KİŞİSELLEŞTİRİLMİŞ ÜRÜN ÖNERİ SİSTEMİ İÇİN KULLANICI BAZLI İŞBİRLİKÇİ FİLTRELEME VE KÜMELEME KULLANAN HİBRİT BİR YAKLAŞIM

Year 2022, Volume: 21 Issue: 43, 253 - 268, 15.06.2022

Hüseyin Budak , Enis Gumustas

https://doi.org/10.46928/iticusbe.1055162

Abstract

Günümüz rekabet koşulları firmaları, özellikle perakende ve e-ticaret firmalarını, müşterilerini daha iyi tanımaya, onların tercihlerini ve davranışlarını anlamaya, ihtiyaçlarını tahmin etmeye; böylelikle, onlara kendilerini özel hissettirecek, teklifler sunmaya zorlamaktadır. Firmaların söz konusu kişiselleştirme ihtiyaçlarını karşılayabilmeleri adına kullandıkları yöntemlerden biri ürün öneri sistemleridir.
Amaç: Çalışmada, kişiselleştirilmiş ürün öneri sistemleri için literatürde ve iş dünyasında sıklıkla kullanılan yöntemlerden biri olan kullanıcı bazlı işbirlikçi filtreleme yöntemini iyileştirmek adına k-means ile kullanıcı bazlı işbirlikçi filtreleme algoritmalarını birlikte kullanan hibrit bir yaklaşım önerilmesi amaçlanmıştır.
Yöntem: Kullanıcı bazlı işbirlikçi filtreleme ve k-means yöntemleri kullanılmıştır.
Bulgular: Mevcut yöntem ve önerilen yöntem iki farklı veri seti için uygulanmıştır. Yöntemlerin karşılaştırılması amacıyla veri setleri %80’i eğitim, %20’si de test seti olmak üzere ikiye ayırılmış ve eğitim verisi üzerinden kurulan modellerin test verisindeki hataları (RMSE) hesaplanmıştır. Yapılan karşılaştırma sonucunda her iki veri setinde de önerilen yönteme ilişkin hata değeri daha az olduğu görülmüştür.
Özgünlük: Bu çalışma ile sadece kullanıcı-ürün skorları üzerinden çalışan kullanıcı bazlı işbirlikçi filtreleme yöntemine kullanıcılara ilişkin farklı bilgileri de kullanabilen bir yaklaşım önerilmiştir. Ayrıca, önerilen yöntem literatürde sıklıkla kullanılan MovieLens veri setinden uygulanmasının yanı sıra gerçek bir süpermarket verisinde de uygulanmıştır.

Keywords

Öneri Sistemleri, Kümeleme, Kullanıcı Bazlı İşbirlikçi Filtreleme, K-Means

A HYBRID APPROACH USİNG USER-BASED COLLABORATIVE FILTERING AND CLUSTERING FOR PERSONALIZED PRODUCT RECOMMENDATION SYSTEM

Abstract

Nowadays competition conditions are forced companies, especially retail and e-commerce companies, to know their customers better, to understand their preferences and behaviours, to predict their needs, in this way, to make offers that feel them special. One of the methods used by companies to meet their personalization needs is product recommendation systems.
Purpose: In the study, it is aimed to propose a hybrid approach that uses k-means and user-based collaborative filtering algorithms together to improve the user-based collaborative filtering method, which is one of the most frequently used methods in the literature and business world for personalized product recommendation systems.
Method: User-based collaborative filtering and k-means methods are used.
Findings: The current method and the proposed method were applied for two different data sets. In order to compare the methods, the data sets were divided into two as 80% training and 20% test set, and the errors (RMSE) of the models built on the training data were calculated. As a result of the comparison, it was seen that the error value for the proposed method was less in both data sets.
Originality: In this study, an approach that can also use different information about users is proposed to the user-based collaborative filtering method, which works only on user-product scores. In addition, the proposed method has been applied to a real supermarket data as well as being applied from the MovieLens dataset, which is frequently used in the literature.

