Multifaceted Reciprocal Recommendations for Online Dating
Tulika Kumari, Ravish Sharma, Punam Bedi · 2021
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.