Inf. Sci. | 2021
Fusion of latent categorical prediction and sequential prediction for session-based recommendation
Abstract
Abstract Session-based recommendation is to predict the next item for an anonymous item sequence. Most of recent neural models have focused on how to learn sessions’ sequential representations based on the assumption that items can be projected into a single latent embedding space to describe their latent attributes. In this paper, we argue that an item can also be described by some latent categorical abstractions. To examine our argument, we first mine items’ latent categorical distributions via random walk on an item graph constructed from sessions. We design a new neural model which consists of two prediction modules: One is to learn a session’s latent categorical representation; The other is to learn a session’s sequential representation. Each module independently makes a next item prediction, and their predictions are fused as the final recommendation result. Experiments on three public datasets validate that our model achieves performance improvements over the recent state-of-the-art algorithms.