期刊文献+
共找到21篇文章
< 1 2 >
每页显示 20 50 100
Ontology-based framework for personalized recommendation in digital libraries 被引量:3
1
作者 颜端武 岑咏华 +1 位作者 张炜 毛平 《Journal of Southeast University(English Edition)》 EI CAS 2006年第3期385-388,共4页
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
关键词 digital library personalized recommendation ONTOLOGY content retrieval user preference
下载PDF
Research on the Personalized Recommendation of Clothing Based on Rough Set Theory
2
作者 Lin Qun Yan Ruixia Han Qiuying 《International English Education Research》 2015年第5期6-10,共5页
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
关键词 rough set CLOTHING personalized recommendation
下载PDF
Time-Aware LSTM Neural Networks for Dynamic Personalized Recommendation on Business Intelligence
3
作者 Xuan Yang James A.Esquivel 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2024年第1期185-196,共12页
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
关键词 personalized recommendations evolving interests EMBEDDING LsTM networks
原文传递
Region-aware neural graph collaborative filtering for personalized recommendation
4
作者 Shengwen Li Renyao Chen +5 位作者 Chenpeng Sun Hong Yao Xuyang Cheng Zhuoru Li Tailong Li Xiaojun Kang 《International Journal of Digital Earth》 SCIE EI 2022年第1期1446-1462,共17页
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
关键词 Collaborative filtering neural graph collaborative filtering geographical region personalized recommendation graph convolution networks
原文传递
The Design and Realization of Personalized E-commerce Recommendation System
5
作者 Guofeng ZHANG 《International Journal of Technology Management》 2015年第4期27-29,共3页
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
关键词 E-COMMERCE personalized recommendation recommendation system
下载PDF
Research and implementation of a personalized book recommendation algorithm --Taking the library of Jinan University as an example
6
作者 LI Tianzhang ZHU Yijia XIAO Liping 《International English Education Research》 2018年第3期20-22,共3页
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
关键词 recommendation system book recommendation personalized recommendation algorithm
下载PDF
Multirelationship Aware Personalized Recommendation Model
7
作者 Hongtao Song Feng Wang +1 位作者 Zhiqiang Ma Qilong Han 《国际计算机前沿大会会议论文集》 2022年第1期123-136,共14页
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
关键词 Graph neural networks ATTENTION Relationship aware personalized recommendation
原文传递
Privacy-Preserving Recommendation Based on Kernel Method in Cloud Computing 被引量:1
8
作者 Tao Li Qi Qian +2 位作者 Yongjun Ren Yongzhen Ren Jinyue Xia 《Computers, Materials & Continua》 SCIE EI 2021年第1期779-791,共13页
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
关键词 IOT kernel method PRIVACY-PRESERVING personalized recommendation random perturbation
下载PDF
Representation learning: serial-autoencoder for personalized recommendation
9
作者 Yi ZHU Yishuai GENG +2 位作者 Yun LI Jipeng QIANG Xindong WU 《Frontiers of Computer Science》 SCIE EI 2024年第4期61-72,共12页
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
关键词 personalized recommendation autoencoder representation learning collaborative filtering
原文传递
Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning
10
作者 Saurabh Pal Pijush Kanti Dutta Pramanik +3 位作者 Musleh Alsulami Anand Nayyar Mohammad Zarour Prasenjit Choudhury 《Computers, Materials & Continua》 SCIE EI 2021年第12期3981-4001,共21页
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
关键词 personalized e-learning DEMATEL learner model ONTOLOGY learner context personalized recommendation adaptive decisions
下载PDF
Recommending Personalized POIs from Location Based Social Network
11
作者 Haiying Che Di Sang Billy Zimba 《Journal of Beijing Institute of Technology》 EI CAS 2018年第1期137-145,共9页
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
关键词 location based social network personalized geographical influence location recommendation non-parametric probability estimates
下载PDF
An E-Commerce Recommender System Based on Content-Based Filtering 被引量:3
12
作者 HE Weihong CAO Yi 《Wuhan University Journal of Natural Sciences》 CAS 2006年第5期1091-1096,共6页
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
关键词 E-COMMERCE recommender system personalized recommendation content-based filtering Vector Spatial Model(VSM)
下载PDF
