Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Why we twitter: understanding microblogging usage and communities
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Make new friends, but keep the old: recommending people on social networking sites
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Personalized recommendation of social software items based on social relations
Proceedings of the third ACM conference on Recommender systems
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
The network effects of recommending social connections
Proceedings of the fourth ACM conference on Recommender systems
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With the exponential growth of users' population and volumes of content in micro-blog web sites, people suffer from information overload problem more and more seriously. Recommendation system is an effective way to address this issue. In this paper, we studied celebrities recommendation in micro-blog services to better guide users to follow celebrities according to their interests. First we improved the jaccard similarity measure by significant weighting to enhance neighbor selection in collaborative filtering. Second, we integrated users' social information into the similarity model to ease the cold start problem. Third we increased the density of the rating matrix by predicting the missing ratings to ease the data sparsity problem. Experiment results show that our algorithm improves the recommendation quality significantly.