Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Learning to Probabilistically Identify Authoritative Documents
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Probabilistic author-topic models for information discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Variational methods for the Dirichlet process
ICML '04 Proceedings of the twenty-first international conference on Machine learning
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
Generative model-based document clustering: a comparative study
Knowledge and Information Systems
A Probabilistic Semantic Based Mixture Collaborative Filtering
UIC '09 Proceedings of the 6th International Conference on Ubiquitous Intelligence and Computing
Distance metric learning from uncertain side information with application to automated photo tagging
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Confucius and "its" intelligent disciples
Proceedings of the 18th ACM conference on Information and knowledge management
Dynamic hyperparameter optimization for bayesian topical trend analysis
Proceedings of the 18th ACM conference on Information and knowledge management
A tag recommendation system for folksonomy
Proceedings of the 2nd ACM workshop on Social web search and mining
Parallelizing Random Walk with Restart for large-scale query recommendation
Proceedings of the 2010 Workshop on Massive Data Analytics on the Cloud
Affiliation recommendation using auxiliary networks
Proceedings of the fourth ACM conference on Recommender systems
Learning a user-thread alignment manifold for thread recommendation in online forum
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
Journal of Information Science
Scalable Affiliation Recommendation using Auxiliary Networks
ACM Transactions on Intelligent Systems and Technology (TIST)
Scalable inference in latent variable models
Proceedings of the fifth ACM international conference on Web search and data mining
A conversation with Dr. Edward Y. Chang
ACM SIGKDD Explorations Newsletter
Recommending Flickr groups with social topic model
Information Retrieval
Applying latent semantic analysis to tag-based community recommendations
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Expert Systems with Applications: An International Journal
Combining latent factor model with location features for event-based group recommendation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
FRec: a novel framework of recommending users and communities in social media
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Pairwise learning in recommendation: experiments with community recommendation on linkedin
Proceedings of the 7th ACM conference on Recommender systems
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Rapid growth in the amount of data available on social networking sites has made information retrieval increasingly challenging for users. In this paper, we propose a collaborative filtering method, Combinational Collaborative Filtering (CCF), to perform personalized community recommendations by considering multiple types of co-occurrences in social data at the same time. This filtering method fuses semantic and user information, then applies a hybrid training strategy that combines Gibbs sampling and Expectation-Maximization algorithm. To handle the large-scale dataset, parallel computing is used to speed up the model training. Through an empirical study on the Orkut dataset, we show CCF to be both effective and scalable.