scholarly journals Learning to Recommend Point-of-Interest with the Weighted Bayesian Personalized Ranking Method in LBSNs

Information ◽  
2017 ◽  
Vol 8 (1) ◽  
pp. 20 ◽  
Author(s):  
Lei Guo ◽  
Haoran Jiang ◽  
Xinhua Wang ◽  
Fangai Liu
Keyword(s):  
Ranking Method ◽  
Point Of Interest ◽  
Bayesian Personalized Ranking ◽  
Personalized Ranking

scholarly journals Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking

2017 ◽  
Author(s):  
Jing He ◽  
Xin Li ◽  
Lejian Liao
Keyword(s):  
Ranking List ◽  
Point Of Interest ◽  
Spatial Influence ◽  
Poi Recommendation ◽  
Bayesian Personalized Ranking ◽  
Real World Datasets ◽  
Location Based Social Networks ◽  
Luce Model ◽  
Personalized Ranking ◽  
High Computational Complexity

Next Point-of-interest (POI) recommendation has become an important task for location-based social networks (LBSNs). However, previous efforts suffer from the high computational complexity and the transition pattern between POIs has not been well studied. In this paper, we propose a two-fold approach for next POI recommendation. First, the preferred next category is predicted by using a third-rank tensor optimized by a Listwise Bayesian Personalized Ranking (LBPR) approach. Specifically we introduce two functions, namely Plackett-Luce model and cross entropy, to generate the likelihood of ranking list for posterior computation. Then POI candidates filtered by the predicated category are ranked based on the spatial influence and category ranking influence. Extensive experiments on two real-world datasets demonstrate the significant improvements of our methods over several state-of-the-art methods.

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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