Cross-Technique Mediation of User Models
Nowadays, personalization is considered a powerful approach for designing more precise and easy to use information search and recommendation tools. Since the quality of the personalization provided depends on the accuracy of the user models (UMs) managed by the system, it would be beneficial enriching these models through mediating partial UMs, built by other services. This paper proposes a cross-technique mediation of the UMs from collaborative to content-based services. According to this approach, content-based recommendations are built for the target users having no content-based user model, knowing his collaborative-based user model only. Experimental evaluation conducted in the domain of movies, shows that for small UMs, the personalization provided using the mediated content-based UMs outperforms the personalization provided using the original collaborative UMs.
KeywordsRecommender System User Model Prediction Rate Collaborative Filter Confidence Threshold
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