Improving the Recommendation of Collaborative Filtering by Fusing Trust Network

2012 ◽  
Author(s):  
Bo Yang ◽  
Pengfei Zhao ◽  
Shuqiu Ping ◽  
Jing Huang
Keyword(s):  
Collaborative Filtering ◽  
Trust Network

scholarly journals Research on User Interest Expression and Recommendation Service based on Three-dimensional Relationship of Users and Items

2020 ◽  
Vol 8 (5) ◽  
pp. 01-15
Author(s):  
Dasong Sun ◽  
Shuqing Li ◽  
Wenjing Yan ◽  
Fusen Jiao ◽  
Junpeng Chen
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Memory Function ◽  
Three Dimensional ◽  
Matrix Decomposition ◽  
Data Sets ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Trust Network ◽  
Experimental Conditions

The existing recommendation algorithms often rely heavily on the original score information in the user rating matrix. However, the user's rating of items does not fully reflect the user's real interest. Therefore, the key to improve the existing recommendation system algorithm effectively is to eliminate the influence of these unfavorable factors and the accuracy of the recommendation algorithm can be improved by correcting the original user rating information reasonably. This paper makes a comprehensive theoretical analysis and method design from three aspects: the quality of the item, the memory function of the user and the influence of the social friends trusted by the user on the user's rating. Based on these methods, this paper finally proposes a collaborative filtering recommendation algorithm (FixCF) based on user rating modification. Using data sets such as Movielens, Epinions and Flixster, the data sets are divided into five representative subsets, and the experimental demonstration is carried out. FixCF and classical collaborative filtering algorithms, existing matrix decomposition-based algorithms and trust network-based inference are compared. The experimental results show that the accuracy and coverage of FixCF have been improved under many experimental conditions.

scholarly journals Boosting Memory-Based Collaborative Filtering Using Content-Metadata

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 561 ◽  
Author(s):  
Kyung Soo Kim ◽  
Doo Soo Chang ◽  
Yong Suk Choi
Keyword(s):  
Collaborative Filtering ◽  
Recommendation Systems ◽  
Cold Start ◽  
Experimental Results ◽  
Significant Enhancement ◽  
Trust Network ◽  
Network Information ◽  
Personalized Services ◽  
User Content

Recommendation systems are widely used in conjunction with many popular personalized services, which enables people to find not only content items they are currently interested in, but also those in which they might become interested. Many recommendation systems employ the memory-based collaborative filtering (CF) method, which has been generally accepted as one of consensus approaches. Despite the usefulness of the CF method for successful recommendation, several limitations remain, such as sparsity and cold-start problems that degrade the performance of CF systems in practice. To overcome these limitations, we propose a content-metadata-based approach that uses content-metadata in an effective way. By complementarily combining content-metadata with conventional user-content ratings and trust network information, our proposed approach remarkably increases the amount of suggested content and accurately recommends a large number of additional content items. Experimental results show a significant enhancement of performance, especially under a sparse rating environment.

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