Summary:
Micro-blogging services constitute a popular means of real time communication
and information sharing. Twitter is the most popular of these services with 300
million monthly active user accounts and 500 million tweets posted in a daily
basis at the moment. Consequently, Twitter users suffer from an information
deluge and a large number of recommendation methods have been proposed to
re-rank the tweets in a user's timeline according to her interests. We focus on
techniques that build a textual model for every individual user to capture her
tastes and then rank the tweets she receives according to their similarity with
that model.
In the literature, there is no comprehensive evaluation of these user modeling
strategies as yet. To cover this gap, in this thesis we systematically examine
on a real Twitter dataset, 9 state-of-the-art methods for modeling a user's
preferences using exclusively textual information. Our goal is to identify the
best performing user model with respect to several criteria: (i) the source of
tweet information available for modeling (ii) the user type, as determined by
the relation between the tweeting frequency of a user and the frequency of her
received tweets, (iii) the characteristics of its functionality, as derived
from a novel taxonomy, and (iv) its robustness with respect to its internal
configurations, as deduced by assessing a wide range of plausible values for
internal parameters. Our results can be used for fine-tuning and interpreting
text user models in a recommendation scenario in microblogging services and
could serve as a starting point for further enhancing the most effective user
model with additional contextual information.
Keywords:
Twitter, Microblogging, User model, Textual representation model, Topic model
