EXPLOITING SOCIAL TAGS TO OVERCOME COLD START RECOMMENDATION PROBLEM

Abstract

The practice and method of collaboratively creating and managing tags to annotate and categorize content has resulted in the creation of folksonomy. Folksonomies provide new opportunities and challenges in the field of recommender systems. Despite the considerable amount of researches done in the context of recommender systems, the specific problem of integrating tags into standard recommender system algorithms is less explored than the problem of recommending tags. Collaborative filtering is one of the popular approaches for providing recommendation. However, despite the popularity of collaborative filtering, to some extent, it could not recognize the preferences of users in cold-start scenarios, where insufficient preferences are associated to certain users or items, which leads to degraded recommendation quality. This study presents a collaborative filtering approach based on the expansion of users’ tags. In this case, semantics between tags can be unveiled which subsequently resulted in the identification of semantically similar users. Experiment on real-life dataset shows that our approach outperforms the state-of-the-art tag-aware collaborative filtering approaches in terms of recommendation quality particularly in the cold-start situation.