Enhancing Session-Based Recommendation with Global Context Information and Knowledge Graph

Abstract

Predicting a user’s next click by utilizing a short anonymous behavior is a challenging problem in the real-life session-based recommendation (SBR). Most existing methods usually learn the users’ preference from current session. However, they seldom consider global context information or knowledge graph and failed to distill high-quality item from similar sessions. In this work, we combine Global Context information with Knowledge Graph, and develop a new framework to enhance session-based recommendation (GCKG). Technically, we model a global knowledge graph, exploiting a knowledge aware attention mechanism for better learning item embeddings. Then, we leverage an attention network and a gated recurrent unit to learn session representations. Furthermore, session representations are augmented simultaneously through constructing a similar session referral circle. Comprehensive experiments demonstrate that GCKG significantly outperforms the state-of-the-art methods of existing SBR.