=Paper=
{{Paper
|id=Vol-1911/13
|storemode=property
|title=Preference Elicitation for Group Recommender Systems
|pdfUrl=https://ceur-ws.org/Vol-1911/13.pdf
|volume=Vol-1911
|authors=Thuy Ngoc Nguyen, Francesco Ricci
|dblpUrl=https://dblp.org/rec/conf/iir/Nguyen017
}}
==Preference Elicitation for Group Recommender Systems==
https://ceur-ws.org/Vol-1911/13.pdf
Preference Elicitation for Group Recommender
Systems
Thuy Ngoc Nguyen and Francesco Ricci
Faculty of Computer Science
Free University of Bozen-Bolzano, 39100 Bolzano, Italy
{ngoc.nguyen,fricci}@unibz.it
Abstract. In group decision making, users’ behaviour are influenced by
their long-term and group-induced preferences. However, how to leverage
them is challenging due to their dynamic nature, which is also dependent
on the specific group settings. In our work, we employ a group recom-
mendation model that utilizes both types of preferences and we analyze
alternative ways of combing them, under diverse group settings. Based
on a custom-designed simulation process, we examine the effect of these
combinations on the model performance. The experimental results demon-
strate that a combination scheme weighing more the long-term preferences
is well adapted to the scenarios where the group setting has no impact on
users’ preferences, but when users tend to be cooperative or when their
preferences diverge in the context of groups, users seem to benefit more
from a recommender that quicker adapts to the group-induced preferences,
which reflect their newly emerging interests.
Keywords: Recommender Systems; Group Recommendations; Conver-
sational Systems; Preference Elicitation.
1 Introduction
Today, the challenge for recommender systems (RSs) is expanding from barely
suggesting items that match individual’s preferences to recommending those sat-
isfying the needs of a group of users [8]. This is derived from real-life cases where
people often participate in activities together with others, e.g., having dinners
with friends or traveling with family. Several methods for supporting a group of
users in making decisions have been incorporated into group recommender sys-
tems (GRSs) [5, 6]. However, most of the existing work in GRSs is based on the
assumption that by knowing the individual preferences alone, the GRS should still
be able to predict the group choice and generate relevant recommendations. For
example, research in [4] provides recommendations to a group by fusing three rec-
ommendation techniques: demographic, content-based and collaborative filtering
but without considering the users’ joint interactions with the system. Conversely,
our work assumes that the knowledge of individual preferences prior to a group
discussion does not suffice, and the system must track the group discussion to
effectively support the group decision making process. In fact, a recent observa-
tional study on group decision processes has confirmed that group preferences are
2 T.N. Nguyen and F. Ricci
constructed during the decision-making process and further stressed that research
in GRSs should put more focus on the process itself rather than on solving group
recommendation problems in a mechanical way [2].
Motivated by these findings, in our previous work we introduced a group rec-
ommendation model that exploits both individual long-term and session-based
preferences. The long-term interests are acquired in the form of item ratings.
While, the session-based preferences, also known as group-induced preferences,
are inferred from users’ feedback on items during the group discussion. The model
was implemented in a GRS that offers a chat environment in which a variety of
decision support and recommendation functions are integrated [7]. The usability
and the perceived recommendation quality of the system were evaluated through
a controlled live user study. Nevertheless, the user study could not fully assess
the system performance, which must be examined under a variety of conditions,
which users are likely to experience in a group setting.
In a follow-up study, we have hypothesized that the relative importance of each
type of preference could vary according to the specific type of the group that needs
to be supported. Hence, we have designed a simulation process in order to analyze
the proposed model under different preference combination strategies and in three
alternative group dynamics settings that match the three kinds of social impact
on users’ behavior that have been identified in [3]: (a) independence - the group
has no effect on the user preferences, (b) conversion - the group setting nudges
group members to be more similar to each other, and (c) anti-conformity - the
group setting causes group members to react negatively, so they tend to diverge.
In these scenarios, three preference combination strategies have been employed:
(i) when the importance of the long-term and session-based preferences is equal,
(ii) when a much stronger importance is given to the long-term preferences, and
(iii) when greater importance is given to the session-based preferences.
