=Paper=
{{Paper
|id=Vol-3177/paper13
|storemode=property
|title=Group Dynamic and Group Recommender Systems for Decision Support
|pdfUrl=https://ceur-ws.org/Vol-3177/paper13.pdf
|volume=Vol-3177
|authors=Hanif Emamgholizadeh,Francesco Ricci
|dblpUrl=https://dblp.org/rec/conf/iir/Emamgholizadeh022
}}
==Group Dynamic and Group Recommender Systems for Decision Support==
https://ceur-ws.org/Vol-3177/paper13.pdf
Group Dynamic and Group Recommender Systems
for Decision Support
Hanif Emamgholizadeh1 , Francesco Ricci1
1
Free University of Bozen-Bolzano, Italy
Abstract
Making a choice that equally satisfies all group members is challenging and time-consuming. In fact, group
decision making is a complicated and time-consuming process that may involve group members with
different preferences and personalities. To deal with this challenge, novel types of group recommender
systems are emerging. The main objective of our research is to develop technologies that can help
groups to make justifiable and fair choices, in a short amount of time (limited costs). We are therefore
addressing three questions related to group recommender systems: (i) how to predict a group choice
by leveraging data related to the group dynamic, (ii) how to design a conversational system that can
help groups to make better choices, and (iii) how to support groups while the state of the group and
the group/system interaction is evolving. We believe that a conversational group recommender system
can use the predicted group choice to interact more effectively with the group. But, in order to do that
the system should understand the dynamic of the group and in particular how the group preferences
evolve during the group discussion. The conversational group recommender system should use this
information to support the groups in different dynamically evolving states. Our research attempts to
address these questions.
Keywords
Recommender System, Group Recommender System, Decision Making, Group Decision Making
1. Introduction
Recommender Systems (RSs) are software tools and techniques that recommend relevant and
specific items to each user [1]. These tools have been designed to help people cope with
information overload in their individual decision-making (DM) processes. However, there
are circumstances in which DM is performed by a group of people, while searching for items
that may suit the whole group, like finding a movie to watch with some friends or parents.
Groups require some specific support during their DM process. To help such groups, Group
Recommender Systems (GRSs) have been introduced, with the initial goal to recommend relevant
items to a group of people [2].
For several decades, small groups have been the research subject of social psychologists
[3]. Social psychologists have shown that though two minds are better than one, the group
discussion may end up with a wrong decision due to decisional biases, unshared information, and
groupthink [4]. A wrong decision can be defined as one that does not meet the group expectation
[4]. Therefore, groups need some support in their DM process to tackle these problems, whereas
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most of the existing GRSs overlook these problems and they just generate recommendations for
items.
GRSs have been proposed to help groups in different domains, like music and movies [5, 6].
But, another limitations of these state of the art applications is related to the fact that group
members’ preferences are considered to be constant during the DM process. But, as already
indicated many years ago by Masthoff et al. [7], group members’ preferences and satisfaction
can change during the group discussion, before the decision is made. Hence, a group profile
should evolve during the discussion, and to successfully support groups, GRSs must take into
account group dynamics in their methods. Forsyth [4] divides the process of a group DM into
four stages: Orientation (defining the problem), Discussion (conducting a discussion), Decision
(making a decision), Implementation (carrying out the decision).
Hence, to effectively support groups, the GRS should initially create group profiles and,
while monitoring the group activity, revise the group members’ profiles, based on the members’
actions and the new elicited preferences. STSGroup [8] is one of a few applications that monitor
the group during the group discussion and support the group members in their DM process. This
system exploits short-term and long-term preferences to help groups by recommending relevant
items and exploits an aggregation method to rank the items for producing new recommendations.
One of the drawbacks of this system is that it supports groups by only suggesting relevant items
and recommending one of the suggested items as the group’s final choice. Although ranking
items and recommending them is important, as explained in [4, 9], recommendations are not the
only support that a group requires. Groups need to be helped during orientation and discussion
stages in addition to decision stage.
A good choice for a group must have the following criteria (i) Good outcomes (minimizing
group members’ dissatisfaction), (ii) Limited costs (shorter time), and (iv) Justifiability (expla-
nation of why the choice is good for group) [9]. Additionally, the system should decrease the
unfairness of the group choice. Fairness can be defined as the variance of group members’
dissatisfaction with respect to an item [10].
