Divide and transfer: Understanding latent factors for recommendation tasks

Abstract

Traditionally, latent factor models have been the most successful techniques to build recommendation systems. While the key is to capture the user interests effectively, most research is focused on learning latent factors under cold-start and data sparsity situations. Our work brings a complementary approach to the previous studies showing that understanding the semantic aspects of latent factors could give a hint on how to transfer useful knowledge from auxiliary domain(s) to the target domain. In this work, we propose a collaborative filtering technique that can effectively utilize the user preferences and content information. In our approach, we follow a divide and transfer strategy that could derive semantically meaningful latent factors and utilize only the appropriate components for recommendations. We demonstrate the effectiveness of our approach due to improved latent feature space in both single and cross-domain tasks. Further, we also show its robustness by performing extensive experiments under cold-start and data sparsity contexts.