In our daily activities we interact with different types of devices, i.e. personal computers, smartphones and tablets, in order to access information. The interactions exploit also different means, such as the usage of mobile applications, the visualization and the upload of user-generated content in social networks, the browsing of a website, and so on. Recommender Systems produce suggestions to users for items, contents, user profiles, etc. they have not considered but might interest them, by analyzing what they previously liked, bought, watched or listened. Such an explicit feedback is an expression of extreme ratings either positive or negative. In the middle of the range stays a set of different actions in the interface that might be interpreted as feedback, but that needs to be collected implicitly. Even if the literature provides different techniques for collecting implicit feedback, they are tailored for specific types of applications. From the user’s point of view Recommender Systems remain a black box that suggests objects or contents, but the users hardly understand why some items are included in the suggestion list. Providing the users with an understandable representation of how the system represents them would have two types of benefits. On the one hand, the user is able to track the origin of each suggested item, connecting it to a property in the user model. This would increase the user’s trust towards the system. On the other hand, the user may change incorrect attributes and this would lead to more precise recommendations. For instance, it would be possible for the user to search for the latest album of her sister’s favorite band in order to give a present for her birthday. But maybe the user likes a completely different genre.
In this regard the user interface engineering community has the expertise for generalizing the existing approaches, and to elaborate new patterns and metaphors for supporting users in both inspecting and controlling Recommender Systems and the goal of this workshop is to solicit the collaboration between recommendation and user interface experts.
The papers in this workshop proceedings book present different results and ongoing research on the following topics: • Design patterns, metaphors and innovative solutions for the end-user inspection and control of a Recommender System • Case studies, applications, prototypes of innovative ways for considering the users’ interactions as data for Recommender Systems • Position papers on problems and solutions for supporting the Recommender Systems through user interaction and the user while interacting with applications that exploit Recommender Systems • Feature selection and data filtering approaches to extract information from the data gathered through Human-Computer Interaction techniques, for recommendation purposes • Analysis of implicit data collected from real-world systems, in order to evaluate their effectiveness for recommendation and personalization purposes The workshop was an event co-located with the eight ACM SIGCHI conference on Engineering Interactive Systems (EICS 2016). After the review process for ensuring the paper quality, the programme committee selected 6 papers: 4 full and 2 short papers. In addition, Markus Zanker was invited for presenting his work on persuasive recommender systems during the workshop keynote.
We thank all the authors for their submissions and all members of the program committee. We are grateful to the EICS workshop chairs Judy Bowen, Bruno Dumas and Jan Van den Bergh for their support in the workshop organization.
Organizing Committee Workshop organizers • Ludovico Boratto, (University of Cagliari, Italy) • Lucio Davide Spano, (University of Cagliari, Italy) • Salvatore Mario Carta, (University of Cagliari, Italy) • Gianni Fenu, (University of Cagliari, Italy) Programme Committee • Panagiotis Adamopoulos (Stern School of Business,
New York University, USA) • Mohammad Alshamri (KSA and Ibb University, Yemen) • Marcelo Armentano (Universidad Nacional del Centro
de la Pcia. de Buenos Aires, Argentina) • Pedro G. Campos (Universidad del Bío-Bío, Chile) • Michael D. Ekstrand (Texas State University, USA) • Sampath Jayarathna (Texas A&M University, USA) • Toon De Pessemier (Ghent University, Belgium) • Anisio Mendes Lacerda (CEFET-MG, Brazil) • Denis Parra Santander (Pontificia Universidad Católica
de Chile, Chile) • Sangkeun Lee (Oak Ridge National Laboratory, USA) • Elisabeth Lex (Graz University of Technology, Austria) • Lara Quijano SÃa˛nchez (Carlos III University of Madrid,
Spain) • Mustansar Sulehri (University of Southampton, United
Kingdom) • Christoph Trattner (Graz University of Technology,
Austria) • Hao Wu (School of Information Science and Engi
neerin, China) • Eva Zangerle (University of Innsbruck, Austria) • Yong Zheng (DePaul University, USA)
Persuasive recommender systems - Keynote (invited paper) Markus Zanker Free University of Bozen-Bolzano Interactive Recommending: Framework, State of Research and Future Challenges Benedikt Loepp, Catalin-Mihai Barbu and Jügen Ziegler University of Duisburg-Essen What Can Be Learnt from Engineering Safety Critical Partly-Autonomous Systems when Engineering Recommender Systems Camille Fayollas1, Célia Martinie1, Philippe Palanque1, Eric Barboni1 and Yannick Deleris2 1ICS-IRIT, University of Toulouse 2AIRBUS Operations Beyond De-Facto Standards for Designing Human-Computer Interactions in Configurators Tony Leclercq1, Jean-Marc Davril1, Maxime Cordy2 and Patrick Heymans1 1University of Namur 2Skalup 1 3 14 26 40 44 54