=Paper=
{{Paper
|id=Vol-1278/paper12
|storemode=property
|title=Choicla: An Intelligent Group Decision Support Environment
|pdfUrl=https://ceur-ws.org/Vol-1278/paper12.pdf
|volume=Vol-1278
|dblpUrl=https://dblp.org/rec/conf/dmrs/StettingerF14
}}
==Choicla: An Intelligent Group Decision Support Environment==
https://ceur-ws.org/Vol-1278/paper12.pdf
Choicla: An Intelligent Group Decision Support
Environment
Martin Stettinger and Alexander Felfernig
Institute for Software Technology,
Inffeldgasse 16b, 8010 Graz, Austria
{martin.stettinger,alexander.felfernig}@ist.tugraz.at
Abstract. Group recommendation technologies have been successfully
applied in domains such as interactive television, music, and tourist des-
tinations. Existing group recommendation environments are focusing on
specific domains and do not offer the possibility of supporting different
kinds of decision scenarios. The Choicla group decision support environ-
ment advances the state of the art by supporting decision scenarios in a
domain-independent fashion. In this paper we give a short overview of
the Choicla group decision support environment.
Keywords: Recommender Systems, Group Recommendation, Group
Decision Making
1 Introduction
Decisions in everyday life often come up in groups, for example, a decision about
the destination for the next holidays or a decision about which restaurant to
choose for a dinner. The quality of many group decisions is negatively influenced
by various factors. So-called anchoring effects [3] are responsible for decisions
which are biased by the voting of the first preference-articulating person. Missing
explanations can lead to a lower level of trust in recommendations [1]. Knowl-
edge about the preferences of other users in early phases of a decision process
as well as limited domain knowledge can lead to sub-optimal decision outcomes
([5]). Decision processes are often not open in the sense that it is impossible to
easily integrate new decision alternatives or change the individual preferences
within the scope of a decision process - these aspects can lead to low-quality
decision outcomes (see [6]). In many cases, the criteria for a decision remain
unclear since there is no explanation of the outcome of ”the final decision”.
One major goal of the Choicla environment is to facilitate group decision making
and improve the overall quality of decision outcomes. The idea of this environ-
ment is to support definitions of different types of decision tasks in a domain-
independent fashion while taking into account the above mentioned risk factors.
In order to achieve this goal, Choicla builds upon different group recommenda-
tion algorithms [4] which are used for determining alternative solutions for the
participants of a group decision process.
2 Martin Stettinger and Alexander Felfernig
2 Choicla Decision Support
Because decision scenarios differ from each other in terms of their process design,
a variety of parameters is needed. Some decision scenarios rely on a preselected
decision heuristic that defines the criteria for taking the decision, for example, a
group decides to use majority voting for deciding about the next restaurant or
cinema visit. The design of decision tasks (the underlying process) can be inter-
preted as a configuration problem (see [8]). Configurable parameters in Choicla
are, for example, the inclusion of explanations, the way of administrating the
decision alternatives, the preference visibility and the recommendation support.
For a more detailed discussion of all the available parameters in Choicla we refer
the reader to [7]. The achieved flexibility of making the process design of a de-
cision task configurable is needed due to the heterogeneity of decision problems.
This way the Choicla components are organized as a kind of a software product
line that is open in terms of the implementation (generation) of problem-specific
decision applications.
After the design process has been finished, the creator of the decision task as
well as all invited participants (after accepting the invitation) can interact with
a Choicla decision app. A decision app is automatically installed on the home
screens of the participants.
Choicla also offers a possibility to search for public available decision apps (if
someone has created an app before and set it public). This technique makes
it possible to reuse a created decision app and thus prevents a creation from
scratch every time - especially for frequent decision tasks such as, for example,
scheduling decision tasks. This reuse technique has the potential to reduce the
entry barrier for using Choicla and keep the interaction simple. Of course there
is also an option for designing a new decision app from scratch.
To keep the potentially large number of decision tasks manageable, every de-
cision app consists of a variable number of instances. A concrete instance of a
decision app can be accessed within the corresponding decision app. This mech-
anism offers the possibility of an exact documentation of all past decisions and
is also a basis for supporting recurring decision tasks.
3 Future Work
Our future work will focus on the analysis of further application domains for
the Choicla technologies. Our vision is to make the creation (design) of domain-
independent group decision tasks as simple and straightforward as possible. The
resulting decision task should be easy to handle for users and make group deci-
sions in general more efficient. Our focus will also be on the analysis of decision
phenomena within the scope of group decision processes. Phenomena such as
decoy effects [2], [9] and anchoring effects [3] have been well studied for single-
user cases, however, in group-based decision scenarios no studies have been con-
ducted. A further issue for future work is to figure out which group recommenda-
tions help to achieve consensus more quickly. Finally, we want to point up that
one of our major goals is to make the Choicla datasets available to the research
community in an anonymized fashion for experimentation purposes.
Workshop on Decision Making and Recommender Systems 2014 3
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