Keywords

Recommendation Systems, Clustering, User-Based Collaborative Filtering, K-Means

References

• Akpınar, H. (2014). Data veri madenciliği veri analizi. İstanbul: Papatya Yayıncılık Eğitim.
• Anwar, T., & Uma, V. (2021). Comparative study of recommender system approaches and movie recommendation using collaborative filtering. International Journal of System Assurance Engineering and Management, 12(3), 426-436.
• Awan, M. J., Khan, R. A., Nobanee, H., Yasin, A., Anwar, S. M., Naseem, U., & Singh, V. P. (2021). A Recommendation engine for predicting movie ratings using a big data approach. Electronics, 10(10), 1215.
• Bulut, H., & Milli, M. (2016). İşbirlikçi filtreleme için yeni tahminleme yöntemleri. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 22(2), 123-128.
• Castelo-Branco, F., Reis, J. L., Vieira, J. C., & dos Santos, J. P. M. (2021). Business intelligence in sports retail: data mining application, 16th Iberian Conference on Information Systems and Technologies (CISTI), 23-26 June 2021.
• Chen, Y. Z., & Lai, Y. C. (2016). Universal structural estimator and dynamics approximator for complex networks. ArXiv E-prints, arXiv-1611.
• Chung, K. R., Park, K. R., & Park, S. H. (2021). Design and implementation of a music recommendation model through social media analytics. Journal of Convergence for Information Technology, 11(9), 214-220.
• Demir, F. O., & Kırdar, Y. (2007). Müşteri ilişkileri yönetimi: crm. Review of Social, Economic & Business Studies, 8, 293-308.
• Do, M. P. T., Nguyen, D. V., & Nguyen, L. (2010). Model-based approach for collaborative filtering. The 6th International Conference on Information Technology for Education, 217-228, 18-20 August 2010.
• Ekstrand, M. D., Riedl, J. T., & Konstan, J. A. (2011). Collaborative filtering recommender systems. Foundations and Trends in Human–Computer Interaction, 4(2), 81-173.
• Falk, K. (2019). Practical recommender systems. New York: Manning Publications.
• Gao, Y., Zhao, H., Zhou, Q., Qiu, M., & Liu, M. (2020). An improved news recommendation algorithm based on text similarity. 3rd International Conference on Smart BlockChain (SmartBlock), 132-136, 23-25 October 2020.
• Gupta, M., Thakkar, A., Gupta, V., & Rathore, D. P. S. (2020). Movie recommender system using collaborative filtering. International Conference on Electronics and Sustainable Communication Systems (ICESC), 415-420, 2-4 July 2020.
• Gupta, S., & Dave, M. (2021). A hybrid recommendation system for e-commerce. In Proceedings of International Conference on Communication and Computational Technologies, 229-236, Singapore: Springer.
• Hahsler, M. (2015). Recommenderlab: a framework for developing and testing recommendation algorithms. 13.03.2022 tarihinde https://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf adresinden alındı.
• Han, K. (2020). Personalized news recommendation and simulation based on improved collaborative filtering algorithm. Complexity, 2020, 1-12.
• Harper, F. M., & Konstan, J. A. (2015). The movielens datasets: history and context. Acm Transactions on Interactive Intelligent Systems (tiis), 5(4), 1-19.
• Jitendra, M., & Radhika, Y. (2021). An automated music recommendation system based on listener preferences. In Recent Trends in Intensive Computing, 80-87. Amsterdam: IOS Press.
• Kathavate, S. (2021). Music recommendation system using content and collaborative filtering methods. International Journal of Engineering Research & Technology (IJERT), 10(02), 167-171.
• Kumar, P. S. (2020). Recommendation system for e-commerce by memory based and model based collaborative filtering. In Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019), (1182), 123, Springer Nature.
• Munuswamy, S., Saranya, M. S., Ganapathy, S., Muthurajkumar, S., & Kannan, A. (2021). Sentiment analysis techniques for social media-based recommendation systems. National Academy Science Letters, 44, 281–287.
• Murad, D. F., Heryadi, Y., Isa, S. M., & Budiharto, W. (2020). Personalization of study material based on predicted final grades using multi-criteria user-collaborative filtering recommender system. Education and Information Technologies, 25(6), 5655-5668.
• Pratama, B. Y., Budi, I., & Yuliawati, A. (2020). Product recommendation in offline retail industry by using collaborative filtering. International Journal of Advanced Computer Science and Applications, 11(9), 635-643.
• Rebelo, M. Â., Coelho, D., Pereira, I., & Fernandes, F. (2021). A new cascade-hybrid recommender system approach for the retail market. International Conference on Innovations in Bio-Inspired Computing and Applications, 371-380, 16-18 December 2021.
• Sariman, G. (2011). Veri madenciliğinde kümeleme teknikleri üzerine bir çalışma: k-means ve k-medoids kümeleme algoritmalarının karşılaştırılması. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 15(3), 192-202.
• Shen, J., Zhou, T., & Chen, L. (2020). Collaborative filtering-based recommendation system for big data. International Journal of Computational Science and Engineering, 21(2), 219-225.
• Silahtaroğlu, G. (2013). Veri madenciliği kavram ve algoritmaları. İstanbul: Papatya Yayıncılık Eğitim.
• Su, X., & Khoshgoftaar, T. M. (2009). A survey of collaborative filtering techniques. Advances in Artificial Intelligence, 2009, 1-19.
• Zhou, L., Zhang, F., Zhang, S., & Xu, M. (2021). Study on the personalized learning model of learner-learning resource matching. International Journal of Information and Education Technology, 11(3).