A genetic algorithm solution to the collaborative filtering problem

AR Y., BOSTANCI G. E.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.61, ss.122-128, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 61
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.eswa.2016.05.021
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.122-128
  • Anahtar Kelimeler: Collaborative filtering, Genetic algorithms, Evaluation, Recommender systems, RECOMMENDER SYSTEMS, INFORMATION
  • Ankara Üniversitesi Adresli: Evet

Özet

Development of approaches for reducing the prediction error has been an active research field in collaborative filtering recommender systems since the accuracy of the prediction plays a crucial role in user purchase preferences. Unlike the conventional collaborative filtering methods which directly use the computed user-to-user similarity values, this paper presents a genetic algorithm approach for refining them before using in the prediction process. The approach was found to yield promising results according to the statistical analysis performed on a variety numbers of neighbours for various similarity metrics including Pearson's Correlation, Extended Jaccard Coefficient and Vector Cosine Similarity along with a metric that assigns random weights to be used as a benchmark. Results show that the evolutionary approach has significantly reduced the prediction error using the evolved weights and Vector Cosine Similarity has shown the best performance. (C) 2016 Elsevier Ltd. All rights reserved.