BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model

TL;DR

BiCoRec is a novel recommendation model that mitigates popularity bias by adaptively capturing user preferences for both popular and niche items, leading to significant performance improvements.

Contribution

The paper introduces BiCoRec, a framework with a co-attention mechanism and a new training scheme to better model evolving user preferences and reduce popularity bias in recommendations.

Findings

26% average improvement in NDCG@10 for niche users

Achieves high NDCG@10 scores across multiple datasets

Effectively mitigates popularity bias in sequential recommendation

Abstract

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.