Ensuring accuracy and fairness: a de-biasing framework for sequential recommendation

Qifeng Bai, Nankai Lin, Meiyu Zeng, Guanqiu Qin, Dong Zhou, Aimin Yang

Abstract: In recommendation systems, the use of a user’s interaction history as sequential information can greatly improve performance. However, user interactions with preferred items are not only sparse, but also dynamically change over time, making it difficult to learn high-quality representations of user interaction item sequences. Furthermore, recommendation systems often suffer from popularity bias; namely, popular items are disproportionately recommended. To address these problems, this paper proposes a dynamic de-biasing framework based on adversarial and contrastive learning for sequential recommendations called DACRec. Firstly, to capture the changing of user preferences and enhance the temporal representation, we introduce a temporal information attention mechanism. Secondly, DACRec utilizes contrastive learning to enable efficient encoding of user representations by capturing underlying user patterns. Finally, we utilize adversarial learning to mitigate the negative impact of popularity bias on recommendation results, achieving a balance between sequential recommendation accuracy and fairness. To evaluate the effectiveness of the DACRec framework, we conduct experiments on two widely used sequential recommendation datasets Steam and Ml-1 M. The results show that there is an improvement in the three evaluation metrics of NDCG, recall, and ARP compared with multiple baselines. Particularly, the recommendation performance is better than the baseline on all-negative sampling sparse dataset, which proves that the DACRec framework is able to utilize the popularity well to improve the recommendation performance. The code is available at https://github.com/baigeshi/DACRec.