A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings

Abstract

<jats:title>Abstract</jats:title> <jats:p>Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems (GRSs) with full ranking, but partial ranking (PR) where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties (PRWOT). However, the rankings may have ties where some items are placed in the same position, but where some items are partially ranked to be aggregated may not be permutations. In this work, in order to handle problem of PR in GRS for PRWOT and PR with ties (PRWT), we propose a novel approach to GRS based on genetic algorithm (GA) where for PRWOT Spearman foot rule distance and for PRWT Kendall tau distance with bucket order are used as fitness functions. Experimental results are presented that clearly demonstrate that our proposed GRS based on GA for PRWOT (GRS-GA-PRWOT) and PRWT (GRS-GA-PRWT) outperforms well-known baseline GRS techniques.</jats:p>

Implicit Feedback-based Group Recommender System for Internet of Things Applications

“…As preference features of members in a group are usually diversified, how to balance their interest conflicts remains a challenging task. Existing solutions concerning GRS can be classified into two categories: preference aggregation-based approaches [1]- [9] and score aggregation-based approaches [10]- [19]. The former aggregate preference feedbacks (e.g.…”