In our research1 , we aim at the following goals: (i) to predict a group choice by leveraging data
related to the group dynamic, (ii) to design a conversational system that can help groups to make
better choices, and (iii) to support groups while the state of the group and the group/system
interaction is evolving. Conversational GRSs can use the predicted choice of the group to interact
more effectively with groups, for instance, by reducing the candidate set to the items that are not
significantly different from the predicted choice, thereby reducing the DM time. Conversational
GRSs, in addition to suggesting items in several rounds, can capture the group dynamic and
produce new recommendation in each round to increase the group satisfaction. The supporting
functionalities of the developed conversational RS should take the group dynamic into account
to better support groups. In other words, the provided support should be adapted to the group
dynamic for effectively supporting groups.
1
This paper is a summary of our previous paper [11]
Figure 1: Comparing the ML-based methods accuracies with standard aggregation methos
2. Results and Contributions
In the first step of our research, we propose to use Machine Learning (ML) for predicting the
group choice. We note that classical preference aggregation methods (such as Borda count or
Copeland rule) can also be used to predict a group choice by first computing group’s scores,
i.e., a score for each option that represents how much the considered preference aggregation
strategy estimates that the option may suit the group. Then, the option with the maximum group
score is predicted as the group’s final choice. These strategies do not consider the evolution of
preferences during the discussion and the effect of the discussion stage. On the other hand, Social
Decision Scheme (SDS) theory [12] tries to predict group choice using some basic ingredients:
individual preferences, group profile, social influence, and collective responses. This method
considers group influence that can happen during the group discussion and predict group choice
by taking into account this influence.
In our proposed ML-based method, the system learns (predicts) the social decision schema
that determines the group choice by relying on a data set of observed groups discussions and
choices (individual preferences for options and final choice of the group). In our approach, the
group profiles constructed by one of the aggregation methods and group final choices are used
to train a model to predict the final choice of the group. Hence, the group profiles, created by
a standard aggregation, are the input of the model, and group choices are the output classes
(Logistic regression model). Next, the trained model is used to predict the choice of a new
group, when only the individual preferences of the group members are known. This is clearly
different from the mechanical application of the preference aggregation methods that aggregate
the individual preferences and predict item with the maximum group score as the final choice.
We evaluate our methods using data collected by Delic et al. [13]. This dataset includes 79
groups and 282 participants and contains individual ratings and groups’ choices. We found that
all our proposed ML-based models perform better than the corresponding baseline strategies
(Figure 1). We used three standard aggregation methods and two additional methods motivated
by SDS theory to create group profiles: (i) Average (AVE): averaging individual ratings, (ii)
Multiplicative (MULT): multiplying individual ratings, (iii) Least misery (LM): selecting the
minimum rating of group members for an option as group score, and (iv) SDSk: a group’s score
for an option is calculated by counting the number of times that item is among the most 𝑘
preferred one by the group members. Figure 1 shows that for all of the methods, the accuracy
of the proposed variants of our choice prediction model (called GPCP, for Group Profile Choice
Prediction) performs better than the corresponding baseline method.
3. Conclusion and Future Work
In this research, we aim to address three questions regarding group decision-making and the
usage of group recommender systems: (i) how to predict a group choice by leveraging data
related to the group dynamic, (ii) how to design a conversational system that can help groups
to make better choices, and (iii) how to support groups while the state of the group and the
group/system interaction is evolving. Conversational group recommender systems can use
the predicted group choice to interact more effectively with groups. Thereby, the system can
help groups to reduce decision-making costs (e.g., time). Conversational group recommender
systems are conjectured to be better than other approaches in the capability to capture the
group’s dynamic state, since group members express various types of feedback during their
interaction with the system. The supporting functionalities can use this information to help
groups in different group dynamic states. In the future, we want to extend our prediction model
to be used when we have many options for groups. We also aim to develop a conversational GRS
which is able to detect group dynamic. Then, we want to unobtrusively detect important group
members’ roles, namely, leaders, influencers, and experts. This roles will be used to effectively
and efficiently support groups based on the group members’ roles and current dynamic state of
groups.
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