Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Making large-scale support vector machine learning practical
Advances in kernel methods
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
The Journal of Machine Learning Research
Automatic identification of user interest for personalized search
Proceedings of the 15th international conference on World Wide Web
RankMass crawler: a crawler with high personalized pagerank coverage guarantee
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Introduction to Information Retrieval
Introduction to Information Retrieval
Co-ranking Authors and Documents in a Heterogeneous Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Single document summarization with document expansion
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
DivRank: the interplay of prestige and diversity in information networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic evaluation of linguistic quality in multi-document summarization
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Cross-language document summarization based on machine translation quality prediction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Recommending twitter users to follow using content and collaborative filtering approaches
Proceedings of the fourth ACM conference on Recommender systems
An empirical study on learning to rank of tweets
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
#TwitterSearch: a comparison of microblog search and web search
Proceedings of the fourth ACM international conference on Web search and data mining
Predicting popular messages in Twitter
Proceedings of the 20th international conference companion on World wide web
Semantic enrichment of twitter posts for user profile construction on the social web
ESWC'11 Proceedings of the 8th extended semantic web conference on The semanic web: research and applications - Volume Part II
Analyzing user modeling on twitter for personalized news recommendations
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
User oriented tweet ranking: a filtering approach to microblogs
Proceedings of the 20th ACM international conference on Information and knowledge management
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Predicting purchase behaviors from social media
Proceedings of the 22nd international conference on World Wide Web
Recommending branded products from social media
Proceedings of the 7th ACM conference on Recommender systems
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As one of the most popular micro-blogging services, Twitter attracts millions of users, producing millions of tweets daily. Shared information through this service spreads faster than would have been possible with traditional sources, however the proliferation of user-generation content poses challenges to browsing and finding valuable information. In this paper we propose a graph-theoretic model for tweet recommendation that presents users with items they may have an interest in. Our model ranks tweets and their authors simultaneously using several networks: the social network connecting the users, the network connecting the tweets, and a third network that ties the two together. Tweet and author entities are ranked following a co-ranking algorithm based on the intuition that that there is a mutually reinforcing relationship between tweets and their authors that could be reflected in the rankings. We show that this framework can be parametrized to take into account user preferences, the popularity of tweets and their authors, and diversity. Experimental evaluation on a large dataset shows that our model outperforms competitive approaches by a large margin.