WORKSHOP REPORT

Report on the SIGIR 2015 Workshop on Social
Personalization and Search
Christoph Trattner
Know-CenterGraz, AT
ctrattner@know-center.atDenis Parra
PUC ChileSantiago, CL
dparra@ing.puc.cl

Peter Brusilovsky
University of Pittsburgh
Pittsburgh, USA
peterb@pitt.edu

Leandro B. Marinho
Federal University of Campina GrandeCampina Grande, Brazil
lbmarinho@dsc.ufcg.edu.br
October 23, 2015
Abstract
The use of contexts –side information associated to information tasks– has been one of
the most important dimensions for the improvement of Information Retrieval tasks, helping
to clarify the information needs of the users which usually start from a few keywords in atext box. Particularly, the social context has been leveraged in search and personalization
since the inception of the Social Web, but even today we find new scenarios of information
filtering, search, recommendation and personalization where the use of social signals canproduce a steep improvement. In addition, the action of searching has become a social process on the Web, making traditional assumptions of relevance obsolete and requiring newparadigms for matching the most useful resources that solve information needs. This escenario has motivated us for organizing the Social Personalization and Search (SPS) workshop,
a forum aimed at sharing and discussing research that leverage social data for improving
classic personalization models for information access and to revisiting search from individual
phenomena to a collaborative process.

1Introduction

The Social Personalization & Search workshop1 targeted researchers working in the fields
of Information Retrieval, Personalization, User Modeling, and Recommender Systems where
the social dimension plays a fundamental role. The workshop examined several approaches
that leverage the social side of the search process on two main contexts: (a) using social datafor improving search and recommendations, and (b) search as a social process, collaborative1

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IR. For the first context, we expected to attract contributions that use the social dimension
in different methods for personalization and search such as data from online social networks,
social tagging systems, online forums, and community-based Q&A. This topic is importantbecause it involves leveraging new sources of information that are specific to social systems(such as shared items and tags, user public profiles, online and offline social connections,and logs of user social activities) in order to improve people’s information access in a widevariety of tasks and devices. On the second context, the social dimension was emphasized ascollaboration, so we aimed to discuss works that consider information seeking as a collaborative activity. Here, researches can potentially use explicit social communication information(such as online, offline chat) to boost the personalized search experience for each user, or in acommunity-wise sense, support the common goal of users working on the same search tasks,
such as people planning a group trip. Put together, we invited submissions that included thefollowing topics:• search and recommendations based on social links• search and recommendations in collaborative tagging systems• group-level search personalization• search and recommendations in blogs and microblogs• approaches for social personalization in recommender systems• approaches on personalized social collaboration• approaches on social linking• methods for social search and navigation• methods for social predictive models• social methods for information visualization and display• applications of the aforementioned topics to diverse domains (education, smart cities,music, etc.)• any other methods that exploit new forms of social data for search and personalization
The invited keynote speakers and presenters contributed to achieve the goal of sharing
and discussing research that goes beyond classic personalization techniques, trying to capitalize on potentially useful information available in social data for paving the way to more
efficient personalized information access technologies. In addition to personalization, we also
considered social search and navigation as topics that involve the importance of the social
dimension on filtering and information access.
Overall, we had two great keynote talks, 5 papers and a Panel session to summarise thetopics discussed in the workshop.

2Keynotes

We invited two keynote speakers, who shared their experience and vision with respect to theuse of social signals in personalisation and information access.
Ricardo Baeza-Yates, Yahoo! Labs [1]. This keynote provided an introduction towisdom of crowds in the Web, the long tail of web content, and the bias involved in thegeneration of user generated content (UGC). This bias creates the wisdom of ad hoc crowds

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or the wisdom of a few. Although it is well known that user activity in most settings follows
a power law, that is, few people do a lot, while most do nothing, there are few studies thatcharacterize well this activity. The presenter described a recent analysis of social network
data which corroborated that a small percentage of the active users (passive users are themajority) represent at least the 50% of the UGC. As a sub-product, the study also found alower bound for the digital desert, the content in the Web that nobody reads. These results
implies that most of the wisdom comes from a few users, which is not that surprising, as
the Web is a reflection of our own society, where economical or political power also is in thehands of minorities.
Paul Bennett, Microsoft Research [2]. In this talk the author focused on user andbehavioral modeling, which plays a critical role in a variety of online services such as websearch, advertising, e-commerce, and news recommendation. For example, the ability toaccurately interpret the intent of a web search can be informed by knowledge of the web
pages a searcher was viewing when initiating the search or recent actions of the searcher
such as queries issued, results clicked, and pages viewed. In this talk, the presenter describeda recent framework for personalized search which improves the quality of search results byenabling a representation of a broad variety of contexts including the searcher’s long-term
interests, recent activity, current focus, and other user characteristics. Then, he reviewed avariety of related work that extends these approaches from signals focused on the individual
to social signals such as likes, cohorts, and affiliation networks. At the end of the talk, hespeculated on how social signals and networks can provide directions for relatively unexplored
directions in social personalized retrieval.

3Paper Presentations

Of the nine papers submitted to the workshop, five were selected for inclusion in the proceedings and for presentation at the workshop:• Improving Contextual Suggestions using Open Web Domain Knowledge by Thaer Samar,Alejandro Bellogn and Arjen de Vries [8].
• Finding Intermediary Topics Between People of Opposing Views: A Case Study, byEduardo Graells-Garrido, Mounia Lalmas and Ricardo Baeza-Yates [4].
• Persona-ization: Searching on Behalf of Others, by Paul Bennett and Emre Kiciman [3]• Retrieving Relevant Conversations for Q&A on Twitter by Jose Miguel Herrera, Denis
Parra and Barbara Poblete [6].
• Analysis of Sentiment Communities in Online Networks by Davide Feltoni Gurini, Fabio
Gasparetti, Alessandro Micarelli and Giuseppe Sansonetti [5].

