Collaborative Information Retrieval: Concepts, Models and Evaluation

Lynda Tamine, Université de Toulouse UPS IRIT, France
Laure Soulier, Sorbonne Universités, UPMC - CNRS LIP6, France

Duration: Half-Day

Traditional conceptualizations of an IR task generally rely on an individual user's perspective. Accordingly, a great amount of research in the IR domain mostly dealt with both the design of enhanced document ranking models and a deep user's behavior understanding with the aim of improving an individual search effectiveness. However, in practice, collaboration among a community of users is increasingly acknowledged as an effective mean for gathering the complementary skills and/or knowledge of individual users in order to solve complex shared search tasks. This category of complex search settings frequently occurs within a wide-range of domain-applications, such as the medical domain, the legal domain, and the librarian domain. This interactive multi-user context gives rise to several challenges in IR addressed in the area of Collaborative Information retrieval (CIR).
The main body of the tutorial is composed of five parts. In the first part, we will introduce basic and intermediate concepts underlying CIR. In the second part of the tutorial, we will shift our focus to collaborative ranking models proposed in the literature. In the third part of the tutorial we address the evaluation issue. In particular, we will pay attention to the evaluation frameworks for CIR models as well as the related metrics. The fourth part will be devoted to present some perspectives in CIR. Finally, we will conclude by a discussion phase.
The tutorial is ideal for early career scientists interested in designing CIR frameworks where collaboration is held either by small working teams or social-media-based communities. The tutorial should also attract researchers and experts aiming at tackling the core issue of evaluation in CIR.

Website: http://www-connex.lip6.fr/~soulier/CIR2016.html

Presentation: Download slides (PDF)

Group Recommender Systems: State of the Art, Emerging Aspects and Techniques, and Research Challenges (GroupRecSys2016)

Ludovico Boratto, University of Cagliari, Italy

Duration: Half-Day

A recommender system aims at suggesting to users items that might interest them and that they have not considered yet. A class of systems, known as group recommendation, provides suggestions in contexts in which more than one person is involved in the recommendation process. The goal of this tutorial is to provide the ECIR audience with an overview on group recommendation. We will first illustrate the recommender systems principles, then formally introduce the problem of producing recommendations to groups, and present a survey based on the tasks performed by these systems. We will also analyze challenging topics like their evaluation, and present emerging aspects and techniques in this area. The tutorial will end with a summary that highlights open issues and research challenges.

Website: http://people.unica.it/ludovicoboratto/group-recommendation-ecir2016/

Living Labs for Online Evaluation: From Theory to Practice (LiLa2016)

Anne Schuth, University of Amsterdam, The Netherlands
Krisztian Balog, University of Stavanger, Norway

Duration: Half-Day

Experimental evaluation was always central to Information Retrieval research. The field is increasingly moving towards online evaluation, which involves experimenting with real, unsuspecting users in their natural task environments, a so-called living lab. Specifically the recent introduction of the Living Labs for IR Evaluation initiative, which also runs as a lab at CLEF and TREC OpenSearch, made it possible for researchers to have direct access to such labs. With recent developments, we believe that online evaluation will be an exciting area to work on in the future. This half-day tutorial aims to provide a comprehensive overview of the underlying theory and complement it with practical guidance.

Website: http://living-labs.net/tutorial/

Real-Time Bidding based Display Advertising: Mechanisms and Algorithms (RTBMA 2016)

Jun Wang, University College London, UK
Shuai Yuan, MediaGamma, UK
Weinan Zhang, University College London, UK

Duration: Half-Day

In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. The ability of making impression level bid decision and targeting to an individual user in real-time has fundamentally changed the landscape of the digital media. The further demand for automation, integration and optimisation in RTB brings new research opportunities in the IR fields, including information matching with economic constraints, CTR prediction, user behaviour targeting and profiling, personalised advertising, and attribution and evaluation methodologies. In this tutorial, teamed up with presenters from both the industry and academia, we aim to bring the insightful knowledge from the real-world systems, and to provide an overview of the fundamental mechanism and algorithms with the focus on the IR context. We will also introduce to IR researchers a few datasets recently made available so that they can get hands-on quickly and enable the said research.

Website: http://tutorial.computational-advertising.org