Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Journal of Machine Learning Research
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Recommender systems and their impact on sales diversity
Proceedings of the 8th ACM conference on Electronic commerce
Topic modeling with network regularization
Proceedings of the 17th international conference on World Wide Web
Opinion integration through semi-supervised topic modeling
Proceedings of the 17th international conference on World Wide Web
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Modeling hidden topics on document manifold
Proceedings of the 17th ACM conference on Information and knowledge management
SoRec: social recommendation using probabilistic matrix factorization
Proceedings of the 17th ACM conference on Information and knowledge management
A hybrid approach to item recommendation in folksonomies
Proceedings of the WSDM '09 Workshop on Exploiting Semantic Annotations in Information Retrieval
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Combining link and content for community detection: a discriminative approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Performance of recommender algorithms on top-n recommendation tasks
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
Like like alike: joint friendship and interest propagation in social networks
Proceedings of the 20th international conference on World wide web
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Preference-based graphic models for collaborative filtering
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
mTrust: discerning multi-faceted trust in a connected world
Proceedings of the fifth ACM international conference on Web search and data mining
An exploration of improving collaborative recommender systems via user-item subgroups
Proceedings of the 21st international conference on World Wide Web
Circle-based recommendation in online social networks
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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Traditionally, Collaborative Filtering assumes that similar users have similar responses to similar items. However, human activities exhibit heterogenous features across multiple domains such that users own similar tastes in one domain may behave quite differently in other domains. Moreover, highly sparse data presents crucial challenge in preference prediction. Intuitively, if users' interested domains are captured first, the recommender system is more likely to provide the enjoyed items while filter out those uninterested ones. Therefore, it is necessary to learn preference profiles from the correlated domains instead of the entire user-item matrix. In this paper, we propose a unified framework, TopRec, which detects topical communities to construct interpretable domains for domain-specific collaborative filtering. In order to mine communities as well as the corresponding topics, a semi-supervised probabilistic topic model is utilized by integrating user guidance with social network. Experimental results on real-world data from Epinions and Ciao demonstrate the effectiveness of the proposed framework.