Making Recommender Systems More Knowledgeable: A Framework to Incorporate Side Information
This work addresses the challenge of improving recommendation accuracy and efficiency for users in session-based systems, though it is incremental as it builds on existing models with side information.
The paper tackles the problem of session-based recommender systems by incorporating item-specific side information to enhance performance, resulting in outperforming state-of-the-art models by a considerable margin and achieving faster convergence.
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.