Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Collaborative filtering via gaussian probabilistic latent semantic analysis
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Personalized social search based on the user's social network
Proceedings of the 18th ACM conference on Information and knowledge management
Content-based recommendation systems
The adaptive web
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Interaction-based collaborative filtering methods for recommendation in online dating
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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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Social recommendation, predicting people who match other people for friendship or as potential partners in life or work, has recently become an important task in many social networking sites. Traditional content-based and collaborative filtering methods are not sufficient for people-to-people recommendation because a good match depends on the preferences of both sides. We proposed a framework for social recommendation and develop a representation for classification of interactions in online dating applications that combines content from user profiles plus interaction behaviours. We show that a standard algorithm can be used to learn a model to predict successful interactions. We also use a method to search for the best model by minimising a cost based on predicted precision and recall. To use the model in real world applications to make recommendations, we generate candidate pairs using the selected models and ranked them using a novel probabilistic ranking function to score the chance of success. Our model-based social recommender system is evaluated on historical data from a large commercial social networking site and shows improvements in success rates over both interactions with no recommendations and those with recommendations generated by standard collaborative filtering.