Automatic Meta-Path Discovery for Effective Graph-Based Recommendation

TL;DR

This paper introduces a reinforcement learning framework for automatically discovering effective meta-paths in heterogeneous information networks to enhance graph-based recommendation systems, outperforming manual methods.

Contribution

It proposes RMS, a reinforcement learning-based method for automatic meta-path selection, and introduces RMS-HRec, a new meta-path-based recommender with attention mechanisms.

Findings

Meta-paths discovered by RMS improve recommendation quality.

RMS-HRec outperforms state-of-the-art recommenders by 7% in hit ratio.

The framework is compatible with existing meta-path-based recommenders.

Abstract

Heterogeneous Information Networks (HINs) are labeled graphs that depict relationships among different types of entities (e.g., users, movies and directors). For HINs, meta-path-based recommenders (MPRs) utilize meta-paths (i.e., abstract paths consisting of node and link types) to predict user preference, and have attracted a lot of attention due to their explainability and performance. We observe that the performance of MPRs is highly sensitive to the meta-paths they use, but existing works manually select the meta-paths from many possible ones. Thus, to discover effective meta-paths automatically, we propose the Reinforcement learning-based Meta-path Selection (RMS) framework. Specifically, we define a vector encoding for meta-paths and design a policy network to extend meta-paths. The policy network is trained based on the results of downstream recommendation tasks and an early stopping approximation strategy is proposed to speed up training. RMS is a general model, and it can work with all existing MPRs. We also propose a new MPR called RMS-HRec, which uses an attention mechanism to aggregate information from the meta-paths. We conduct extensive experiments on real datasets. Compared with the manually selected meta-paths, the meta-paths identified by RMS consistently improve recommendation quality. Moreover, RMS-HRec outperforms state-of-the-art recommender systems by an average of 7% in hit ratio. The codes and datasets are available on https://github.com/Stevenn9981/RMS-HRec.