Probabilistic latent semantic indexing
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
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
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
Increasing engagement through early recommender intervention
Proceedings of the third ACM conference on Recommender systems
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
BPR: Bayesian personalized ranking from implicit feedback
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
MyMediaLite: a free recommender system library
Proceedings of the fifth ACM conference on Recommender systems
An exploration of improving collaborative recommender systems via user-item subgroups
Proceedings of the 21st international conference on World Wide Web
Increasing temporal diversity with purchase intervals
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Social-network analysis using topic models
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Advances in Web 2.0 technology has led to the rising popularity of many social network services. For example, there are over 500 million active users in Twitter. Given the huge number of users, user recommendation has gained importance where the goal is to find a set of users whom a target user is likely to follow. Content-based approaches that rely on tweet content for user recommendation have low precision as tweet contents are typically short and noisy, while collaborative filtering approaches that utilize follower-followee relationships lead to higher precision but data sparsity remains a challenge. In this work, we propose a community-based approach to user recommendation in Twitter-style social networks. Forming communities enables us to reduce data sparsity as the focus is on discover the latent characteristics of communities instead of individuals. We employ an LDA-based method on the follower-followee relationships to discover communities before applying the state-of-the-art matrix factorization method on each of the communities. This approach proves effective in improving the conversion rate (by as much as 20%) as demonstrated by the results of extensive experiments on two real world data sets Twitter and Weibo. In addition, the community-based approach is scalable as the individual community can be analyzed separately.