Improving Cold-Start Recommendation via Multi-prior Meta-learning

Zhengyu Chen, Donglin Wang, Shiqian Yin

Abstract: Optimization-based meta-learning has been applied in cold-start recommendations, where a good initialization of meta learner is obtained from past experiences and then reused for fast adaptation to new tasks. However, when dealing with various users with diverse preferences, meta-learning with a single prior might fail in cold-start recommendations due to its insufficient capability for adaptation. To address this problem, a multi-prior meta-learning (MPML) approach is proposed in this paper and applied in cold-start recommendations. More concretely, we integrate a novel accuracy-based task clustering scheme with double gradient to learn multiple priors. Experiments demonstrate the effectiveness of MPML.