Methods of preference aggregation in group recommender systems

Abstract

The rapid growth of data volumes has led to information overload, which impedes informed decision-making. To solve this
problem, recommender systems have emerged that analyze user preferences and offer relevant products on their own. One type of
recommender system is group recommender systems, which are designed to facilitate collaborative decision-making, increase user
engagement, and promote diversity and inclusion. However, these systems face challenges such as accommodating diverse group
preferences and maintaining transparency in recommendation processes. In this study, we propose a method for aggregating
preferences in group recommendation systems to retain as much information as possible from group members and improve the
accuracy of recommendations. The proposed method provides recommendations to groups of users by avoiding the aggregation
process in the first steps of recommendation, which preserves information throughout the group recommendation process and delays
the aggregation step to provide accurate and diverse recommendations. When the object of a collaborative filtering-based
recommender system is not a single user but a group of users, the strategy for calculating similarity between individual users to find
similarity should be adapted to avoid aggregating the preferences of group members in the first step. In the proposed model, the
nearest neighbors of a group of users are searched, so the method of finding neighbors is adapted to compare individual users with
the group profile. An experimental study has shown that the proposed method achieves a satisfactory balance between accuracy and
diversity. This makes it well suited for providing recommendations to large groups in situations where accuracy is more or less
important compared to diversity. These results support the assumption that retaining all information from group members without
using aggregation techniques can improve the performance of group recommender systems, taking into account various features.