期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2012
卷号：2012
出版社：ACL Anthology
摘要：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.
