Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction

TL;DR

This paper introduces MGNN-SPred, a novel graph neural network model that leverages multi-relational item graphs and auxiliary behaviors to improve session-based target behavior prediction, outperforming existing methods.

Contribution

The paper proposes a multi-relational graph neural network that globally encodes item relations across behaviors and integrates auxiliary data for enhanced prediction accuracy.

Findings

MGNN-SPred outperforms state-of-the-art methods on real-world datasets.

Leveraging auxiliary behaviors improves prediction for sparse target actions.

Global item-to-item relation modeling enhances understanding of user preferences.

Abstract

Session-based target behavior prediction aims to predict the next item to be interacted with specific behavior types (e.g., clicking). Although existing methods for session-based behavior prediction leverage powerful representation learning approaches to encode items' sequential relevance in a low-dimensional space, they suffer from several limitations. Firstly, they focus on only utilizing the same type of user behavior for prediction, but ignore the potential of taking other behavior data as auxiliary information. This is particularly crucial when the target behavior is sparse but important (e.g., buying or sharing an item). Secondly, item-to-item relations are modeled separately and locally in one behavior sequence, and they lack a principled way to globally encode these relations more effectively. To overcome these limitations, we propose a novel Multi-relational Graph Neural Network model for Session-based target behavior Prediction, namely MGNN-SPred for short. Specifically, we build a Multi-Relational Item Graph (MRIG) based on all behavior sequences from all sessions, involving target and auxiliary behavior types. Based on MRIG, MGNN-SPred learns global item-to-item relations and further obtains user preferences w.r.t. current target and auxiliary behavior sequences, respectively. In the end, MGNN-SPred leverages a gating mechanism to adaptively fuse user representations for predicting next item interacted with target behavior. The extensive experiments on two real-world datasets demonstrate the superiority of MGNN-SPred by comparing with state-of-the-art session-based prediction methods, validating the benefits of leveraging auxiliary behavior and learning item-to-item relations over MRIG.