%0 Conference Paper %1 Yan:2012:TRG:2390524.2390597 %A Yan, Rui %A Lapata, Mirella %A Li, Xiaoming %B Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1 %C Stroudsburg, PA, USA %D 2012 %I Association for Computational Linguistics %K phdproposal recommendation tweets %P 516--525 %T Tweet Recommendation with Graph Co-ranking %U http://dl.acm.org/citation.cfm?id=2390524.2390597 %X 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.

@inproceedings{Yan:2012:TRG:2390524.2390597, abstract = {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.}, acmid = {2390597}, added-at = {2015-03-06T17:39:05.000+0100}, address = {Stroudsburg, PA, USA}, author = {Yan, Rui and Lapata, Mirella and Li, Xiaoming}, biburl = {https://www.bibsonomy.org/bibtex/2ad8c03e78154ae5274d61cce67185c07/asmelash}, booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1}, description = {Tweet recommendation with graph co-ranking}, interhash = {45d28504e9e2016411d43797c9f7934c}, intrahash = {ad8c03e78154ae5274d61cce67185c07}, keywords = {phdproposal recommendation tweets}, location = {Jeju Island, Korea}, numpages = {10}, pages = {516--525}, publisher = {Association for Computational Linguistics}, series = {ACL '12}, timestamp = {2015-03-06T17:39:05.000+0100}, title = {Tweet Recommendation with Graph Co-ranking}, url = {http://dl.acm.org/citation.cfm?id=2390524.2390597}, year = 2012 }