Collaborative group embedding and decision aggregation based on attentive influence of individual members: A group recommendation perspective

A key group decision making task is to aggregate individual preferences. Conventional group decision methods adopt pre-defined and fixed strategies to aggregate individuals' preferences, which can be ineffective due to the varying importance and influence of individual group members. Recent studies have proposed to assign different weights to individual members automatically based on the level of consistency of their ratings with group assessment outcomes. However, they ignored the high-order influence relationship among individual group members on group decision making. In this study, from a group recommendation perspective, we propose a novel collaborative Group Embedding and Decision Aggregation (GEDA) approach by leveraging the graph neural network technique to address those limitations. Specifically, GEDA first deploys a graph convolution operation on user-item interaction and group-item interaction graphs to generate embedding representations of members, groups, and items. A novel multi-attention (MA) module then learns each member's decision weight by simul-taneously considering the relationships among members for aggregating individual preferences into group preferences. The empirical evaluation using two real-world datasets demonstrates the advantage of the proposed GEDA model over the state-of-the-art group recommendation models.