Analysis of Strategies for Building Group Profiles

Abstract

Abstract. Today most of existing personalization systems (e.g. content recommenders, or targeted ad) focus on individual users and ignore the social situation in which the services are consumed. However, many human activities are social and involve several individuals whose tastes and expectations must be taken into account by the service providers. When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate content and services to a group of users based on their individual profiles. In this paper, we consider an approach intended to determine the factors that influence the choice of an aggregation strategy. We present a preliminary evaluation made on a real large-scale dataset of TV viewings, showing how group interests can be predicted by combining individual user profiles through an appropriate strategy. The conducted experiments compare the group profiles obtained by aggregating individual user profiles according to various strategies to the "reference" group profile obtained by directly analyzing group consumptions.

A group package recommender based on learning group preferences, multi-criteria decision analysis, and voting

“…It includes the simple multi-attribute rating technique (SMART) (Edwards 1977;Edwards and Barron 1994), which is the simplest form of the MAUT methods: the technique for order preference by similarity to ideal solution (TOPSIS) (Hwang and Yoon 1981); and the analytic hierarch process (AHP) (Saaty 1980), based on pairwise comparisons to determine the criteria weights. The two main families of the outranking methods are ELECTRE (Roy 1968(Roy , 1978 and PROMETHEE methods (Brans and Vincke 1985;Brans et al 1986).…”

Dominance Weighted Social Choice Functions for Group Recommendations

“…The latter has been widely studied in Mathematics, Economics and Multi-agent systems (MAS), with the definition of Social Choice functions. These strategies, according to (Senot et al, 2010), can be classified as majority-based (mainly implemented as voting mechanisms to determine the most popular choices among alternatives), consensus-based (that try to average among all the possible choices and preferences), and role-based (that explicitly take into account possible roles and hierarchical relationships among members). PolyLens (O'Connor et al, 2001) has been one of the first approaches to include social characteristics (such as the nature of a group, the rights of group members, and social value functions for groups) within the group recommendation process.…”

Combining Multiple Metadata Types in Movies Recommendation Using Ensemble Algorithms

“…According to Senot et al [21] there are three categories of strategies, namely majoritybased, which strength the "most popular" choice among the group, e.g. Borda Count and Plurality Voting strategies; Consensus-based strategies, which average somehow all the available choices, e.g.…”