Multifaceted Reciprocal Recommendations for Online Dating

Recommender Systems (RS) aim to filter relevant items from the huge pool of information available to users and assist users by predicting their future preferences. In traditional item-to-user based RS, items are recommended to user and preferences of the user are considered for generating recommendations. Reciprocal Recommender Systems (RRS) are people-to-people systems which recommend users to each other. Thus, the preferences of both the users should be satisfied in order to produce successful recommendations. In RRS, two unilateral user-to-user preference scores are aggregated to generate reciprocal recommendations that should be accepted by both the users involved. In this paper, we propose a reciprocal recommendation algorithm that computes unilateral preference scores based on multiple aspects including multi-criteria preferences of a user, popularity-awareness, demographic information and availability of users. Experimental study conducted with speed-dating experiment data set demonstrate the effectiveness of the proposed approach.