All times below are in UTC. The workshop proceedings are online at http://ceur-ws.org/Vol-2697/.
14:05-14:50 (Keynote) Ratings in, rankings out. Keep it simple, they said. But we need more than that. Christine Bauer (Utrecht University)
14:50-15:10 Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning. Oleksii Moskalenko, Diego Saez-Trumper and Denis Parra [PDF]
15:10-15:30 CuratorNet: Visually-aware Recommendation of Art Images. Denis Parra, Pablo Messina, Manuel Cartagena, Felipe del Rio and Patricio Cerda-Mardini [PDF]
16:30-16:50 Beyond algorithms: Ranking at scale at Booking.com. Themis Mavridis, Soraya Hausl, Andrew Mende and Roberto Pagano [PDF]
16:50-17:10 Personalizing Item Recommendation via Price Understanding. Soumya Wadhwa, Ashish Ranjan, Selene Xu, Jason H.D. Cho, Sushant Kumar and Kannan Achan [PDF]
17:10-17:30 Making Sense of the Urban Future: Recommendation Systems in Smart Cities. Dirk Ahlers [PDF]
17:30-18:00 Discussion + Closing
General attendees can join the session via an embedded Zoom interface on Whova (link to session on Whova), which prevents the Zoom Meeting link from leaking and potential zoom bombing.
Title: Ratings in, rankings out. Keep it simple, they said. But we need more than that.
Abstract: Among the many viable research questions in the field of recommender systems, a frequently addressed problem is to accurately predict the relevance of individual items to users, with the goal of presenting the assumedly most relevant ones as recommendations. Typically, we have users’ (explicit or implicit) ratings as input and rankings of items as output. Complex enough, yet too simplistic to reflect reality and indeed meet the various demands in practice.
We have learned that “context matters”. But what does it mean? What is the context that matters? And how do we get the relevant signals? It is more than what we currently ascribe to and reflect in what we call “context-aware recommender systems”. Let’s have a view to related fields that deal with context as deeply complex input.
And on the output side, we have individual items and also item bundles, complementaries, sequences, repeated recommendations, etc. What do we actually want to present? And how? For who? And why? A ranked list as output may seem like an appropriate one-size-fits-all solution, does it?
In this talk, I will reflect on the complexity of our research field, reach out to related fields such as context-aware computing and pervasive advertising for inspiration, and I will raise a lot of questions that have yet to be answered.
Speaker: Christine Bauer is an assistant professor at Utrecht University, The Netherlands. Her research activities center on interactive intelligent systems. In doing so, she takes a human-centered perspective, where technology follows humans’ and the society’s needs. She focuses on context-adaptive systems and, currently, on music recommender systems in particular.
Her research and teaching activities are driven by her interdisciplinary background. She holds a Doctoral degree in Social and Economic Sciences, a Diploma degree in International Business Administration, and a Master degree in Business Informatics. Furthermore, she pursued studies in jazz saxophone.
Christine has authored more than 90 scientific papers in refereed journals and conference proceedings and holds 4 best paper awards as well as 3 awards for her reviewing activities.
Before joining Utrecht University, she brought her prestigious Elise Richter grant to Johannes Kepler University Linz, Austria. Earlier she researched at WU Vienna, Austria, University of Cologne, Germany, and the E-Commerce Competence Center, Austria. In 2013 and 2015, she was Visiting Fellow at Carnegie Mellon University, Pittsburgh, PA, USA. Before starting her academic career, she has built up and led the field of Licensing New Media at Austria’s biggest collecting society AKM.
Christine is an experienced teacher and has been teaching a wide spectrum of topics in computing and information systems across 10 institutions. She engages in mentoring for initiatives such as Women in Music Information Retrieval. More information can be found at https://christinebauer.eu
- Oleksii Moskalenko, Diego Saez-Trumper and Denis Parra. Scalable Recommendation of Wikipedia Articles to Editors Using Representation Learning
- Denis Parra, Pablo Messina, Manuel Cartagena, Felipe del Rio and Patricio Cerda-Mardini. CuratorNet: Visually-aware Recommendation of Art Images
- Themis Mavridis, Soraya Hausl, Andrew Mende and Roberto Pagano. Beyond algorithms: Ranking at scale at Booking.com
- Soumya Wadhwa, Ashish Ranjan, Selene Xu, Jason H.D. Cho, Sushant Kumar and Kannan Achan. Personalizing Item Recommendation via Price Understanding
- Dirk Ahlers. Making Sense of the Urban Future: Recommendation Systems in Smart Cities