LIKE and Recommendation in Social Media

This tutorial covers the state-of-the-art developments in LIKE and recommendation in social media. It is designed for graduate students, practitioners, or IT managers with general understanding on WWW and social media. No prerequisite is expected. 1. TOPICS AND DESCRIPTION The recent dramatic increase in the usage and prevalence of social media has led to the creation and sharing of a significant amount of information in various formats such as texts, photos, or videos. When it comes to information consumption, people not only access and appreciate published contents, but also interact with them by adding comments or pressing Like buttons (or expressing other relationships similar to Like in nature such as “+1” in Google+, “re-pin” in Pinterest, and “favorite” in Flickr). With such massive social media data with rich LIKE-like relationships therein, recommendation techniques has been proven to be effective in mitigating the information overload problem. They have demonstrated their 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. With the explosive increase of massive amount of user generated contents and relationships thereof found in WWW and social media, the topic covered in this tutorial is timely and important. As such, the summarized coverage of the topic in general and elaborated presentation on the selected techniques in particular would be a useful tutorial to WWW conference and participating audience. In this tutorial, therefore, we aim to provide a comprehensive overview of: • Various examples of LIKE in social media, the analysis and modeling of LIKE activities, and techniques to predict the creation and deletion of LIKE relationship in social media, and Copyright is held by the author/owner(s). WWW 2015 Companion, May 18–22, 2015, Florence, Italy. ACM 978-1-4503-3473-0/15/05. http://dx.doi.org/10.1145/2740908.2741981. • Various recommendation tasks in social media, especially their recent advances and new frontiers, and the emerging challenges and opportunities. In particular, this tutorial consists of four parts: (1) Preclude we introduce real-world examples and show how to bridge LIKE and Recommendation in Social Media; (2) LIKE in Social Media LIKE Analysis, Modeling, Prediction, and Summary; (3) Recommendation in Social Media Friend, Content, and Location Recommendation, and summary; and (4) Postlude Conclusions with Future Directions. The reference section below lists some exemplary work related to the topics covered in the tutorial.