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Program

The workshop consists of an opening keynote by Prof. Joe Konstan, oral presentations of research papers (12+3 minutes), the presentation of position papers as posters during the coffee break, and a concluding open discussion.

The proceedings of the workshop are available at CEUR-WS.org (Volume 2462)

Keynote

Recommending for Impact: Intentions, Algorithms, and Metrics (slides)

Abstract: What does it mean to recommend for impact? Where are there tensions between the interests of the recommender operator and the interests of the user? Can we measure the impact of a recommendation, or a recommender algorithm? And how can we optimize algorithms to improve impact? This talk takes a long look at the field of recommender systems, including periods of significant impact-focused developments and periods of technical progress made by looking at other objectives. Building on that past, we look at promising directions for impact-focused recommender systems research.

		Joseph A. Konstan is Distinguished McKnight University Professor, Distinguished University Teaching Professor, and the College of Science and Engineering's Associate Dean for Research at the University of Minnesota. His research addresses a variety of human-computer interaction issues, including personalization (particularly through recommender systems), eliciting on-line participation, and designing computer systems to improve public health. He is probably best known for his work in collaborative filtering recommenders (the GroupLens project, which won the ACM Software Systems Award).

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Detailed Schedule

14:00 - 14:10 Opening
14:10 - 15:00 Keynote (Joe Konstan)
15:00 - 15:30 Paper Session 1

• The Impact of Recommenders on Scientific Article Discovery: The Case of Mendeley Suggest by Minh Le and Subhradeep Kayal
• Towards creating a non-synthetic group recommendation dataset by Matthijs Rijlaarsdam, Sebastiaan Scholten and Cynthia Liem

15:30 - 16:00 Coffee Break / Poster Presentations
16:00 - 17:00 Paper Session 2

• A Model for Evaluating Popularity and Semantic Information Variations in Radio Listening Sessions by Lorenzo Porcaro and Emilia Gomez
• Assessing the Impact of a User-Item Collaborative Attack on Class of Users by Felice Antonio Merra, Yashar Deldjoo and Tommaso Di Noia
• Does the Bubble Go Beyond? by Annelien Smets, Eladio Montero and Pieter Ballon
• Recommendations for Long-Term Profit Optimization by Patrick Hosein, Inzamam Rahaman, Keanu Nichols and Kiran Maharaj

17:00 - 17:30 Open Discussion and Workshop Closing

• Towards More Impactful Recommender Systems Research -- A working paper by Dietmar Jannach, Oren Sar Shalom, and Joe Konstan

Position Papers

• Analysing Recommender Systems Impact on Users' Choices by Naieme Hazrati, Mehdi Elahi and Francesco Ricci
• Here, There, and Everywhere: Building a Scaffolding for Children's Learning through Recommendations by Ashlee Milton, Maria Soledad Pera, Emiliana Murgia, Monica Landoni and Theo Huibers
• Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings by Christine Bauer and Eva Zangerle
• Measuring the Impact of Recommender Systems by Benedikt Loepp and Jürgen Ziegler
• Up Close, but not too Personal: Hypotargeting for Recommender Systems by Martha Larson and Manel Slokom

Best Paper

The best paper award sponsored by Intuit goes to:
Recommendations for Long-Term Profit Optimization by Patrick Hosein, Inzamam Rahaman, Keanu Nichols and Kiran Maharaj

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Background

Research in the area of recommender systems is largely focused on helping individual users finding items they are interested in. This is usually done by learning to rank the recommendable items based on their assumed relevance for each user. The implicit underlying goal of a such system is to affect users in different positive ways, e.g., by making their search and decision processes easier or by helping them discover new things.

Recommender systems can, however, also have other more directly-measurable impacts, e.g., such that go beyond the individual user or the short term influence. A recommender system on a news platform, for example, can lead to a shift in the reading patterns of the entire user base. Similarly, on e-commerce platforms, it has been shown that a recommender can induce significant changes in the purchase behavior of consumers, leading, for example, to generally higher sales diversity across the site. On the other hand, recommender systems usually serve certain business goals and can have an impact not only on the customers, e.g., by stimulating higher engagement on a media streaming platform or a social network, but also direct and indirect affect sales, revenue or conversion and churn rates.

