School of Computer Science and Applied Mathematics (ETDs)

Browse

Now showing 1 - 1 of 1

BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model
(University of the Witwatersrand, Johannesburg, 2024-09) Muthivhi, Mufhumudzi; van Zyl, Terence; Bau, Hairong
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. The analysis of the experimental results shows that our approach is 7% more capable of uncovering the most relevant items.