مقالات ISI مدیریت

TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation

A B S T R A C T

Twitter provides search services to help people find users to follow by recommending
popular users or the friends of their friends. However, these services neither offer the
most relevant users to follow nor provide a way to find the most interesting tweet
messages for each user. Recently, collaborative filtering techniques for recommendations
based on friend relationships in social networks have been widely investigated. However,
since such techniques do not work well when friend relationships are not sufficient, we
need to take advantage of as much other information as possible to improve the
performance of recommendations.
In this paper, we propose TWILITE, a recommendation system for Twitter using probabilistic
modeling based on latent Dirichlet allocation which recommends top-K users to
follow and top-K tweets to read for a user. Our model can capture the realistic process of
posting tweet messages by generalizing an LDA model as well as the process of connecting
to friends by utilizing matrix factorization. We next develop an inference algorithm based
on the variational EM algorithm for learning model parameters. Based on the estimated
model parameters, we also present effective personalized recommendation algorithms
to find the users to follow as well as the interesting tweet messages to read. The
performance study with real-life data sets confirms the effectiveness of the proposed
model and the accuracy of our personalized recommendations.
& 2013 Elsevier Ltd. All rights reserved.

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کد محصول : شماره 176

jozvekade (176)

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