Inter-sequence Enhanced Framework for Personalized Sequential Recommendation

TL;DR

This paper introduces ISSR, a novel framework that models both inter-sequence and intra-sequence item correlations to improve personalized sequential recommendation accuracy.

Contribution

The paper proposes an inter-sequence enhanced framework using graph neural networks and combined encoders, addressing the neglect of inter-sequence correlations in prior methods.

Findings

ISSR outperforms state-of-the-art methods on three real-world datasets.

The inter-sequence correlation encoder significantly improves recommendation performance.

Modeling both inter- and intra-sequence correlations enhances personalization accuracy.

Abstract

Modeling the sequential correlation of users' historical interactions is essential in sequential recommendation. However, the majority of the approaches mainly focus on modeling the \emph{intra-sequence} item correlation within each individual sequence but neglect the \emph{inter-sequence} item correlation across different user interaction sequences. Though several studies have been aware of this issue, their method is either simple or implicit. To make better use of such information, we propose an inter-sequence enhanced framework for the Sequential Recommendation (ISSR). In ISSR, both inter-sequence and intra-sequence item correlation are considered. Firstly, we equip graph neural networks in the inter-sequence correlation encoder to capture the high-order item correlation from the user-item bipartite graph and the item-item graph. Then, based on the inter-sequence correlation encoder, we build GRU network and attention network in the intra-sequence correlation encoder to model the item sequential correlation within each individual sequence and temporal dynamics for predicting users' preferences over candidate items. Additionally, we conduct extensive experiments on three real-world datasets. The experimental results demonstrate the superiority of ISSR over many state-of-the-art methods and the effectiveness of the inter-sequence correlation encoder.