TY - JOUR
T1 - TDD-BPR
T2 - The topic diversity discovering on Bayesian personalized ranking for personalized recommender system
AU - Wang, Chi Shiang
AU - Chen, Bo Syun
AU - Chiang, Jung Hsien
N1 - Funding Information:
The authors express their sincere gratitude to the Ministry of Science and Technology (MOST) for their support (grant number MOST 109-2634-F-006-006). The authors would also like to acknowledge the efforts of Chong-Yao Mo, a Master’s student, who was involved in the experiment and model testing. Moreover, the authors especially thank to National Center for High-performance Computing (NCHC) for providing computational and storage resources.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/6/21
Y1 - 2021/6/21
N2 - The main objective of the recommender system is to determine user behavior or preference and suggest the items of interest to the user. We notice that topic information is one of the significant factors that enables a user to choose the next items. To mimic this behavior, we design a topic diversity discovering (TDD) model to capture user preferences on topics. Unlike the previous studies applied the topic information to recommender systems, which only considered the item topics. In this paper, we not only focus on the item topic information but also consider the user topic information on the recommender system. We develop a TDD model to learn the topic information from the item, and we then utilize the transfer learning to obtain the user topic information. We then integrate the TDD model with the Bayesian personalized ranking (BPR) model to build a novel TDD-BPR recommender system. We experiment on three well-known three datasets MovieLens-1 M, Pinterest, and TMDB dataset, which achieved 71.8%, 91.2%, and 96.1% in the Hit Ratio@10. Empirical evidence showed that TDD-BPR discover the potential topic information to understand the user's preferences and get better recommendation performance.
AB - The main objective of the recommender system is to determine user behavior or preference and suggest the items of interest to the user. We notice that topic information is one of the significant factors that enables a user to choose the next items. To mimic this behavior, we design a topic diversity discovering (TDD) model to capture user preferences on topics. Unlike the previous studies applied the topic information to recommender systems, which only considered the item topics. In this paper, we not only focus on the item topic information but also consider the user topic information on the recommender system. We develop a TDD model to learn the topic information from the item, and we then utilize the transfer learning to obtain the user topic information. We then integrate the TDD model with the Bayesian personalized ranking (BPR) model to build a novel TDD-BPR recommender system. We experiment on three well-known three datasets MovieLens-1 M, Pinterest, and TMDB dataset, which achieved 71.8%, 91.2%, and 96.1% in the Hit Ratio@10. Empirical evidence showed that TDD-BPR discover the potential topic information to understand the user's preferences and get better recommendation performance.
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U2 - 10.1016/j.neucom.2021.02.016
DO - 10.1016/j.neucom.2021.02.016
M3 - Article
AN - SCOPUS:85102453029
SN - 0925-2312
VL - 441
SP - 202
EP - 213
JO - Neurocomputing
JF - Neurocomputing
ER -