Privacy-Preserving Collaborative Filtering Algorithm Based on Local Differential Privacy
13
作者 Ting Bao Lei Xu +3 位作者 Liehuang Zhu Lihong Wang Ruiguang Li Tielei Li 《China Communications》 SCIE CSCD 2021年第11期42-60,共19页
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
关键词 personalized recommendation collaborative filtering data perturbation privacy protection local differential privacy
下载PDF
Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network
14
作者 Yong Yu Yongjun Luo +4 位作者 Tong Li Shudong Li Xiaobo Wu Jinzhuo Liu Yu Jiang 《Computers, Materials & Continua》 SCIE EI 2020年第4期489-507,共19页
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
关键词 personalized recommendation one-mode projection weighted bipartite network novelty recommendation diversity
下载PDF
Integrating Machine Learning and Evidential Reasoning for User Profiling and Recommendation
15
作者 Toan Nguyen Mau Quang-Hung Le +2 位作者 Duc-Vinh Vo Duy Doan Van-Nam Huynh 《Journal of Systems Science and Systems Engineering》 SCIE EI CSCD 2023年第4期393-412,共20页
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
关键词 Machine learning Dempster-Shafer theory of evidence user profiles personalized recommendation PREFERENCES
原文传递
Personalized travel route recommendation using collaborative filtering based on GPS trajectories 被引量:6
16
作者 Ge Cui Jun Luo Xin Wang 《International Journal of Digital Earth》 SCIE EI 2018年第3期284-307,共24页
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
关键词 Historical GPS trajectories personalized travel route recommendation collaborative filtering naïve Bayes model
原文传递
Trust-Based Personalized Service Recommendation: A Network Perspective 被引量:3
17
作者 邓水光 黄龙涛 +1 位作者 吴健 吴朝晖 《Journal of Computer Science & Technology》 SCIE EI CSCD 2014年第1期69-80,共12页
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
关键词 personalized service recommendation TRUST network modeling and analysis collaborative filtering
原文传递
Collaborative filtering recommendation algorithm based on interactive data classification 被引量:4
18
作者 Ji Yimu Li Ke +3 位作者 Liu Shangdong Liu Qiang Yao Haichang Li Kui 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2020年第5期1-12,共12页
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
关键词 personalized recommendation deep learning CLUSTERING collaborative filtering
原文传递
The construction of personalized virtual landslide disaster environments based on knowledge graphs and deep neural networks 被引量:3
19
作者 Yunhao Zhang Jun Zhu +5 位作者 Qing Zhu Yakun Xie Weilian Li Lin Fu Junxiao Zhang Jianmei Tan 《International Journal of Digital Earth》 SCIE 2020年第12期1637-1655,共19页
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
关键词 Landslide disaster scene virtual disaster environment knowledge graph deep neural network personalized recommendation
原文传递
Multi-factor Fusion POI Recommendation Model
20
作者 Xinxing Ma Jinghua Zhu +1 位作者 Shuo Zhang Yingli Zhong 《国际计算机前沿大会会议论文集》 2020年第2期21-35,共15页
In the context of the rapid development of location-based socialnetworks (LBSN), point of interest (POI) recommendation becomes an importantservice in LBSN. The POI recommendation service aims to recommendsome new pla... In the context of the rapid development of location-based socialnetworks (LBSN), point of interest (POI) recommendation becomes an importantservice in LBSN. The POI recommendation service aims to recommendsome new places that may be of interest to users, help users to better understandtheir cities, and improve users’ experience of the platform. Although the geographicinfluence, similarity of POIs, and user check-ins information have beenused in the existing work recommended by POI, little existing work consideredcombing the aforementioned information. In this paper, we propose to makerecommendations by combing user ratings with the above information. Wemodel four types of information under a unified POI recommendation frameworkand this model is called extended user preference model based on matrixfactorization, referred to as UPEMF. Experiments were conducted on two realworld datasets, and the results show that the proposed method improves theaccuracy of POI recommendations compared to other recent methods. 展开更多
关键词 Multi-factor fusion model Matrix factorization Euclidean distance personalized recommendation
原文传递
上一页 1 2 下一页 到第
使用帮助 返回顶部