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
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Recommendations in taste related domains: collaborative filtering vs. social filtering
Proceedings of the 2007 international ACM conference on Supporting group work
International Journal of Learning Technology
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
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
Learning to recommend with social trust ensemble
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Recommending new movies: even a few ratings are more valuable than metadata
Proceedings of the third ACM conference on Recommender systems
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
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Cross-domain collaboration recommendation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Crowdsourcing recommendations from social sentiment
Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining
Patent partner recommendation in enterprise social networks
Proceedings of the sixth ACM international conference on Web search and data mining
Expert Systems with Applications: An International Journal
Proceedings of the 28th Annual ACM Symposium on Applied Computing
An experimental study on implicit social recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Understanding and improving relational matrix factorization in recommender systems
Proceedings of the 7th ACM conference on Recommender systems
Recommendation in heterogeneous information networks with implicit user feedback
Proceedings of the 7th ACM conference on Recommender systems
Analyzing user behavior across social sharing environments
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Personalized entity recommendation: a heterogeneous information network approach
Proceedings of the 7th ACM international conference on Web search and data mining
| Hi-index | 0.00 |
Collaborative Filtering (CF) based recommender systems often suffer from the sparsity problem, particularly for new and inactive users when they use the system. The emerging trend of social networking sites and their accommodation in other sites like e-commerce can potentially help alleviate the sparsity problem with their provided social relation data. In this paper, we have particularly explored a new kind of social relation, the membership, and its combined effect with friendship. The two type of heterogeneous social relations are fused into the CF recommender via a factorization process. Due to the two relations' respective properties, we adopt different fusion strategies: regularization was leveraged for friendship and collective matrix factorization (CMF) was proposed for incorporating membership. We further developed a unified model to combine the two relations together and tested it with real large-scale datasets at five sparsity levels. The experiment has not only revealed the significant effect of the two relations, especially the membership, in augmenting recommendation accuracy in the sparse data condition, but also identified the ability of our fusing model in achieving the desired fusion performance.