[1Q3-J-2] Machine learning: structural modeling

[1Q3-J-2-05] A study on recommender system considering diversity in recommendation items based on LDA

〇Zhiying Zhang¹, Taiju Hosaka¹, Haruka Yamashita², Masayuki Goto¹ (1. Waseda University, 2. Sophia University)

Keywords:Diversity, Collaborative Filtering, Latent Dirichlet Allocation, Recommender System

With the development of information technology, a huge amount of users' action history data has been accumulated on web sites.On such background, recommender system making use of these rich data has become important tool for searching contents or products. Diversifying the recommendation lists in recommender systems could potentially satisfy users' needs. In a previous research, the diversity is raised by the topic diversification method using Latent Dirichlet Allocation, but since the items belonging to the same topic are not diversified, there is a high possibility that they are similar. Therefore, this reserach proposes a recommendation method considering item diversification. Experimental results on MovieLens datasets demonstrate that our approach keeps accuracy produces more diversified results.