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https://ceur-ws.org/Vol-1945/enchires2017_preface.pdf
Preface on the Second Workshop on Engineering
Computer-Human Interaction in Recommender
Systems (EnCHIReS 2017)
A recommender system suggests items that might be interesting for the users, by
analyzing their previous preferences. While these preferences can be explicitly
expressed in the form of ratings or likes, the interactions of the users with the
system can also be exploited, in order to collect implicit preferences and provide
more fine-grained and objective knowledge on what the users are experiencing [2].
Therefore, the user interface engineering community can play a crucial role
in the design of more e↵ective recommender systems. Indeed, it is important to
move from the perception of a recommender system as a black box that provides
suggestions that are not interpretable and are completely disconnected from the
user model, since this would lead to a lack of trust of the users in the system [3].
It is also widely-known that a current challenge in the recommender systems
research is to go beyond accuracy, since the acceptance of a recommendation
by the user is related to a set of other factors, such as the way in which the
recommended items are presented to the user [1, 4–9]. Therefore, an analysis of
the capability of the user interface to improve both the e↵ectiveness and the
understanding of the recommendations is an aspect of central interest in this
research area.
Moreover, the possibility to allow the users to tailor the recommendations to
their current needs is essential. This dynamical adaptation to the users can be
pursued by o↵ering means to let a user express what she is currently interested
in and is expecting from the system, or by inferring these needs by monitoring
her interactions with it. Being able to control the user model, in order to discard
outdated preferences or preferences that are related to other users (e.g., when a
profile is used to buy a gift for another person) is another crucial aspect.
The user interface also plays a crucial role when visualizing or communicating
risks in recommendation domains such as health and medicine.
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. The papers in this workshop proceedings volume present di↵erent re-
sults and ongoing research on the following topics:
– Design patterns, metaphors, and innovative solutions for the end-user in-
spection 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 appli-
cations that exploit Recommender Systems;
II
– Feature selection and data filtering approaches to extract information from
the data gathered through Human-Computer Interaction techniques, for rec-
ommendation purposes;
– Analysis of implicit data collected from real-world systems, in order to eval-
uate their e↵ectiveness for recommendation and personalization purposes.
The workshop was an event co-located with the 9th ACM SIGCHI Sym-
posium on Engineering Interactive Computing Systems (EICS 2017). After the
review process, the programme committee selected 6 papers. In addition, Mar-
tijn C. Willemsen was invited to give a talk on understanding user preferences
and goals in recommender systems. We thank all the authors for their submis-
sions and all members of the program committee. We are grateful to the EICS
workshop chairs Teresa Romão and Lucio Davide Spano for their support in the
workshop organization.
September 2017 Ludovico Boratto
Salvatore Carta
Gianni Fenu
Organization
The workshop was organized by the Digital Humanities unit at Eurecat (Spain)
and by the Department of Mathematics and Computer Science at the University
of Cagliari (Italy).
Workshop Organizers
– Ludovico Boratto (Eurecat, Spain)
– Salvatore Carta (University of Cagliari, Italy)
– Gianni Fenu (University of Cagliari, Italy)
Program Committee
– Christine Bauer (Johannes Kepler University Linz, Austria)
– André Calero Valdez (RWTH-Aachen University, Germany)
– Li Chen (HKBU Faculty of Science, Hong Kong)
– Ed H. Chi (Google Research, USA)
– Maria Laura Clemente (CRS4, Italy)
– Michael D. Ekstrand (Boise State University, USA)
– Mehdi Elahi (Free University of Bozen-Bolzano, Italy)
– Mark Graus (Eindhoven University of Technology, Netherlands)
– Bart Knijnenburg (Clemson University, USA)
– Tobias Ley (Tallinn University, Estonia)
– John O’Donovan (University of California, Santa Barbara, USA)
– Denis Parra (Pontificia Universidad Catolica de Chile, Chile)
– Roberto Saia (University of Cagliari, Italy)
– Olga C. Santos (UNED, Spain)
– Giovanni Stilo (Sapienza University of Rome, Italy)
– Wolfgang Woerndl (TU Mnchen, Germany)
Table of Contents
Understanding user preferences and goals in recommender systems . . . . . . 1
Martijn C. Willemsen
Testing a Recommender System for Self-Actualization . . . . . . . . . . . . . . . . . 3
Daricia Wilkinson, Saadhika Sivakumar, Pratitee Sinha, Bart P.
Knijnenburg
Towards a Design Space for Personalizing the Presentation of
Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Catalin-Mihai Barbu, Jürgen Ziegler
A Framework for Comparing Interactive Route Planning Apps in Tourism 18
Sergejs Pugacs, Sven Helmer, Markus Zanker
Towards Chatbots as Recommendation Interfaces . . . . . . . . . . . . . . . . . . . . . 26
Maurizio Atzori, Ludovico Boratto, Lucio Davide Spano
Twixonomy Visualization Interface: How to Wander Around User
Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Giorgia Di Tommaso, Giovanni Stilo
A List of Pre-Requisites to Make Recommender Systems Deployable in
Critical Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
E. Bouzekri, A. Canny, C. Fayollas, C. Martinie, P. Palanque, E.
Barboni, Y. Deleris, C. Gris
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