Robust multi-objective visual bayesian personalized ranking for multimedia recommendation

2021 ◽  
Author(s):  
Agyemang Paul ◽  
Zhefu Wu ◽  
Kai Liu ◽  
Shufeng Gong
Keyword(s):  
Multi Objective ◽  
Bayesian Personalized Ranking ◽  
Multimedia Recommendation ◽  
Personalized Ranking

scholarly journals CMBPR: Category-Aided Multi-Channel Bayesian Personalized Ranking for Short Video Recommendation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 48209-48223 ◽  
Author(s):  
Xichen Wang ◽  
Chen Gao ◽  
Jingtao Ding ◽  
Yong Li ◽  
Depeng Jin
Keyword(s):  
Short Video ◽  
Bayesian Personalized Ranking ◽  
Video Recommendation ◽  
Personalized Ranking

scholarly journals Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding

2019 ◽  
Vol 4 (2) ◽  
pp. 119-131 ◽  
Author(s):  
Vachik S. Dave ◽  
Baichuan Zhang ◽  
Pin-Yu Chen ◽  
Mohammad Al Hasan
Keyword(s):  
Network Embedding ◽  
Attributed Network ◽  
Bayesian Personalized Ranking ◽  
Personalized Ranking

scholarly journals Comparison of Three Recent Personalization Algorithms

2020 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Matrix Factorization ◽  
Parallel Implementation ◽  
Apache Spark ◽  
Test Set ◽  
Hit Rate ◽  
The Matrix ◽  
Bayesian Personalized Ranking ◽  
Personalized Ranking

<p>Personalization algorithms recommend products to users based on their previous interactions with the system. The products could be books, movies, or products in a retail system. The earliest personalization algorithms were based on factorization of the user-item matrix where each entry in the matrix would correspond to an interaction, or absence of an interaction of the user with the product. In this article, we compare three recently developed personalization algorithms. The three algorithms are Bayesian Personalized Ranking, Taxonomy Discovery for Personalized Recommendations and Multi-Matrix Factorization. We compare the three algorithms on the hit rate @ position 10 on a held out test set on 1 million users and 200 thousand items in the catalog of Target Corporation. We report our findings in table 1. We develop all three algorithms on an Apache Spark parallel implementation.</p>

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