Combining association-rule-guided sequence augmentation with listwise contrastive learning for session-based recommendation

Abstract

Sequence augmentation based contrastive learning (SACL) plays a critical role in user behavior modeling towards sequential recommendation tasks. However, SACL cannot work well in the scenario of session-based recommendation (SBR), where the anonymous user behavior sequences (known as sessions) are very short (e.g., with no more than 5 interactions), making most augmentation techniques ineffective. In this paper, we propose a novel method named LCAA (Listwise Contrastive learning with Association-rule-based sequence Augmentation), which lengthens the current session with association rules to create an augmented session, and then leverages a corresponding listwise contrastive loss to maximize the agreement of two recommendation lists generated from the original session and its augmentation. Remarkably, LCAA is a model-agnostic method that can be easily plugged into a wide range of existing SBR models towards better accuracy. To evaluate the effectiveness of LCAA, we implement it with five SBR models utilizing various deep learning techniques (NARM, STAMP, SRGNN, CORE, and ATTMIX) and then compare the performance of each SBR baseline with its LCAA-modified version. Extensive experiments on three datasets (Diginetica, Nowplaying, and Tmall) demonstrate that LCAA yields the average improvement of around 5% on the complete testing sets and around 3% on the short session testing sets in terms of HR and MRR metrics. The code is publicly available.¹