SR-HetGNN:Session-based Recommendation with Heterogeneous Graph Neural Network

TL;DR

SR-HetGNN introduces a heterogeneous graph neural network approach for session-based recommendation, effectively incorporating user profiles and session dependencies to improve prediction accuracy over existing methods.

Contribution

The paper presents SR-HetGNN, a novel method that models session data with heterogeneous graphs to capture complex user-item-session relationships, enhancing recommendation performance.

Findings

Outperforms state-of-the-art session-based recommendation methods.

Effectively captures user preferences using heterogeneous graph structures.

Demonstrates superior results on Diginetica and Tmall datasets.

Abstract

The Session-Based Recommendation System aims to predict the user's next click based on their previous session sequence. The current studies generally learn user preferences according to the transitions of items in the user's session sequence. However, other effective information in the session sequence, such as user profiles, are largely ignored which may lead to the model unable to learn the user's specific preferences. In this paper, we propose SR-HetGNN, a novel session recommendation method that uses a heterogeneous graph neural network (HetGNN) to learn session embeddings and capture the specific preferences of anonymous users. Specifically, SR-HetGNN first constructs heterogeneous graphs containing various types of nodes according to the session sequence, which can capture the dependencies among items, users, and sessions. Second, HetGNN captures the complex transitions between items and learns the item embeddings containing user information. Finally, local and global session embeddings are combined with the attentional network to obtain the final session embedding, considering the influence of users' long and short-term preferences. SR-HetGNN is shown to be superior to the existing state-of-the-art session-based recommendation methods through extensive experiments over two real large datasets Diginetica and Tmall.