Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2012

Abstract

Twitter is a social information network where short messages or tweets are shared among a large number of users through a very simple messaging mechanism. With a population of more than 100M users generating more than 300M tweets each day, Twitter users can be easily overwhelmed by the massive amount of information available and the huge number of people they can interact with. To overcome the above information overload problem, recommender systems can be introduced to help users make the appropriate selection. Researchers have began to study recommendation problems in Twitter but their works usually address individual recommendation tasks. There is so far no comprehensive survey for the realm of recommendation in Twitter to categorize the existing works as well as to identify areas that need to be further studied. The paper therefore aims to fill this gap by introducing a taxonomy of recommendation tasks in Twitter, and to use the taxonomy to describe the relevant works in recent years. The paper further presents the datasets and techniques used in these works. Finally, it proposes a few research directions for recommendation tasks in Twitter.

Keywords

Twitter, Recommender systems, Personalization

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Science and Engineering

Publication

Social Informatics: 4th International Conference, SocInfo 2012, Lausanne, Switzerland, December 5-7, 2012: Proceedings

Volume

7710

First Page

420

Last Page

433

ISBN

9783642353864

Identifier

10.1007/978-3-642-35386-4_31

Publisher

Springer

City or Country

Cham

Copyright Owner and License

Authors/LARC

Additional URL

https://doi.org/10.1007/978-3-642-35386-4_31

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