Few-Shot Learning for Cold-Start Recommendation

Mingming Li, Songlin Hu, Fuqing Zhu, Qiannan Zhu

Abstract: Cold-start is a significant problem in recommender systems. Recently, with the development of few-shot learning and meta-learning techniques, many researchers have devoted themselves to adopting meta-learning into recommendation as the natural scenario of few-shots. Nevertheless, we argue that recent work has a huge gap between few-shot learning and recommendations. In particular, users are locally dependent, not globally independent in recommendation. Therefore, it is necessary to formulate the local relationships between users. To accomplish this, we present a novel Few-shot learning method for Cold-Start (FCS) recommendation that consists of three hierarchical structures. More concretely, this first hierarchy is the global-meta parameters for learning the global information of all users; the second hierarchy is the local-meta parameters whose goal is to learn the adaptive cluster of local users; the third hierarchy is the specific parameters of the target user. Both the global and local information are formulated, addressing the new user’s problem in accordance with the few-shot records rapidly. Experimental results on two public real-world datasets show that the FCS method could produce stable improvements compared with the state-of-the-art.