Our intuition suggests that the more the users disclose their session-based
preferences, the better the group recommendations become. Thus, we have mea-
sured and observed how the utility of the top recommendation changes when the
amount of elicited preferences in the group discussion grows. We observed that
the proposed model can correctly capture the changes in user preferences and,
as more feedback is provided in the simulated group session, the utility of the
top recommended item converges to that of the assumed group choice. Moreover,
the results show fundamental properties of long-term and session-based prefer-
ence fusion in group recommendations: in the scenarios (a), a GRS requires less
preference information derived from the group discussion while in the scenario
(b) and (c) it must take into account more the session-based preferences to faster
identify the true preferences of the group.
2 Group Recommendation Logic
In our recommendation model, we monitor and utilize the evolving preferences of
users in a group decision making process, and we combine them with long-term
preferences that are acquired before the group discussion. Combined users’ pref-
erences are modeled and continuously updated in the form of utility functions.
Preference Elicitation for Group Recommender Systems 3
The final recommended items are generated by using the group model that aggre-
gates individual preference models; in our case, we use the Average aggregation
function.
(u)
We call w(u) and wG the utility vector that represents the preferences of user
u expressed before and during a discussion of group G, respectively. The vector
(u)
w(u) is determined by using a content-based approach [7]. We call φG the set
of constraints on the user u utility function derived from the evaluations given
by u in the G group session. In order to infer the user utility function from the
(u)
constraints in φG we use a technique that was introduced in [9], and previously
applied only in conversational RSs for individuals [1].
(u)
Next, we search for the utility vector wG that not only satisfies the inferred
(u)
constraints in φG , but also maximizes the cosine similarity of this vector with the
(G)
vector w , the aggregated utility vector of the group. The resulting optimization
problem is formulated as follows:
(u) (u) (u) (u)
wG = arg max cos(wG , w(G) ) s.t. wG sat. φG (1)
Finally, each user utility vector w(u) is updated by taking a linear combination
of the long term and short term utility vectors, weighted by the parameter σ ∈
[0, 1], which is the “stability” of the long-term preferences:
(u)
w(u) = σw(u) + (1 − σ)wG (2)
3 Experiments and Results
In the three mentioned scenarios, we have generated user groups and simulated
users’ behaviors by generating items that they could propose to their group, along
with their evaluations for the proposed items. The detailed description of the used
dataset can be found at [7]. Afterwards, in each scenario, we have investigated the
effect of the stability parameter σ (see Eq. 2) on the recommendation quality, i.e.,
how the utility of the top recommendation changes as a function of the quantity
of user feedback acquired by the system. We recall that the parameter σ is used to
balance the preference knowledge elicited before and during the group interaction
in three scenarios. Particularly, we considered the following cases: σ = 0.1, 0.5,
and 0.9.
Figure 1 shows how, in the three considered scenarios, the true group utility
of the top recommendation converges to the best attainable utility which is the
utility of the simulated group choice (the item with the largest group utility
according to the true, but unknown utilities of the users).
In the independence scenario, if σ = 0.9, the two utilities are exactly the
same. With σ = 0.5, the utility of the top recommended is optimal after each
group member has proposed 7 items, while with σ = 0.1, the true utility of the
top recommendation grows much slower. In general, we notice that if the group
members incrementally reveal their preferences, the system is eventually able to
learn the user needs and adapt to their requirements.
4 T.N. Nguyen and F. Ricci
Independence Conversion Anti−conformity
0.21650 0.21650
0.1976
0.21625 0.21625
Utility
Utility
Utility
0.1972
0.21600 0.21600
0.21575 0.21575 0.1968
0.21550 0.21550
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
The number of proposed items The number of proposed items The number of proposed items
Group choice Top rec sigma = 0.9 Top rec sigma = 0.5 Top rec sigma = 0.1
Fig. 1. The true group utility of the top recommended item and the group choice in the
three scenarios with respect to random groups of 5 users.
In the conversion scenario, when σ = 0.9, the group utilities of the top recom-
mended item and the group choice are no longer the same as in the independence
scenario. But, we can see that as the number of proposed items grows, the utility
of the top recommended item converges faster to the optimum. This illustrates
that if the users have more similar preferences, as expected, the proposed model
will learn faster their true preferences.
In the anti-conformity scenario, the convergence still occurs but at a slower
rate. The results indicate that it is harder to infer the true user profiles if the group
members tend to diverge. Additionally, in contrast to the previous scenarios, the
group utility of the top recommended item tends to converge more quickly to the
optimum when σ is lower.
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