Debiased Sequential Recommendation by Separating Long-Term and Short-Term Interests
Zhenyu Yang, Wenyue Hu, Tong Zhang, Yuhu Cheng, Xuesong Wang · IEEE Transactions on Computational Social Systems · 2025
A significant problem in sequential recommendation (SR) is the over-recommendation of popular items, leading to popularity bias, as users often follow these items due to conformity. Existing methods measure users’ conformity factors to reduce the impact of popularity bias. However, these methods do not consider the differences in users’ conformity behavior in the long-term and short-term. To address this, we propose LSDRec, a novel debiased SR method structured around three key tasks: degree-centrality conformity awareness, dual-scale interest encoding, and adaptive conformity information fusing. The degree-centrality conformity awareness task constructs a multiuser interaction graph, employs a graph convolutional network (GCN) to obtain global user conformity representations, and uses the degree centrality algorithm to compute users’ long-term and short-term conformity factors. The dual-scale interest encoding task models users’ long-term and short-term interests separately, obtaining corresponding interest representations and further enhancing them through the adaptive conformity information fusing task. The adaptive conformity information fusing task contrasts global conformity representations with long-term and short-term interest representations, adaptively integrating conformity factors and dynamically adjusting the degree of conformity information transfer. Together, these three tasks effectively mitigate popularity bias and improve the accuracy of user interest modeling. Our extensive evaluations of four diverse datasets demonstrate LSDRec's superior performance over current state-of-the-art methods.