User Feedback-Based Counterfactual Data Augmentation for Sequential Recommendation

Haiyang Wang, Yan Chu, Hui Ning, Zhengkui Wang, Wen Shan

Abstract: The sequential recommendation is a prominent task that aims to provide accurate recommendations by leveraging users’ historical behavior. However, the challenge of data sparsity poses a significant obstacle in achieving effective sequential recommendations. In this paper, we propose a User Feedback-based Counterfactual data augmentation method for Sequential Recommendation (UFC4-SRec) to address this challenge. Our approach focuses on expanding the dataset for sequential recommendation tasks by employing counterfactual inference techniques. The UFC4-SRec method consists of two main components: a counterfactual generator and a recommender. The counterfactual generator is responsible for generating counterfactual examples based on users’ feedback. By incorporating users’ preferences for items, the generated counterfactual data are designed to be closer to their actual preferences. On the other hand, the recommender employs various sequential recommendation models to provide recommendation results. To guide the counterfactual generator, the recommender imitates reinforcement learning by computing reward values based on the quality of the generated data. To evaluate the effectiveness of our method, we conduct experiments on three real-world datasets. The experimental results demonstrate that our UFC4-SRec approach significantly improves the performance of sequential recommendation tasks. Moreover, it effectively addresses the data sparsity problem commonly encountered in sequential recommendations.