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

Ritu Meena,Kamal K Bharadwaj

doi:10.1515/jisys-2017-0561

Abstract

Abstract 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.

Highlights

Recommender systems (RSs) are one of the information overloaded web personalization tools to manage the web and provide users those items that best fit their individual needs
In order to handle problem of partial ranking (PR) in group recommender systems (GRSs) for PR without ties (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
We have performed experiments for Scheme 1 using synthetic data sets because the type of data required for this Scheme is not publicly available, whereas for Scheme 2 MovieLens data set is used, which contains 12,832 ratings provided by 1043 users for 1682 movies; we generated list of users who have rated at least 12 movies, which provide five random splits (S), S-1, S-2, S-3, S-4, and S-5, where for each split, 20 users were randomly selected as active users

Summary

Introduction

Recommender systems (RSs) are one of the information overloaded web personalization tools to manage the web and provide users those items that best fit their individual needs. Most recommender systems make recommendations of the group consumable selected items (e.g. movies) to the individual. There have been several efforts in the area of group recommender systems (GRSs). GRSs [4, 15] consider preferences of each member of a group and provide such recommendations to groups so that suggested list of items (or a single recommended item) satisfies the group members optimally. The main concern is how to effectively aggregate the preferences of individuals in a group to produce recommendations that will satisfy a group of users

Objectives

Results

Conclusion