A Weibo Recommendation Algorithm Integrating User Interests

2021 ◽  
Author(s):  
Xiaojing Zhu
Keyword(s):  
Recommendation Algorithm ◽  
User Interests

Two Phase Recommendation Algorithm Based on Clustering User Interest and Trust

2014 ◽  
Vol 543-547 ◽  
pp. 1856-1859
Author(s):  
Xiang Cui ◽  
Gui Sheng Yin
Keyword(s):  
Social Networks ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Information Overload ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Two Phase ◽  
Rating Data ◽  
User Interests ◽  
The Future

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. We need a method to solve such as what items to buy, what music to listen, or what news to read. The diversification of user interests and untruthfulness of rating data are the important problems of recommendation. In this article, we propose to use two phase recommendation based on user interest and trust ratings that have been given by actors to items. In the paper, we deal with the uncertain user interests by clustering firstly. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, we construct trust based social networks, and work out the trust scoring, in the class. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.

scholarly journals RESEARCH ON PERSONALIZED RECOMMENDATION ALGORITHM FUSING TIME AND LOCATION

CONVERTER ◽  
2021 ◽  
pp. 302-314
Author(s):  
Zhongyong Fan, Yongqian Zhao, Yongkang Wang, Zhijun Zhang
Keyword(s):  
Recommendation Systems ◽  
Geographic Location ◽  
Personalized Recommendation ◽  
Location Information ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
User Interests ◽  
Dependent Change ◽  
Collaborative Filtering Algorithm ◽  
Time Dependent Change

With development of recommendation systems, they are faced with more and more challenges. In order to relieve problems existing in commodity selection by users of different preferences from different regions, personalized recommendation based on location information has emerged. Nowadays most recommendation systems based on location information neglect the fact that users’ preference will change with time. To solve the above problem, geographic location and time factor of users are effectively combined in this paper, and a personalized recommendation algorithm TLPR combining time and location information is proposed. This algorithm determines the users’ geographic location according to postcode information of the users, uses pyramid quadtree model to distribute users into nodes at each layer in the pyramid, utilizes collaborative filtering algorithm for local recommendation in each node, introduces a time function to regulate time-dependent change of user interests when calculating user similarity at each node and finally realizes a comprehensive recommendation by distributing a weight for recommendation result at each layer in the pyramid quadtree. A comparative experience is carried out for recommendation performance of this algorithm on MovieLens dataset, and experimental results indicate that this algorithm is of better recommendation effect

TrustSVD: A Novel Trust-Based Matrix Factorization Model with User Trust and Item Ratings

2017 ◽  
Vol 7 (11) ◽  
pp. 7 ◽  
Author(s):  
K Sobha Rani
Keyword(s):  
Matrix Factorization ◽  
Social Trust ◽  
State Of The Art ◽  
Data Sets ◽  
Real World Data ◽  
Recommendation Algorithm ◽  
Active User ◽  
Factorization Model ◽  
The Social ◽  
Matrix Factorization Technique

Collaborative filtering suffers from the problems of data sparsity and cold start, which dramatically degrade recommendation performance. To help resolve these issues, we propose TrustSVD, a trust-based matrix factorization technique. By analyzing the social trust data from four real-world data sets, we conclude that not only the explicit but also the implicit influence of both ratings and trust should be taken into consideration in a recommendation model. Hence, we build on top of a state-of-the-art recommendation algorithm SVD++ which inherently involves the explicit and implicit influence of rated items, by further incorporating both the explicit and implicit influence of trusted users on the prediction of items for an active user. To our knowledge, the work reported is the first to extend SVD++ with social trust information. Experimental results on the four data sets demonstrate that our approach TrustSVD achieves better accuracy than other ten counterparts, and can better handle the concerned issues.

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