抄録
Offer Organization: Japan Society for the Promotion of Science, System Name: Grants-in-Aid for Scientific Research, Category: Grant-in-Aid for Early-Career Scientists, Fund Type: competitive_research_funding, Overall Grant Amount: - (direct: 3200000, indirect: 960000)
This study develops model-based collaborative filtering techniques with explanations for recommended items based on linear regression. The proposed model treats users and items simultaneously in a single linear regression equation, and the reason for recommendation can be explained by presenting regression coefficients. We evaluated that the proposed model has achieved 25 times faster and the same level of recommendation accuracy, compared with Factorization Machines. In addition, a linear regression model with interaction between users and items is proposed. According to the results, we validated that the linear regression models have potentials for fast and accurate enough to make recommendations.