WWW 2011 Tutorial
Social Recommender Systems
Tuesday, March 29th
(coffee break 15:00-15:30)
room MR 1-01

Tutorial Abstract

The goal of this tutorial is to expose participants to the current research on social recommender systems (i.e., recommender systems for the social web). Participants will become fabvbamiliar with state-of-the-art recommendation methods, their classifications according to various criteria, common evaluation methodologies, and potential applications that can utilize social recommender systems. Additionally, open issues and challenges in the field will be discussed.

Recommendation for the Social Web

Social media sites have become tremendously popular in recent years. Prominent examples include photo and video sharing sites such as Flickr and YouTube, blog and wiki systems such as Blogger and Wikipedia, social tagging sites such as Delicious, social network sites such as MySpace and Facebook, and micro-blogging sites such as Twitter. Millions of users are active daily in these sites, creating rich information online that has not been available before. Yet, the abundance and popularity of social media sites floods users with huge volumes of information and hence poses a great challenge in terms of information overload.

Social Recommender Systems (SRSs) aim to alleviate information overload over social media users by presenting the most attractive and relevant content. SRSs also aim at increasing adoption, engagement, and participation of new and existing users of social media sites. Recommendations of content (blogs, wikis, etc.) [5], tags [7], people [3], and communities [2] often use personalization techniques adapted to the needs and interests of the individual user, or a set of users [6].

Tutorial outline

Tutorial materials

Presentation slides will be posted prior to the tutorial.

Who should attend

Social recommender systems connect many different research area, including information retrieval, human-computer interaction, data mining, machine learning, and artificial intelligence. Therefore, this tutorial should be of theoretical and practical interest to a large portion of the WWW community.

The tutorial does not assume prior knowledge in the area of recommender systems or the other related fields.


Ido Guy manages the Social Technologies group at the IBM Haifa Research Lab, to which he joined in 2000. Ido has led and contributed to various projects around collaboration technologies and social analytics. In 2010, he received a Corporate Award for his contribution to Enterprise Social Software. Ido's main area of research activity is social media, with special focus on social network mining and analysis and onrecommender systems. In recent years, Ido has been particularly active in the area of Social Recommender Systems, publishing key papers on people recommendation [3], content recommendation in social media [4,5], and personalized social search [1]. He is co-chair of the Workshop on Social Recommender Systems (held at IUI 2010 and CSCW2011) and a guest co-editor of the ACM TIST special issue on Social Recommender Systems (to be published in 2011).

David Carmel is a Research Staff Member at the Information Retrieval group at IBM Haifa Research Lab. David earned his PhD in Computer Science from the Technion, Israel Institute of Technology in 1997. David's research is focused on search in the enterprise, query performance prediction, social search, social recommendation and text mining. At IBM, David is a key contributor to IBM enterprise search offerings. David has published more than 80 papers in Information retrieval and Web journals and conferences, and serves on the PC of many Web and IR conferences (SIGIR, WWW, WSDM, CIKM). He is a member of the editorial board of the IR journal, and co-chaired the WDSM 2011 workshop on user modeling for web applications. David has been involved recently in the area of social search and recommendation [1, 4, 5].


[1] David Carmel, Naama Zwerdling, Ido Guy, Shila Ofek-Koifman, Nadav Har'el, Inbal Ronen, Erel Uziel, Sivan Yogev, and Sergey Chernov
Personalized social search based on the user's social network
In Proceeding CIKM, pages 1227-1236. ACM, 2009
[2] Wen-Yen Chen, Jon-Chyuan Chu, Junyi Luan, Hongjie Bai, Yi Wang, and Edward Y. Chang
Collaborative Filtering for orkut communities: discovery of user latent behavior
In Proceedings of WWW, pages 681-690. ACM, 2009
[3] Ido Guy, Inbal Ronen, and Eric Wilcox
Do you know? recommending people to invite into your social network
In Proceedings of IUI, pages 77-86. ACM, 2009
[4] Ido Guy, Naama Zwerdling, David Carmel, Inbal Ronen, Erel Uziel, Sivan Yogev, and Shila Ofek-Koifman
Personalized recommendation of social software items based on social relations
In Proceedings RecSys, pages 53-60. ACM, 2009
[5] Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, and Erel Uziel
Social media recommendation based on people and tags
In Proceeding of SIGIR, pages 194-201. ACM, 2010
[6] Dietmar Jannach, Markus Zanker, Alexander Felfernig, and Gerhard Friedrich
Recommender Systems: An Introduction
Cambridge University Press, 2011
[7] Borkur Sigurbjornsson and Roelof van Zwol
Flickr tag recommendation based on collective knowledge
In Proceedings of WWW, pages 327-336. ACM, 2008