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Program

The workshop consists of an opening keynote, oral presentations of research papers, an interactive session, and a concluding open discussion.

Keynote

Recommender Systems in Business and Life

Abstract: This talk will present a personal perspective on my involvement in recommender systems over the past 25 years, including my experiences in translating academic research into practical products and services. I will reflect on some of the early lessons learned when bringing recommender systems to new markets — such as the nascent mobile internet back in the early 2000’s — where the primary impact was less about the now familiar e-commerce benefits of selling more products as it was about saving people time and reducing friction and frustration as they struggled with small screens, low bandwidth, and a distinctly limited mode of interaction. This talk will also consider what the future might hold for recommender systems as they begin to be deployed in novel application contexts and domains, such as health and exercise, which forces us to confront a very different set of deployment circumstances, user needs, and potential benefits.

		Prof. Barry Smyth holds the Digital Chair of Computer Science in University College Dublin and is a Director of the Insight Centre for Data Analytics. He is a Fellow of the European Coordinating Committee on Artificial Intelligence (ECCAI) since 2003 and a Member of the Royal Irish Academy since 2011. In 2014 Barry was awarded an Honrary Doctor of Technology (Hons. D.Tech) from Robert Gordon University in the UK. Barry was the Director of the Clarity Centre for Sensor Web Technologies (2008 - 2013) and has previously held the position of Head of School for the School of Computer Science and Informatics in UCD.Barry's research interests fall within the field Artificial Intelligence and include case-based reasoning, machine learning, recommender systems, user modeling and personalization.

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Detailed Schedule, all times in GMT

14:00 - 14:10 Opening
14:10 - 15:00 Keynote (Barry Smyth)
15:00 - 15:30 Paper Session 1

• Exploring Artist Gender Bias in Music Recommendation by Dougal Shakespeare, Lorenzo Porcaro, Emila Gomez and Carlos Castillo (video)
• Maximizing the Engagement: Exploring New Signals of Implicit Feedback in Music Recommendations by Andres Ferraro, Sergio Oramas, Massimo Quadrana and Xavier Serra (video)

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

• div2vec: Diversity-Emphasized Node Embedding by Jisu Jeong, Jeong-Min Yun, Hongi Keam, Young-Jin Park, Zimin Park and Junki Cho (video)
• Intelligent Recommendations for Citizen Science by Naama Dayan, Kobi Gal, Avi Segal, Guy Shani and Darlene Cavalier (video)

17:00 - 18:00 Panel Discussion

• Long-term and Indirect Impact of Recommender Systems in Business
  Panelists: Mounia Lalmas (Spotify), Johanna Boston (JustData), Madhu Kurup (Amazon), and Emanuel Vianna (SEEK)

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Background of the Workshop

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.

The workshop is a successor of the First Workshop on the Impact of Recommender Systems (ImpactRS 2019), which was held at ACM RecSys 2019 in Copenhagen.

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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 (14 pages plus references), short papers reporting on work in progress (7 pages plus references), and position papers (up to four pages including references). Submissions must be formatted according to the conference guidelines (ACM SIG Proceedings Template - single column) 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.

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Important dates

• Paper submission deadline: July 29th, 2020
• Author notification: August 18th, 2020
• Camera-ready version deadline: September 4th, 2020
• ImpactRS '20 Workshop: September 25th, 2020
• RecSys conference: 22nd to 26th September, 2020

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Organization

Workshop Co-Organizers

• Oren Sar Shalom: Oren is a principal data scientist at Facebook, Israel, 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.
• Joe Konstan: Joe is 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.

Program Committee

• Gediminas Adomavicius, University of Minnesota
• Christine Bauer, Utrecht University
• Joeran Beel, Trinity College Dublin
• Pablo Castells, Universidad Autónoma de Madrid
• Li Chen, Hong Kong Baptist University
• 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
• Amit Livne, Ben-Gurion University
• Massimo Quadrana, Pandora
• Adi Shalev, Intuit
• Bracha Shapira, Ben-Gurion University
• Harald Steck, Netflix
• Tao Ye, Amazon
• 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: impactrs20@ainf.at

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