4Conclusions

This was the second workshop on social personalisation & search and the first one heldat the ACM SIGIR conference (the first one was held at the ACM Hypertext Conference2 ).
Summarizing the main topics of the articles and the lessons provided by the keynote speakers,2

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we highlight three interesting topics: (1) Social interaction can help us identify biases inonline communities and we can exploit those biases to overcome the filter bubble [7], (2)Social signals as a context can be leveraged in further domains to the currently studied,such as community question and answering (Q&A) beyond specialized web sites, and (3)Searching on behalf of others could open an interesting new paradigm where the “social”
must be inferred but produce important improvement on search results.

5Acknowledgments

We would like the tank our reviewers ( Luca Maria Aiello (Yahoo! Labs), Jussara Almeida(UFMG), Nazareno Andrade (Universidade Federal de Campina Grande), Krisztian Balog (University of Stavanger), Alejandro Bellogin (UAM), Steven Bourke (Schibsted), RobinBurke (DePaul University), Ernesto Diaz-Aviles (IBM Reseach), Lucas Drumond (Universityof Hildesheim), Michael Ekstrand (Texas State University), Alexander Felfernig (Graz University of Technology), Zeno Gantner (Nokia gate5 GmbH), Ruth Garcia-Gavilabes (BarcelonaMedia), Eduardo Graells (Telefonica I+D), Michael Granitzer (University of Passau), Ido
Guy (Yahoo!), Eelco Herder (L3S), Shuguang Han (University of Pittsburgh), Andreas Hotho(University of Wuerzburg), Geert-Jan Houben (TU Delft), Sharon Hsiao (Arizona State University), Kris Jack (Mendeley), Alexandros Karatzoglou (Telefonica Research), Bart Knijnenburg (University of California), Milos Kravcik (RWTH Aachen University), Kjetil Nrvg
(Norwegian University of Science and Technology), Barbara Poblete (University of Chile),
Giancarlo Ruffo (Universita’ di Torino), Shaghayegh Sahebi (University of Pittsburgh), AlanSaid (Recorded Future), Markus Schedl (Johannes Kepler University), Marc Smith (Connected Action Consulting Group), Nava Tintarev (University of Aberdeen), Eduardo Veas(Know-Center), Tao Ye (Pandora Inc), Zhen Yue (Yahoo Labs), Arkaitz Zubiaga (University of Warwick)), as well our keynote speakers, the authors and the attendees who madeour workshop a great success. Last but not least, we would like to thank the organizationcommittee of the ACM SIGIR 2015 conference for making this workshop happen.

References[1] Baeza-Yates, R. Wisdom of crowds or wisdom of a few? In Proceedings of the International Workshop on Social Personalization & Search, co-located at SIGIR 2015, C. Trattner, D. Parra, P. Brusilovsky, and L. B. Marinho, Eds., CEUR Workshop Proceedings,
CEUR-WS.org, p. 1.
[2] Bennett, P. Search from personal to social context: Progress and challenges. In
Proceedings of the International Workshop on Social Personalization & Search, co-locatedat SIGIR 2015, C. Trattner, D. Parra, P. Brusilovsky, and L. B. Marinho, Eds., CEUR
Workshop Proceedings, CEUR-WS.org, p. 2.
[3] Bennett, P., and Kiciman, E. Persona-ization: Searching on behalf of others. In
Proceedings of the International Workshop on Social Personalization & Search, co-locatedat SIGIR 2015, C. Trattner, D. Parra, P. Brusilovsky, and L. B. Marinho, Eds., CEUR
Workshop Proceedings, CEUR-WS.org, pp. 26–32.
[4] Graells-Garrido, E., Lalmas, M., and Baeza-Yates, R. Finding intermediary
topics between people of opposing views: A case study. In Proceedings of the International

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Workshop on Social Personalization & Search, co-located at SIGIR 2015, C. Trattner,
D. Parra, P. Brusilovsky, and L. B. Marinho, Eds., CEUR Workshop Proceedings, CEURWS.org, pp. 10–16.
[5] Gurini, D. F., Gasparetti, F., Micarelli, A., and Sansonetti, G. Analysis ofsentiment communities in online networks. In Proceedings of the International Workshop
on Social Personalization & Search, co-located at SIGIR 2015, C. Trattner, D. Parra,P. Brusilovsky, and L. B. Marinho, Eds., CEUR Workshop Proceedings, CEUR-WS.org,pp. 17–20.
[6] Herrera, J. M., Parra, D., and Poblete, B. Retrieving relevant conversations for
q&a on twitter. In Proceedings of the International Workshop on Social Personalization& Search, co-located at SIGIR 2015, C. Trattner, D. Parra, P. Brusilovsky, and L. B.
Marinho, Eds., CEUR Workshop Proceedings, CEUR-WS.org, pp. 21–25.
[7] Pariser, E. The filter bubble: What the Internet is hiding from you. Penguin UK, 2011.
[8] Samar, T., Bellogn, A., and de Vries, A. Improving contextual suggestions using
open web domain knowledge. In Proceedings of the International Workshop on Social Personalization & Search, co-located at SIGIR 2015, C. Trattner, D. Parra, P. Brusilovsky,and L. B. Marinho, Eds., CEUR Workshop Proceedings, CEUR-WS.org, pp. 3–9.

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