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Goals of the Workshop

The research literature that considers such more direct measurements of impact of recommender systems on the various stakeholders is comparably scarce and scattered. With the proposed workshop, we pursue different goals.

• First, the workshop will serve as a platform where researchers can present their latest works in which they analyzed different forms of impact of recommenders. We consider both papers where impact on individual users was measured (e.g., more healthy eating habits that were stimulated by a food recommender or a more efficient choice process), papers that highlight effects on a community or a society as a whole, and papers that demonstrate effects in terms of business value.
• Second, the goal of the interactive session is to explore new ways how the impact of recommender systems can be measured within academic settings, i.e., were some impacts can only be analyzed based on simulations or alternative computational measures.
• Third, the workshop shall serve as an instrument to raise awareness in the community regarding the importance of impact-oriented research. This aspect in our view is particularly important as more and more research works indicate that optimizing for the most accurate prediction not necessarily leads to the best recommendations in terms of the users' quality perception or the desired effects of a recommender.

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Topics

The topics of interest include, e.g.,

• Field studies on the impact and business value of recommender systems
• Offline evaluation protocols and measures to assess the impact or recommenders
• User studies on the effects of recommenders on users, e.g., on decision making
• Simulation-based approaches to impact assessment
• Price- and profit-awareness of recommender systems
• Network effects of recommender systems
• Multi-stakeholder recommender systems
• Beyond accuracy optimization methods for recommender systems (e.g., business metrics, user satisfaction)
• Long-term implications of deployed recommender systems

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Submission and Publication

Submission types are long research papers reporting on complete research (8 pages plus references), short papers reporting on work in progress (4 pages plus references), and position papers (up to two pages). Submissions must be formatted according to the conference guidelines (ACM SIG Proceedings Template) and submitted via EasyChair.

The review process is single-blind, i.e., please include author names in the papers. All papers will be peer reviewed by the workshop's program committee.

Accepted papers will be published within formal proceedings at CEUR-WS.org.

The best paper will be awarded with a Google Home (can be replaced with an equivalent prize upon request).

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Important dates

• Paper submission deadline: July 1st, 2019
• Author notification: July 29th, 2019
• Camera-ready version deadline: August 27th,2019
• ImpactRS'19 Workshop: September 19th, 2019
• RecSys conference: September 16-20th, 2019

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Organization

Workshop Co-Organizers

• Oren Sar Shalom: Oren is a principal data scientist at Intuit AI, where he conducts applied machine learning research in the fields of recommender systems and NLP
• Dietmar Jannach: Dietmar is a professor of computer science at the University of Klagenfurt, Austria, with a special research focus on recommender systems.
• Ido Guy: Ido is a Global Director of Applied Research at eBay. Prior to that he was a Principal Research Engineer at Yahoo! Labs and a Senior Technical Staff Engineer and Manager at IBM Research.

Program Committee

• Gediminas Adomavicius, University of Minnesota
• Christine Bauer, University of Innsbruck
• Joeran Beel, Trinity College Dublin
• Pablo Castells, Universidad Autónoma de Madrid
• Paolo Cremonesi, Politecnico di Milano
• Michael Ekstrand, Boise State University
• Alexander Felfernig, TU Graz
• Maurizio Ferrari Dacrema, Politecnico di Milano
• Werner Geyer, IBM T. J. Watson Research
• Michael Jugovac, TU Dortmund
• Surya Kallumadi, The Home Depot
• Iman Kamehkhosh, TU Dortmund
• Gal Lavee, Microsoft
• Slava Novgorodov, eBay Research
• Massimo Quadrana, Pandora
• Filip Radlinski, Google
• Adi Shalev, Intuit
• Harald Steck, Netflix
• Markus Zanker, Free University of Bozen-Bolzano
• Yong Zheng, Illinois Institute of Technology
• Alex Zhicharevich, Tel Aviv University

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Contact

If you have questions regarding the workshop, do not hesitate to contact the workshop chairs: impactrs19@ainf.at

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