SAM: A Self-adaptive Attention Module for Context-Aware Recommendation System

TL;DR

This paper introduces SAM, a self-adaptive attention module that enhances recommendation systems by effectively capturing contextual information and adjusting for selection bias, leading to improved performance on real-world datasets.

Contribution

The paper presents a novel self-adaptive attention module that can be integrated into recommendation systems to better handle textual context and mitigate selection bias.

Findings

SAM improves recommendation accuracy on three datasets.

State-of-the-art models with SAM outperform baseline models.

SAM effectively captures contextual information and adjusts for bias.

Abstract

Recently, textual information has been proved to play a positive role in recommendation systems. However, most of the existing methods only focus on representation learning of textual information in ratings, while potential selection bias induced by the textual information is ignored. In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation. This module can be embedded into recommendation systems that contain learning components of contextual information. Experimental results on three real-world datasets demonstrate the effectiveness of our proposal, and the state-of-the-art models with SAM significantly outperform the original ones.