BayesSentiRS: Bayesian sentiment analysis for addressing cold start and sparsity in ranking-based recommender systems

Abstract

Recommendation systems are widely used to filter massive information. However, they often face the challenges of cold start and sparsity problems, limiting their effectiveness. Bayesian Personalized Ranking (BPR), which focuses on predicting the relative order of user items, has been conventionally proposed to address these challenges for ranking-based recommendation systems. However, conventional BPR approaches rely solely on implicit feedback and lack semantic and visual sentiment information. In this study, we propose a novel multi-modal BPR method that incorporates both semantic and visual sentiment to capture more nuanced user preferences and provide more accurate recommendations. Experimental evaluations show that the performance of our proposed method across seven metrics outperforms conventional BPR in both the review-rich and image-rich scenarios, indicating the potential and significance of considering sentiment for improving the performance of BPR recommendation systems.