Context-Aware Multi-Criteria Recommender Systems Using Variable Selection Networks
Résumé
Conventional recommender systems, which rely on a single criterion such as overall rating, often fail to capture the complexity of user preferences and the influence of contextual information. Context-aware multi-criteria recommender systems address these limitations by incorporating multiple dimensions of user preferences, item attributes, and contextual factors, leading to more accurate and relevant recommendations. This paper presents a context-aware multi-criteria recommender system using variable selection networks. Our approach dynamically selects the most relevant features from a variety of inputs, including user profiles, item characteristics, multiple criteria, and contextual factors, to enhance personalization. By leveraging deep learning-based variable selection networks, our model significantly improves recommendation accuracy and interpretability, outperforming several baseline models in experimental evaluations. This advancement underscores the importance of integrating both multi-criteria and context-aware methodologies in modern recommender systems.
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