Tailoring Group Package Recommendations to Large Heterogeneous Groups Based on Multi-criteria Optimization

A group package recommender framework is proposed to provide recommendations on dynamically defined packages of products and services to large heterogeneous groups based on multi-criteria optimization. The framework is based on: (1) sampling the entire large group; (2) eliciting the utility function for each member; (3) clustering the sample heterogeneous group into a number of relatively small homogeneous subgroups; (4) extracting the representative utility function for each subgroup; (5) estimating the utility function of the entire group, and use it to find an optimal recommendation alternative; (6) diversify recommendations across those subgroups; (7) applying a group decision-making method, to refine the recommendations. A preliminary experimental study is conducted, which shows that the proposed framework is able to produce a small set of ranked recommendations that retains close to optimal precision and recall, as compared to the baseline method applied directly to original lar...

• Type: Journal
• Year: 2016
• Where: HICSS
• Authors: Hanan Mengash, Alexander Brodsky
• Keywords: Biometrics, HICSS 2016