The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Evaluating similarity measures: a large-scale study in the orkut social network
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Recommendations in taste related domains: collaborative filtering vs. social filtering
Proceedings of the 2007 international ACM conference on Supporting group work
A group recommendation system with consideration of interactions among group members
Expert Systems with Applications: An International Journal
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Combinational collaborative filtering for personalized community recommendation
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
FriendSensing: recommending friends using mobile phones
Proceedings of the third ACM conference on Recommender systems
Use of social network information to enhance collaborative filtering performance
Expert Systems with Applications: An International Journal
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
Recommending friends and locations based on individual location history
ACM Transactions on the Web (TWEB)
Recommendation of similar users, resources and social networks in a Social Internetworking Scenario
Information Sciences: an International Journal
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
SFViz: interest-based friends exploration and recommendation in social networks
Proceedings of the 2011 Visual Information Communication - International Symposium
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
Precision-oriented evaluation of recommender systems: an algorithmic comparison
Proceedings of the fifth ACM conference on Recommender systems
Exploiting various implicit feedback for collaborative filtering
Proceedings of the 21st international conference companion on World Wide Web
| Hi-index | 12.05 |
Up to now, more and more online 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 online interest groups where they shall meet people with common tastes. Therefore, in this environment, users might be interested in not only receiving item recommendations (such as music), but also getting friend suggestions so they might put them in the contact list, and group recommendations that they could consider joining. To support such demanding needs, in this paper, we propose a unified 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 investigate the contribution of fusing other two auxiliary information resources (e.g., fusing friendship and membership for recommending items, and fusing user-item preferences and friendship for recommending groups) for boosting the algorithm performance. More notably, the algorithms were developed based on the matrix factorization framework in order to achieve the ideal efficiency as well as accuracy. We performed experiments with two large-scale real-world data sets that contain users' implicit interaction with items. The results revealed the effective fusion mechanism for each type of recommendation in such implicit data condition. Moreover, it demonstrates the respective merits of regularization model and factorization model: the factorization is more suitable for fusing bipartite data (such as membership and user-item preferences), while the regularization model better suits one mode data (like friendship). We further enhanced the friendship's regularization by integrating the similarity measure, which was experimentally proven with positive effect.