=Paper=
{{Paper
|id=None
|storemode=property
|title=Preface
|pdfUrl=https://ceur-ws.org/Vol-1050/preface.pdf
|volume=Vol-1050
}}
==Preface==
https://ceur-ws.org/Vol-1050/preface.pdf
Proceedings of the
RecSys 2013
Workshop on
Human Decision Making in Recommender Systems
(Decisions@RecSys’13)
October 12, 2013
In conjunction with the
7th ACM Conference on Recommender Systems
October 12-16, 2013, Hong Kong, China
i
Preface
Users interact with recommender systems to obtain useful information about products or
services that may be of interest for them. But, while users are interacting with a recommender
system to fulfill a primary task, which is usually the selection of one or more items, they are
facing several other decision problems. For instance, they may be requested to select specific
feature values (e.g., camera’s size, zoom) as criteria for a search, or they could have to identify
features to be used in a critiquing based recommendation session, or they may need to select a
repair proposal for inconsistent user preferences when interacting with a recommender. In all
these scenarios, and in many others, users of recommender systems are facing decision tasks.
The complexity of decision tasks, limited cognitive resources of users, and the tendency to keep
the overall decision effort as low as possible is modeled by theories that conjecture “bounded
rationality”, i.e., users are exploiting decision heuristics rather than trying to take an optimal.
Furthermore, preferences of users will likely change throughout a recommendation session, i.e.,
preferences are constructed in a specific decision context and users may not fully know their
preferences beforehand. Within the scope of a decision process, preferences are strongly
influenced by the goals of the customer, existing cognitive constraints, and the personal
experience of the customer. Due to the fact that users do not have stable preferences, the
interaction mechanisms provided by a recommender system and the information shown to a
user can have an enormous impact on the outcome of a decision process.
Theories from decision psychology and cognitive psychology have already elaborated a number
of methodological tools for explaining and predicting the user behavior in these scenarios. The
major goal of this workshop is to establish a platform for industry and academia to present and
discuss new ideas and research results that are related to the topic of human decision making in
recommender systems. The workshop consists of a mix of six presentations of papers in which
results of ongoing research as reported in these proceedings are presented and two invited
talks: Bart Knijnenburg presenting “Simplifying privacy decisions: towards interactive and
adaptive solutions” and and Jill Freyne and Shlomo Berkovsky presenting: “Food
Recommendations: Biases that Underpin Ratings”. The workshop is closed by a final discussion
session.
Li Chen, Marco de Gemmis, Alexander Felfernig, Pasquale Lops,
Francesco Ricci, Giovanni Semeraro and Martijn Willemsen
September 2013
ii
Workshop Committee
Workshop Co-Chairs
Li Chen, Hong Kong Baptist University
Marco de Gemmis, University of Bari Aldo Moro, Italy
Alexander Felfernig, Graz University of Technology, Austria
Pasquale Lops, University of Bari Aldo Moro, Italy
Francesco Ricci, University of Bozen‐Bolzano, Italy
Giovanni Semeraro, University of Bari Aldo Moro, Italy
Martijn Willemsen, Eindhoven University of Technology, Netherlands
Organization
Gerald Ninaus, Graz University of Technology
Program Committee
David Amid, IBM Haifa Research Center
Shlomo Berkovsky, NICTA
Robin Burke, DePaul University
Li Chen, Hong Kong Baptist University
Marco De Gemmis, Dipartimento di Informatica – University of Bari
Alexander Felfernig, Graz University of Technology
Gerhard Friedrich, Alpen-Adria-Universitaet Klagenfurt
Sergiu Gordea, AIT
Anthony Jameson, DFKI
Dietmar Jannach, TU Dortmund
Bart Knijnenburg, University of California, Irvine
Gerhard Leitner, Alpen-Adria-Universitaet Klagenfurt
Pasquale Lops, University of Bari
Gerald Ninaus, Graz University of Technology
Florian Reinfrank, Graz University of Technology
Francesco Ricci, Free University of Bozen-Bolzano
Giovanni Semeraro, Dipartimento di Informatica – University of Bari
Ofer Shir, IBM Research
Erich Teppan, Alpen-Adria-Universitaet Klagenfurt
Martijn Willemsen, Eindhoven University of Technology
Markus Zanker, Alpen-Adria-Universitaet Klagenfurt
iii
Table of Contents
Accepted papers
Efficiency Improvement of Neutrality-Enhanced Recommendation
Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh and Jun Sakuma 1
Towards User Profile-based Interfaces for Exploration of Large Collections of Items
Claudia Becerra, Sergio Jimenez and Alexander Gelbukh 9
Selecting Gestural User Interaction Patterns for Recommender Applications on Smartphones
Wolfgang Wörndl, Jan Weicker and Béatrice Lamche 17
The Role of Emotions in Context-aware Recommendation
Yong Zheng, Bamshad Mobasher and Robin Burke 21
Managing Irrelevant Contextual Categories in a Movie Recommender System
Ante Odić, Marko Tkalcic and Andrej Kosir 29
An Improved Data Aggregation Strategy for Group Recommendations
Toon De Pessemier, Simon Dooms and Luc Martens 36
Invited presentations
Simplifying privacy decisions: towards interactive and adaptive solutions
Bart Knijnenburg 40
Food Recommendations: Biases that Underpin Ratings
Jill Freyne and Shlomo Berkovsky 42
Copyright © 2013 for the individual papers by the papers' authors. Copying permitted for
private and academic purposes. This volume is published and copyrighted by its editors.
iv