Recommendation system development for fashion retail e-commerce
| Autor: | Kyung Jin Cha, Hyunwoo Hwangbo, Yang Sok Kim |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Marketing
Online and offline Product category Computer Networks and Communications business.industry Computer science Shopping mall 02 engineering and technology E-commerce Recommender system Computer Science Applications Complementary good Product (business) World Wide Web 020204 information systems Management of Technology and Innovation 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing business |
| Zdroj: | Electronic Commerce Research and Applications. 28:94-101 |
| ISSN: | 1567-4223 |
| Popis: | This study presents a real-world collaborative filtering recommendation system implemented in a large Korean fashion company that sells fashion products through both online and offline shopping malls. The company’s recommendation environment displays the following unique characteristics: First, the company’s online and offline stores sell the same products. Second, fashion products are usually seasonal, so customers’ general preference changes according to the time of year. Last, customers usually purchase items to replace previously preferred items or purchase items to complement those already bought. We propose a new system called K-RecSys, which extends the typical item-based collaborative filtering algorithm by reflecting the above domain characteristics. K-RecSys combines online product click data and offline product sale data weighted to reflect the online and offline preferences of customers. It also adopts a preference decay function to reflect changes in preferences over time, and finally recommends substitute and complementary products using product category information. We conducted an A/B test in the actual operating environment to compare K-RecSys with the existing collaborative filtering system implemented with only online data. Our experimental results show that the proposed system is superior in terms of product clicks and sales in the online shopping mall and its substitute recommendations are adopted more frequently than complementary recommendations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect