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Expert systems with applications v.98, 2018년, pp.153 - 165   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

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

Karabadji, Nour El Islem (Corresponding author at: LabGED, Badji Mokhtar University, PO Box 12, Annaba 23000, Algeria. ) ; Beldjoudi, Samia (High School of Industrial Technologies, P.O. Box 218, Annaba 23000, Algeria ) ; Seridi, Hassina (Electronic Document Management Laboratory (LabGED), Badji Mokhtar-Annaba University, P.O. Box 12, Annaba, Algeria ) ; Aridhi, Sabeur (University of Lorraine, LORIA, Campus Scientifique, BP 239, Vandoeuvre-lès-Nancy 54506, France ) ; Dhifli, Wajdi (University of Lille, EA2694, 3 rue du Professeur Laguesse, BP 83, Lille Cedex 59006, France ) ;
  • 초록  

    Abstract 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. Highlights Memory-based collaborative filtering method is improved through a genetic algorithm. Multi-objective optimization is applied to find the appropriate group of neighbors. We propose an encoding that allows considering all possible neighbors. Our approach ensures accuracy and diversity of recommendations. We show the efficiency of our approach on benchmark and real-world datasets.


  • 주제어

    Recommender systems .   Collaborative filtering .   Genetic algorithms .   Multi-objective optimization .   Diversity.  

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