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.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence