Hybrid Techniques to Address Cold Start Problems for People to People Recommendation in Social Networks

Abstract

We investigate several hybrid approaches to suggesting matches in people to people social recommender systems, paying particular attention to cold start problems, problems of generating recommendations for new users or users without successful interactions. In previous work we showed that interaction-based collaborative filtering (IBCF) works well in this domain, although this approach cannot generate recommendations for new users, whereas a system based on rules constructed using subgroup interaction patterns can generate recommendations for new users, but does not perform as effectively for existing users. We propose three hybrid recommenders based on user similarity and two content-boosted recommenders used in conjunction with interaction-based collaborative filtering, and show experimentally that the best hybrid and content-boosted recommenders improve on the IBCF method (when considering user success rates) yet cover almost the whole user base, including new and previously unsuccessful users, thus addressing cold start problems in this domain. The best content-boosted method improves user success rates more than the best hybrid method over various “cold start” subgroups, but is less computationally efficient overall.