首页    期刊浏览 2024年11月23日 星期六
登录注册

文章基本信息

  • 标题:Product Recommendation in Offline Retail Industry by using Collaborative Filtering
  • 其他标题:Product Recommendation in Offline Retail Industry by using Collaborative Filtering
  • 本地全文:下载
  • 作者:Bayu Yudha Pratama ; Indra Budi ; Arlisa Yuliawati
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2020
  • 卷号:11
  • 期号:9
  • DOI:10.14569/IJACSA.2020.0110975
  • 出版社:Science and Information Society (SAI)
  • 摘要:The variety of purchased products is important for retailers. When a customer buys a specific product in a large number, the customer might get benefit, such as more discounts. On contrary, this could harm the retailers since only some products are sold quickly. Due to this problem, big retailers try to entice customers to buy many variations of products. For an offline retailer, promoting specific products based on the markets’ taste is quite challenging because of the unavailability of information regarding customers’ preferences. This study utilized four years of purchase transaction data to implicitly find customers’ ratings or feedback towards specific products they have purchased. This study employed two Collaborative Filtering methods in generating product recommendations for customers and find the best method. The result shows that the Memory-based approach (k-NN Algorithm) outperformed the Model-based (SVD Matrix Factorization). Another finding is that the more data training being used, the better the performance of the recommendation system will result. To cope with the data scalability issue, customer segmentation through k-Means Clustering was applied. The result implies that this is not necessary since it failed to boost up the models' accuracy. The result of the recommendation system is then applied in a suggested business process for a specific offline retailer shop.
  • 关键词:Recommendation system; offline retail store; memory-based collaborative filtering; customer segmentation
国家哲学社会科学文献中心版权所有