Title :

Improving memory-based user collaborative filtering with evolutionary multi-objective optimization

Author(s) :

Karabadji, Nour El Islem [Auteur]
Beldjoudi, Samia [Auteur]
Seridi, Hassina [Auteur]
Aridhi, Sabeur [Auteur]
Laboratoire Lorrain de Recherche en Informatique et ses Applications [LORIA]
Dhifli, Wajdi [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]

Journal title :

Expert Systems with Applications

Abbreviated title :

Expert Syst. Appl.

Volume number :

98

Pages :

153-165

Publication date :

2018-05-15

ISSN :

0957-4174

English keyword(s) :

Diversity
Collaborative filtering
Recommender systems
Multi-objective optimization
Genetic algorithms

HAL domain(s) :

Sciences du Vivant [q-bio]

English abstract : [en]

The primary task of a memory-based collaborative filtering (CF) recommendation system is to select a group of nearest (similar) user neighbors for an active user. Traditional memory-based CF schemes tend to only focus on ...
Show more >The primary task of a memory-based collaborative filtering (CF) recommendation system is to select a group of nearest (similar) user neighbors for an active user. Traditional memory-based CF schemes tend to only focus on improving as much as possible the accuracy by recommending familiar items (i.e., popular items over the group). Yet, this may reduce the number of items that could be recommended and consequently weakens the chances of recommending novel items. To address this problem, it is desirable to consider recommendation coverage when selecting the appropriate group. This could help in simultaneously making both accurate and diverse recommendations. In this paper, we propose to focus mainly on the growing of the large search space of users’ profiles and to use an evolutionary multi-objective optimization-based recommendation system to pull up a group of profiles that maximizes both similarity with the active user and diversity between its members. In such manner, the recommendation system will provide high performances in terms of both accuracy and diversity. The experimental results on the Movielens benchmark and on a real-world insurance dataset show the efficiency of our approach in terms of accuracy and diversity compared to state-of-the-art competitors.Show less >

Language :

Anglais

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

CHU Lille
Université de Lille

Collections :

• METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694

Submission date :

2019-12-09T18:17:50Z
2024-06-04T12:34:22Z