Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
Pairwise preference regression for cold-start recommendation
Proceedings of the third ACM conference on Recommender systems
RECON: a reciprocal recommender for online dating
Proceedings of the fourth ACM conference on Recommender systems
People recommendation based on aggregated bidirectional intentions in social network site
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Interaction-based collaborative filtering methods for recommendation in online dating
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
CCR: a content-collaborative reciprocal recommender for online dating
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
People-to-People recommendation using multiple compatible subgroups
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
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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.