BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model
This addresses the issue of bias in recommendation systems for users who prefer niche items, representing a strong specific gain in a domain-specific context.
The paper tackled the problem of popularity bias in sequential recommendation models by developing BiCoRec, which adaptively accommodates users' changing preferences for popular and niche items, achieving a 26.00% average improvement in NDCG@10 for users preferring niche items over state-of-the-art baselines.
Sequential recommendation models aim to learn from users evolving preferences. However, current state-of-the-art models suffer from an inherent popularity bias. This study developed a novel framework, BiCoRec, that adaptively accommodates users changing preferences for popular and niche items. Our approach leverages a co-attention mechanism to obtain a popularity-weighted user sequence representation, facilitating more accurate predictions. We then present a new training scheme that learns from future preferences using a consistency loss function. BiCoRec aimed to improve the recommendation performance of users who preferred niche items. For these users, BiCoRec achieves a 26.00% average improvement in NDCG@10 over state-of-the-art baselines. When ranking the relevant item against the entire collection, BiCoRec achieves NDCG@10 scores of 0.0102, 0.0047, 0.0021, and 0.0005 for the Movies, Fashion, Games and Music datasets.