Affiliations: [a] Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, China | [b] School of Computer and lnformation Technology, Beijing Jiaotong University, Beijing, China | [c] College of Engineering, City University of Hong Kong, Hong Kong, China
Correspondence:
[*]
Corresponding author: Jidong Yuan, Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, China. E-mail: yuanjd@bjtu.edu.cn.
Abstract: Sequential recommendation aims to predict users’ future activities based on their historical interaction sequences. Various neural network architectures, such as Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), and self-attention mechanisms, have been employed in the tasks, exploring multiple aspects of user preferences, including general interests, short-term interests, long-term interests, and item co-occurrence patterns. Despite achieving good performance, there are still limitations in capturing complex user preferences. Specifically, the current structures of RNN, GNN, etc., only capture item-level transition relations while neglecting attribute-level transition relations. Additionally, the explicit item relations are studied using item co-occurrence modules, but they cannot capture the implicit item-item relations. To address these issues, we propose a knowledge-augmented Gated Recurrent Unit (GRU) to improve the short-term user interest module and adopt a collaborative item aggregation method to enhance the item co-occurrence module. Additionally, our long-term interest module utilizes a bitwise gating mechanism to select historical item features significant to users’ current preferences. We extensively evaluate our model on three real-world datasets alongside competitive methods, demonstrating its effectiveness in top K sequential recommendation.
