ScholarWorks@중앙대학교

초록

The current trend in recommendation services is prioritizing personalization to ensure accurate recommendations. This study aims to enhance the user-based collaborative filtering algorithm for cross-domain recommendations by exploiting the similarity in user cognition across multiple domains. The research suggests three steps: (i) gathering user feedback from various domains to represent their cognition, (ii) constructing a user cognition-based collaborative filtering model for multi-domain recommendations, and (iii) generating recommendations in the target domain. The experimental results demonstrate that the proposed model outperforms all baseline methods. In particular, the proposed method is better than the baselines, approximately 12% up to 16%, regarding mean average precision and normalized discounted cumulative gain metrics.

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