A Proposed Framework for Group-Based Multi-Criteria Recommendations

Abstract

The Multi Criteria Group Recommender System is gaining attention. In the proposed framework, the authors intend to find solutions to two problems. First, group members assign arbitrary ratings to multiple criteria of items; these do not reflect their honest opinions. The proposed framework selects a promising group for the target user on the basis of demographic attributes. In case more than one group claims to be the promising group, the proposed framework implicitly derives multi-criteria ratings of each group member on multiple item attributes. Second, as the group size increases, difficulty in aggregating the preferences of all the group members increases, resulting in errors in group recommendations. The framework generates group recommendations from the nearest neighbor set of group members (GNN Set ), instead of generating group recommendations from all the members of the target group. Cascade TOPSIS is proposed, which selects the GNN Set from the identified group thereby retaining only those group members who are likely to provide good quality group recommendations. TOPSIS on expert group members and nonexpert group members are used to handle group decision making. The preferences of members of the GNN Set are used to generate recommendations for the target user. Using the Movie Lens dataset, significant improvement in recommendation quality is observed.