BiCoRec: Bias-Mitigated Context-Aware Sequential Recommendation Model - Muthivhi, Mufhumudzi (University of the Witwatersrand, Johannesburg, 2024)
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.
A dissertation submitted in fulfilment of the requirements for the degree of Master of Science, to the Faculty of Science, School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, 2024.
Keywords: Machine learning; Transformers; Recommendation; Popularity bias; Multi-modality sequential recommendation; Deep learning; Co-attention; Context awarnes; UCTD; SDG-9: Industry, innovation and infrastructure; SDG-4: Quality education