3 research outputs found
Inferring user interests in microblogging social networks: a survey
With the growing popularity of microblogging services such as Twitter in recent years,
an increasing number of users are using these services in their daily lives. The huge volume of information generated by users raises new opportunities in various applications
and areas. Inferring user interests plays a significant role in providing personalized
recommendations on microblogging services, and also on third-party applications
providing social logins via these services, especially in cold-start situations. In this
survey, we review user modeling strategies with respect to inferring user interests
from previous studies. To this end, we focus on four dimensions of inferring user
interest profiles: (1) data collection, (2) representation of user interest profiles, (3)
construction and enhancement of user interest profiles, and (4) the evaluation of the
constructed profiles. Through this survey, we aim to provide an overview of state-of-the-art user modeling strategies for inferring user interest profiles on microblogging
social networks with respect to the four dimensions. For each dimension, we review
and summarize previous studies based on specified criteria. Finally, we discuss some
challenges and opportunities for future work in this research domain
User Interest Modeling in Twitter with Named Entity Recognition
Considering wide use of Twitter as the source of information, reaching an interesting tweet for a user among a bunch of tweets is challenging. In this work we propose a Named Entity Recognition (NER) based user profile modeling for Twitter users and employ this model to generate personalized tweet recommendations. Effectiveness of the proposed method is shown through a set of experiments. Copyright © 2015 held by author(s)