Home // ICIW 2019, The Fourteenth International Conference on Internet and Web Applications and Services // View article

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

Authors:
Merve Artukarslan
S. Emre Alptekin

Keywords: Collaborative Filtering; Singular Value Decomposition; Alternating Least Squares; Recommender Systems; Matrix Factorization.

Abstract:
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 Decomposition (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 gathered from 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 recommenda-tions match customers’ interests.

Pages: 21 to 26

Copyright: Copyright (c) IARIA, 2019

Publication date: July 28, 2019

Published in: conference

ISSN: 2308-3972

ISBN: 978-1-61208-728-3

Location: Nice, France

Dates: from July 28, 2019 to August 2, 2019