GCAR: A Group Composite Alternatives Recommender Based on Multi-criteria Optimization and Voting
Hanan Abdullah Mengash, Alexander Brodsky · 2014
This paper proposes a Group Composite Alternatives Recommender (GCAR) framework, which provides recommendations on dynamically defined composite bundles of products and services. This framework is based on: (1) defining the space of alternatives, (2) eliciting the utility function for each individual decision maker, (3) estimating the group utility function, (4) using the group utility function to find an optimal recommendation alternative, (5) constructing a set of diverse recommendations which contains the optimal recommendation alternative, and (6) applying the Instant Runoff Voting (IRV) method, from social choice theories, to refine the recommendations. A preliminary experimental study is conducted which shows that the proposed framework significantly outperforms three popular aggregation strategies normally used for group recommendations.