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Multi-criteria Recommendations through Preference Learning

In today's internet era, recommender system (RS) addresses information overload problem, which is common in many information driven domains. RS helps users chose a set of appropriate options from a plethora of options. Traditional single rating recommender systems have been playing a vital role over the decades in various domains. However, it is limited in a sense of providing user's accurate preferences about an item or services to the recommendation engine. The single rating recommender systems receive a single rating about an item, due to which these systems are inadequate to understand the reasons behind users' choice of items. On the other hand, multi-criteria rating systems allow the users to share more information about user's interest/ disinterest through multiple criteria of an item. Therefore, the multi-criteria recommender engine gets more information from the users and provides relevant recommendations to the users. In this paper, we propose a novel technique to learn and rank users' preferences over different criteria. Dominant criteria of each item are also learnt and ranked in the proposed technique. The obtained ranks are exploited to predict the overall rating by adapting the traditional user-based and item-based collaborative filtering techniques. We conducted experiments on two real world datasets (TripAdvisor and Yahoo! Movies) and our approach outperforms the traditional single rating systems and existing approaches on multi-criteria recommender systems.