Do you know?: recommending people to invite into your social network
Proceedings of the 14th international conference on Intelligent user interfaces
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Affiliation recommendation using auxiliary networks
Proceedings of the fourth ACM conference on Recommender systems
A matrix factorization technique with trust propagation for recommendation in social networks
Proceedings of the fourth ACM conference on Recommender systems
Transitive node similarity for link prediction in social networks with positive and negative links
Proceedings of the fourth ACM conference on Recommender systems
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
A generalized stochastic block model for recommendation in social rating networks
Proceedings of the fifth ACM conference on Recommender systems
Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation
Proceedings of the fifth ACM conference on Recommender systems
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Up to now, more and more social media sites have started to allow their users to build the social relationships. Take the Last.fm for example (which is a popular music-sharing site), users can not only add each other as friends, but also join interest groups that include people with common tastes. Therefore, in this environment, users might be interested in not only receiving item recommendations, but also friend recommendations whom they might consider putting in the contact list, and group recommendations that they may consider joining in. To support such needs, in this paper, we propose a generalized framework that provides three different types of recommendation in a single system: recommending items, recommending groups and recommending friends. For each type of recommendation, we in depth investigated the algorithm impact of fusing other two information resources (e.g., user-item preferences and friendship to be fused for recommending groups), along with their combined effect. The experiment reveals the ideal fusion mechanism for this multi-output recommender, and validates the benefit of factorization model for fusing bipartite data (such as membership and user-item preferences) and the benefit of regularization model for fusing one mode data (such as friendship). Moreover, the positive effect of integrating similarity measure into the regularization model is identified via the experiment.