Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
A maximum entropy web recommendation system: combining collaborative and content features
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Collaborative filtering for orkut communities: discovery of user latent behavior
Proceedings of the 18th international conference on World wide web
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Content-based recommendation systems
The adaptive web
Content-Based image filtering for recommendation
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Challenging the long tail recommendation
Proceedings of the VLDB Endowment
Recommendation in Online Health Communities
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Flickr group recommendation based on user-generated tags and social relations via topic model
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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With the number of social communities grows, social community recommendation has gradually become a critical technique for users to efficiently find their favorite communities. Currently a variety of recommendation techniques have been developed, such as content-based method, collaborative filtering, etc. There methods either easily overfit the data due to the limitation of observations or suffer the heavy computational cost. Besides, they don't consider the relationships between users and communities, and cannot handle incoming users. In this paper, we propose a soft-constraint based online LDA (SO-LDA) method. We use the number of user's posts within each community as soft-constraint to estimate the latent topics across the communities by an online LDA algorithm, in which an incremental method is adopted to facilitate model updating when incomes a new user. Experiment on the well-known MySpace community data shows that the proposed method takes much less time and outperforms the state-of-the-art community recommendation methods.