A Novel Collaborative Recommendation Algorithm Integrating Probabilistic Matrix Factorization and Neighbor Model

2015 ◽  
Vol 12 (5) ◽  
pp. 2011-2019
Author(s):  
Hongtao Yu
Keyword(s):  
Matrix Factorization ◽  
Recommendation Algorithm ◽  
Collaborative Recommendation ◽  
Probabilistic Matrix Factorization

scholarly journals Recommendation Algorithm Based on Probabilistic Matrix Factorization with Adaboost

2020 ◽  
Vol 65 (2) ◽  
pp. 1591-1603
Author(s):  
Hongtao Bai ◽  
Xuan Li ◽  
Lili He ◽  
Longhai Jin ◽  
Chong Wang ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Recommendation Algorithm ◽  
Probabilistic Matrix Factorization

scholarly journals Probabilistic Matrix Factorization Recommendation Algorithm with User Trust Similarity

2018 ◽  
Vol 208 ◽  
pp. 05004
Author(s):  
Yuxin Dong ◽  
Shuyun Fang ◽  
Kai Jiang ◽  
Fukun Chen ◽  
Guisheng Yin
Keyword(s):  
Conference Proceedings ◽  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Cold Start ◽  
Recommendation Algorithm ◽  
User Similarity ◽  
Similarity Calculation ◽  
Probabilistic Matrix Factorization ◽  
User Trust

In this paper, we describe the formatting guidelines for Conference Proceedings. Whether the user similarity calculation is reasonable in the traditional collaborative filtering recommendation algorithm directly affects the result of the collaborative filtering recommendation algorithm. This paper proposes a probabilistic matrix factorization recommendation algorithm with user trust similarity which combines improved similarity of users’ trust and probability matrix factorization recommendation method. The results show that proposed algorithm could relieve user cold start issues and effectively reduce the error of recommendation.

Recommendation algorithm of probabilistic matrix factorization based on directed trust

2021 ◽  
Vol 93 ◽  
pp. 107206
Author(s):  
Shangshang Xu ◽  
Haiyan Zhuang ◽  
Fuzhen Sun ◽  
Shaoqing Wang ◽  
Tianhui Wu ◽  
...  
Keyword(s):  
Matrix Factorization ◽  
Recommendation Algorithm ◽  
Probabilistic Matrix Factorization

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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