Collaborative Filtering Based Recommender System Design For E-Commerce: A Case Study

Creative Commons License

Artukarslan M., Alptekin S. E.

The Fourteenth International Conference on Internet and Web Applications and Services, Nice, France, 28 July - 02 August 2019, pp.21-26

  • Publication Type: Conference Paper / Full Text
  • City: Nice
  • Country: France
  • Page Numbers: pp.21-26
  • Galatasaray University Affiliated: Yes


Recommender Systems (RS) are one of the core engagement functions for e-commerce industry. In a typical recommender system, customer and product data is analyzed and a prediction model is generated, which evaluates products for prospective customers. In terms of business value, it helps individuals identify their interest among an overwhelming variety of products. In this paper, a collaborative filtering based recommender system framework is proposed for Turkey’s leading e-commerce platform hepsiburada. First of all, implicit feedback and customer-product prediction pairs are prepared from collected data. Second, a regularized Singular Value position (SVD) based matrix factorization model is
established for Collaborative Filtering (CF). Customers and products are represented with latent factor vectors. This model is trained with implicit feedback, as the SVD problem is solved with Alternating Least Squares (ALS). Third, predictions are  the CF model. Then, predictions are limited to ten-product recommendation sets. Finally, recommendations
are evaluated by behavioral data generated by prospective customers. The initial results show that 19% of recommendations match customers’ interests.