Publication Type

Journal Article

Publication Date

9-2017

Abstract

Microblogging services such as Sina Weibo and Twitter allow users to create tags explicitly indicated by the # symbol. In Sina Weibo, these tags are called microtopics, and in Twitter, they are called hashtags. In Sina Weibo, each microtopic has a designate page and can be directly visited or commented on. Recommending these microtopics to users based on their interests can help users efficiently acquire information. However, it is non-trivial to recommend microtopics to users to satisfy their information needs. In this article, we investigate the task of personalized microtopic recommendation, which exhibits two challenges. First, users usually do not give explicit ratings to microtopics. Second, there exists rich information about users and microtopics, for example, users' published content and biographical information, but it is not clear how to best utilize such information. To address the above two challenges, we propose a joint probabilistic latent factor model to integrate rich information into a matrix factorization-based solution to microtopic recommendation. Our model builds on top of collaborative filtering, content analysis, and feature regression. Using two real-world datasets, we evaluate our model with different kinds of content and contextual information. Experimental results show that our model significantly outperforms a few competitive baseline methods, especially in the circumstance where users have few adoption behaviors.

Keywords

Microblogs, microtopic recommendation, topic model, collaborative filtering

Discipline

Databases and Information Systems | Data Storage Systems

Research Areas

Data Management and Analytics

Publication

ACM Transactions on Intelligent Systems and Technology

Volume

8

Issue

6

ISSN

2157-6904

Identifier

10.1145/2932192

Publisher

Association for Computing Machinery (ACM)

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org./10.1145/2932192

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