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.

该研究提出了一种利用边缘信息来提高推荐系统性能的新框架，并通过实验证明，该推荐系统在准确率和收敛速度上优于其他最先进的模型。此外，该研究还探讨了关注机制的新型正则化损失，并提出了进一步改进的一些见解。

使推荐系统更具专业知识: 一个整合辅助信息的框架