Recommendation in Social Media

author: Huan Liu, Department of Computer Science and Engineering, Arizona State University
author: Jie Tang, Department of Computer Science and Technology, Tsinghua University
author: Jiliang Tang, Department of Computer Science and Engineering, Arizona State University
published: Oct. 7, 2014,   recorded: August 2014,   views: 498
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Description

The pervasive use of social media generates massive data in an unprecedented rate and the information overload problem becomes increasingly severe for social media users. Recommendation has been proven to be effective in mitigating the information overload problem, demonstrated its strength in improving the quality of user experience, and positively impacted the success of social media. New types of data introduced by social media not only provide more information to advance traditional recommender systems but also manifest new research possibilities for recommendation. In this tutorial, we aim to provide a comprehensive overview of various recommendation tasks in social media, especially their recent advances and new frontiers. We introduce basic concepts, review state-of-the-art algorithms, and deliberate the emerging challenges and opportunities. Finally we summarize the tutorial with discussions on open issues and challenges about recommendation in social media.

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Download slides icon Download slides: kdd2014_tang_tang_liu_media.pdf (33.6┬áMB)


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