stochastic matching and collaborative filtering to recommend people to people
The bias towards popular items is not necessarily an undesired outcome of recommender algorithms since a large amount of revenue on e-commerce websites is drawn from these popular items. On the other hand, in domains such as online dating and employment websites, where users and items of the recommendation are both people, a strong bias towards popular users may cause these users to feel overwhelmed and unpopular users to feel neglected. In this paper, we use collaborative filtering (CF) to generate recommendations for all users, and by using stochastic matching we select a number of reciprocal recommendations for each user that maximizes the matches among all users. In this way, all users, regardless of their popularity, will receive the same number of recommendations the number of times they will be recommended to others. This study is the first to apply a stochastic matching solution to balance the number of recommendations given to users in a CF setting. Using historical data, we demonstrate that the proposed recommender improves the chance of finding a successful relationship in comparison to CF recommendations.
stochastic matching people recommender recommender systems