A Personalized Recommendation System for NetEase Dating Site

With the popularity of internet, more and more people try to find friends or dating partners on online dating web sites. Recommending appropriate partners from a large amount of candidates becomes an interesting and challenging problem in the field of recommendation system. Various types of recommendation techniques (e.g., content based recommendation, collaborative filtering and association rule mining) have be proposed to tackle this problem. However most of them ignore the personalization concerns that they (1) mainly consider the hot users or frequent items, (2) cover only a portion of users especially ignoring the long tails, (3) and cannot deal with the cold start problem properly. In this paper, we present a regression based hybrid recommendation system that makes use of matching degree, fancy degree, activity, sincerity, popularity and enthusiasm, to recommend appropriate partners. The experimental evaluation of our recommendation system on a real dating web site shows our strategy is more effective and efficient than its previous version which follows the principle of giving higher priority to the recent active users.