Local Representative-Based Matrix Factorization for Cold-Start Recommendation

Cold-start recommendation is one of the most challenging problems in recommender systems. An important approach to cold-start recommendation is to conduct an interview for new users, called the interview-based approach. Among the interview-based methods, Representative-Based Matrix Factorization (RBMF) [24] provides an effective solution with appealing merits: it represents users over selected representative items, which makes the recommendations highly intuitive and interpretable. However, RBMF only utilizes a global set of representative items to model all users. Such a representation is somehow too strict and may not be flexible enough to capture varying users' interests. To address this problem, we propose a novel interview-based model to dynamically create meaningful user groups using decision trees and then select local representative items for different groups. A two-round interview is performed for a new user. In the first round, l(1) global questions are issued for group division, while in the second round, l(2) local-group-specific questions are given to derive local representation. We collect the feedback on the (l(1) + l(2)) items to learn the user representations. By putting these steps together, we develop a joint optimization model, named local representative-based matrix factorization, for new user recommendations. Extensive experiments on three public datasets have demonstrated the effectiveness of the proposed model compared with several competitive baselines.