acl acl2012 acl2012-205 acl2012-205-reference knowledge-graph by maker-knowledge-mining
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Author: Rui Yan ; Mirella Lapata ; Xiaoming Li
Abstract: Mirella Lapata‡ Xiaoming Li†, \ ‡Institute for Language, \State Key Laboratory of Software Cognition and Computation, Development Environment, University of Edinburgh, Beihang University, Edinburgh EH8 9AB, UK Beijing 100083, China mlap@ inf .ed .ac .uk lxm@pku .edu .cn 2012.1 Twitter enables users to send and read textbased posts ofup to 140 characters, known as tweets. 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 out- performs competitive approaches by a large margin.
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