Making Recommender Systems More Knowledgeable: A Framework to Incorporate Side Information

TL;DR

This paper introduces a flexible framework to incorporate item-specific side information into session-based recommender systems, significantly improving their accuracy and convergence speed.

Contribution

It presents a general method for integrating side information into existing models, along with a novel loss function for regularizing attention mechanisms.

Findings

Outperforms state-of-the-art models with side information

Converges faster than baseline models

Regularization improves attention mechanism performance

Abstract

Session-based recommender systems typically focus on using only the triplet (user_id, timestamp, item_id) to make predictions of users' next actions. In this paper, we aim to utilize side information to help recommender systems catch patterns and signals otherwise undetectable. Specifically, we propose a general framework for incorporating item-specific side information into the recommender system to enhance its performance without much modification on the original model architecture. Experimental results on several models and datasets prove that with side information, our recommender system outperforms state-of-the-art models by a considerable margin and converges much faster. Additionally, we propose a new type of loss to regularize the attention mechanism used by recommender systems and evaluate its influence on model performance. Furthermore, through analysis, we put forward a few insights on potential further improvements.