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Improving friend recommendation for Online Learning with Fine-Grained Evolving Interest

Journal of Computer Science & Technology 2022年第6期37卷 1444-1463页

作者：邵明民姜文君 Jie Wu 施雨晴任德盛张吉 College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Department of Computer and Information Sciences Temple UniversityPhiladelphia 19122U.S.A. School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Department of Information Engineering Chinese University of Hong KongHong Kong 999077China Department of Mathematics and Computing University of Southern QueenslandBrisbaneQLD 4350Australia

friend recommendation plays a key role in promoting user experience in online social networks(OSNs).However,existing studies usually neglect users’fine-grained interest as well as the evolving feature of interest,which may cause unsuitable *** particular,some OSNs,such as the online learning community,even have little work on friend *** this end,we strive to improve friend recommendation with fine-grained evolving interest in this *** take the online learning community as an application scenario,which is a special type of OSNs for people to learn courses *** partners can help improve learners’learning effect and improve the attractiveness of *** propose a learning partner recommendation framework based on the evolution of fine-grained learning interest(LPRF-E for short).We extract a sequence of learning interest tags that changes over ***,we explore the time feature to predict evolving learning ***,we recommend learning partners by fine-grained interest *** also refine the learning partner recommendation framework with users’social influence(denoted as LPRF-F for differentiation).Extensive experiments on two real datasets crawled from Chinese University MOOC and Douban Book validate that the proposed LPRF-E and LPRF-F models achieve a high accuracy(i.e.,approximate 50%improvements on the precision and the recall)and can recommend learning partners with high quality(e.g.,more experienced and helpful).

关键词： online social network friend recommendation fine-grained interest evolving feature tag online learning community

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From Interest to Location： Neighbor-Based friend recommendation in Social Media

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Journal of Computer Science & Technology 2015年第6期30卷 1188-1200页

作者：朱金奇鲁力马春梅 School of Computer and Information Engineering Tianjin Normal University Tianjin 300387 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu 611731 China

Recent years have witnessed the tremendous development of social media, which attracts a vast number of Internet users. The tweets these users posted provide an effective way of understanding user behaviors. A large amount of previous work benefits from mining user interest to make friend recommendation. However, the potentially strong but inconspicuous relation between location and interest interaction among social media users is overlooked in these studies. Different from the previous researches, we propose a new concept named neighbor-based friend recommendation （NBFR） to improve the friend recommendation results. By recommending surrounding users who have similar interest to each other, social media users are provided a unique opportunity to interact with surrounding people they may want to know. Based on this concept, we first mine users＇ interest from short tweets, and then propose to model the user interest with multiple topics under the hypercube structure for friend recommendation. At the same time, we also offer a topic matching shortcut algorithm for more extensive recommendation. The evaluations using the data gathered from the real users demonstrate the advantage of NBFR compared with the traditional recommendation approaches.

关键词： micro-blogging system neighbor friend recommendation hypercube

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An Attention-Based friend recommendation Model in Social Network

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Computers, Materials & Continua 2020年第12期65卷 2475-2488页

作者： Chongchao Cai Huahu Xu Jie Wan Baiqing Zhou Xiongwei Xie School of Computer Engineering and Science Shanghai UniversityShanghai200444China College of Logistic and Information Engineering Huzhou Vocational&Technical CollegeHuzhou313000China Google New YorkUSA

In social networks,user attention affects the user’s decision-making,resulting in a performance alteration of the recommendation *** systems make recommendations mainly according to users’preferences with a particular focus on ***,the significance of users’attention and the difference in the influence of different users and items are often ***,this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social *** first constructed the basic user and item matrix via convolutional neural networks(CNN).Then,we obtained user preferences by using the relationships between users and items,which were later inputted into our model to learn the preferences between *** error performance of the proposed method was compared with the traditional solutions based on collaborative filtering.A comprehensive performance evaluation was also conducted using large-scale real-world datasets collected from three popular location-based social *** experimental results revealed that our proposal outperforms the traditional methods in terms of recommendation performance.

关键词： friend recommendation collaborative filtering attention mechanism deep learning

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