A people-to-people content-based reciprocal recommender using hidden markov models

Abstract

Users of online social networks such as dating websites often need help to find successful matches. People-to-people recommender systems can be used in social networks to help users find better matches, which requires solving the problem of reciprocal recommendation. However, most existing reciprocal recommenders use either profile similarity or interaction similarity to recommend new matches, without considering temporal features. In this paper we introduce a method for temporal reciprocal recommender systems using Hidden Markov Models to generate recommendations. Instead of summarising the whole historical data in one past state, we propose a model that formalises historical data on interactions as a series of successive states changing over time and then tries to find the recommended next state. We have implemented this new approach and the results of testing on industrial-scale data from a real dating website show a noticeable improvement over the previous best-performing recommenders.