ADSE: Adversarial Debiasing Framework Based on Sinusoidal Embedding for Sequential Recommendation

Qifeng Bai, Nankai Lin, Junheng He, Zhijin Chen, Dong Zhou, Aimin Yang

Abstract: Sequential recommendation plays a key role in recommender systems, where the goal is to predict a user’s future points of interest by analyzing his or her historical interactions. This process not only requires the system to be able to accurately identify and recommend items that are likely to be of interest to the user but also ensures that all items receive equal exposure to prevent over-concentration or marginalization of items due to algorithmic bias. To address these challenges, in this paper, we propose a novel Adversarial Debiasing framework based on Sinusoidal Embedding for sequential recommendation, ADSE. This framework employs sinusoidal position embeddings to extract positional information between sequences more precisely and utilizes a dropout strategy to optimize the handling of cold-start sequences, aiming to resolve the cold-start issue while maintaining the semantics of the original sequences. Additionally, adversarial training was incorporated to reduce implicit bias due to assuming interactions in the calculation of exposure.