Abstract
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.