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 recommendation 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 demonstrate 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.
Today, the challenge for recommender systems (RSs) is expanding from barely
suggesting items that match individual’s preferences to recommending those
satisfying the needs of a group of users [
Motivated by these findings, in our previous work we introduced a group
recommendation 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 [
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 [
Our intuition suggests that the more the users disclose their session-based preferences, the better the group recommendations become. Thus, we have measured 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 preference 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
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’ preferences are modeled and continuously updated in the form of utility functions.
Preference Elicitation for Group Recommender Systems The final recommended items are generated by using the group model that aggregates individual preference models; in our case, we use the Average aggregation function.
(u) the utility vector that represents the preferences of user
We call w(u) and wG
u expressed before and during a discussion of group G, respectively. The vector
(u) the set
w(u) is determined by using a content-based approach [
Next, we search for the utility vector wG(u) that not only satisfies the inferred constraints in φ(Gu), but also maximizes the cosine similarity of this vector with the vector w(G), the aggregated utility vector of the group. The resulting optimization problem is formulated as follows: wG(u) = arg max cos(wG(u), w(G)) s.t. wG(u) sat. φ(Gu) (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 ∈
[
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 [
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.
Independence
Conversion
Anti−conformity 0.21650 0.21625 0.21550 0.21650 0.21625 0.21550 0.1976 0.1968 1 2 3 4 5 6 7 8 9 10 The number of proposed items
In the conversion scenario, when σ = 0.9, the group utilities of the top recommended 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.