from shopping.Additionally,the rapid growth of e-commerce in the digital age,coupled with changing market circumstances,has intensified psychological pressure on consumers,making their decision-making processes more complex and difficult.Furthermore,the program will explore issues related to improving customer experience,developing individual marketing strategies,and designing customer loyalty plans.It will also address questions of privacy in a digital environment,the dilemmas of excessive or disruptive consumption behavior,and the complexity and diversity of consumer behavior in the face of digital change.The objective of this study is to develop a fear study that will enable a better understanding of the impact of online shopping on consumer behavior and provide a strategic guide for retailers to meet the challenges and opportunities presented by the digital age.
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.
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.
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supported by National Natural Science Foundation of China(No.61871037)supported by Natural Science Foundation of Beijing(No.M21035).
文摘Mobile edge computing(MEC)is an emerging technolohgy that extends cloud computing to the edge of a network.MEC has been applied to a variety of services.Specially,MEC can help to reduce network delay and improve the service quality of recommendation systems.In a MEC-based recommendation system,users’rating data are collected and analyzed by the edge servers.If the servers behave dishonestly or break down,users’privacy may be disclosed.To solve this issue,we design a recommendation framework that applies local differential privacy(LDP)to collaborative filtering.In the proposed framework,users’rating data are perturbed to satisfy LDP and then released to the edge servers.The edge servers perform partial computing task by using the perturbed data.The cloud computing center computes the similarity between items by using the computing results generated by edge servers.We propose a data perturbation method to protect user’s original rating values,where the Harmony mechanism is modified so as to preserve the accuracy of similarity computation.And to enhance the protection of privacy,we propose two methods to protect both users’rating values and rating behaviors.Experimental results on real-world data demonstrate that the proposed methods perform better than existing differentially private recommendation methods.
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.
基金This work is supported by the University of Information Technology-Vietnam National University Ho Chi Minh City under grant No.D1-2023-10.
文摘User profiles representing users’preferences and interests play an important role in many applications of personalized recommendation.With the rapid growth of social platforms,there is a critical need for efficient solutions to learn user profiles from the information they shared on social platforms so as to improve the quality of recommendation services.The problem of user profile learning is significantly challenging due to difficulty in handling data from multiple sources,in different formats and often associated with uncertainty.In this paper,we introduce an integrated approach that combines advanced Machine Learning techniques with evidential reasoning based on Dempster-Shafer theory of evidence for user profiling and recommendation.The developed methods for user profile learning and multi-criteria collaborative filtering are demonstrated with experimental results and analysis that show the effectiveness and practicality of the integrated approach.A proposal for extending multi-criteria recommendation systems by incorporating user profiles learned from different sources of data into the recommendation process so as to provide better recommendation capabilities is also highlighted.
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 by the National Key Research and Development Program of China[grant number 2016YFC0803105]the National Natural Science Foundation of China[grant numbers 41801297,41801301 and 41941019].
文摘Virtual Landslide Disaster environments are important for multilevel simulation,analysis and decision-making about Landslide Disasters.However,in the existing related studies,complex disaster scene objects and relationships are not deeply analyzed,and the scene contents are fixed,which is not conducive to meeting multilevel visualization task requirements for diverse users.To resolve the above issues,a construction method for Personalized Virtual Landslide Disaster Environments Based on Knowledge Graphs and Deep Neural networks is proposed in this paper.The characteristics of relationships among users,scenes and data were first discussed in detail;then,a knowledge graph of virtual Landslide Disaster environments was established to clarify the complex relationships among disaster scene objects,and a Deep Neural network was introduced to mine the user history information and the relationships among object entities in the knowledge graph.Therefore,a personalized Landslide Disaster scene data recommendation mechanism was proposed.Finally,a prototype system was developed,and an experimental analysis was conducted.The experimental results show that the method can be used to recommend intelligently appropriate disaster information and scene data to diverse users.The recommendation accuracy stabilizes above 80%–a level able to effectively support The Construction of Personalized Virtual Landslide Disaster environments.
基金the National Key Research and Development Program of China(2017YFB1401300,2017YFB1401301)the National Natural Science Foundation of China(61902194)+2 种基金the Outstanding Youth of Jiangsu Natural Science Foundation(BK20170100)the Key Research and Development Program of Jiangsu(BE2017166)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(19KJB520046)。
文摘In the matrix factorization(MF)based collaborative filtering recommendation method,the most critical part is to deal with the interaction between the features of users and items.The mainstream approach is to use the inner product for MF to describe the user-item relationship.However,as a shallow model,MF has its limitations in describing the relationship between data.In addition,when the size of the data is large,the performance of MF is often poor due to data sparsity and noise.This paper presents a model called PIDC,short for potential interaction data clustering based deep learning recommendation.First,it uses classifiers to filter and cluster recommended items to solve the problem of sparse training data.Second,it combines MF and multi-layer perceptron(MLP)to optimize the prediction effect,and the limitation of inner product on the model expression ability is eliminated.The proposed model PIDC is tested on two datasets.The experimental results show that compared with the existing benchmark algorithm,the model improved the recommendation effect.
