RecSys’12 Workshop on Human Decision
Making in Recommender Systems
Marco de Gemmis
Alexander Felfernig
University of Bari Aldo Moro
Via E. Orabona, 4, Bari, Italy
[email protected]
Graz University of Technology
Inffeldgasse 16b/2
Graz, Austria
[email protected]
Francesco Ricci
Giovanni Semeraro
University of Bolzano
Piazza Domenicani 3,
Bozen-Bolzano, Italy
[email protected]
University of Bari Aldo Moro
Via E. Orabona, 4, Bari, Italy
[email protected]
Pasquale Lops
University of Bari Aldo Moro
Via E. Orabona, 4, Bari, Italy
[email protected]
Martijn C. Willemsen
Eindhoven University of Technology
P.O. Box 513
5600MB Eindhoven
[email protected]
specific decision environment and are therefore likely to
change throughout a recommendation session or between
sessions, due to contextual effects. Decision making under
bounded rationality is a door opener for different types of
non-conscious influences on the decision behavior of a
user. Theories from decision psychology and cognitive
psychology are trying to explain these influences, for
example, decoy effects [2] and defaults [3] can trigger
significant shifts in item selection probabilities; in group
decision scenarios [4], the visibility of the preferences of
other group members can have a significant impact on the
final group decision.
ABSTRACT
Interacting with a recommender system means to take different
decisions such as selecting an item from a recommendation list,
selecting a specific item feature value (e.g., camera’s size, zoom)
as a search criteria, selecting feedback features to be critiqued in a
critiquing based recommendation session, or selecting a repair
proposal for inconsistent user preferences when interacting with a
knowledge-based recommender. In all these situations, users face
a decision task. This workshop (Decisions@RecSys) focuses on
approaches for supporting effective and efficient human decision
making in different types of recommendation scenarios.
Categories and Subject Descriptors
The major goal of this workshop was 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 consisted of technical sessions in which results
of ongoing research were presented, informal group
discussions on focused topics, and a keynote talk.
H.3.3 [Information Search and Retrieval]; H.4.2 [Decision
Support Systems]
General Terms
Algorithms, Experimentation, Human Factors, Theory.
Keywords
The topics of papers submitted to the workshop can be
summarized as follows:
Recommender Systems, Decision Making, Decision Psychology,
Recommender Algorithms, Decision Biases.

Avoidance of decision biases: decision biases can lead
to suboptimal user decisions – the detection of
potential biases can significantly improve the
perceived quality of recommender systems.

Intelligent preference elicitation: the understanding of
user preferences is a major precondition for the determination of relevant recommendations. Research areas
covered by this year’s submissions are opinion formation, preference learning, and preference relaxation.

Emotions in recommender systems: emotions play a
major role in the context of human decision making
since the emotional state has a major impact on the
decision outcome. Related submissions are tackling the
1. OVERVIEW
The complexity of decision tasks, limited cognitive
resources of users, and the tendency to keep the overall
decision effort as low as possible lead to the phenomenon
of bounded rationality [5], i.e., users employ decision
heuristics rather than trying to take an optimal decision.
Furthermore, preferences of users are usually not stable
entities easily retrieved from memory when requested.
More often, preferences are constructed [1] while in a
Copyright is held by the author/owner(s).
RecSys’12, September 9–13, 2012, Dublin, Ireland.
ACM 978-1-4503-1270-7/12/09.
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challenge of integrating knowledge about emotions
into the underlying recommendation processes.


3. REFERENCES
[1] R. Bettman, M. Luce, and J. Payne. Constructive
Consumer Choice Processes, Journal of Consumer
Research, 25:187-217, 1998.
New application domains: the variety of application
domains for recommendation technologies is
increasing. An example domain is software
engineering where recommender systems are applied
to support processes such as requirements engineering,
software quality assurance, and software reuse.
[2] J. Huber, W. Payne, and C. Puto. Adding
Asymmetrically Dominated Alternatives: Violations of
Regularity and the Similarity Hypothesis, Journal of
Consumer Research, 9:90-98, 1982.
[3] M. Mandl, A. Felfernig, J. Tiihonen, and K. Isak.
Status Quo Bias in Configuration Systems, 24th
International Conference on Industrial, Engineering &
Other Applications of Applied Intelligent Systems
(IEA/AIE 2010), Syracuse, NY, 105-114, 2011.
Empirical studies: the design of recommender user
interfaces can have a major impact on the outcome of
a decision processes. The results of related empirical
studies have been reported in a couple of submissions
to this year’s Decisions@RecSys workshop.
[4] J. Masthoff. Group recommender systems: Combining
individual models. In F. Ricci, L. Rokach, B. Shapira,
and P. Kantor, editors, Recommender Systems
Handbook, pages 677-702. Springer, 2011.
2. FURTHER INFORMATION
The workshop material (list of accepted papers, invited
talk, and the workshop schedule) can be found at the
Decisions@RecSys
2012
workshop
webpage:
http://recex.ist.tugraz.at/RecSysWorkshop2012.
[5] H. Simon. A Behavioral Model of Choice. Quarterly Journal
of Economics, 69(1): 99-118, 1955.
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