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ART (Attractive Recommendation Tailor): How the Diversity of Product Recommendations Affects Customer Purchase Preference in Fashion Industry?

Published: 19 October 2020 Publication History

Abstract

This study examines the impact of the 'diversity' of product recommendations on the 'preference' of a customer, using online/offline data from a leading fashion company. First, through interviews with fashion professionals, we categorized the characteristics of customers into four types - gift, coordinator, carry-over, and trendsetter. Then, using a hybrid filtering method, we increased the accuracy and diversity of recommended products. We derived 13 salient features that reflect customer behavior based on the Purchase Funnel model and built a classification model that predicts a customer's preference rates. Second, we conducted two large-scale user tests with 20,000 real customers to verify the effectiveness of our recommendation system. Study results empirically demonstrated the importance of diversity of recommended products. The more diverse the product recommendations were, the higher the purchase rate, the average purchase amount, and the cross purchase rate were observed. In addition, we tracked the customers? purchase for two months after the user tests and found that diverse product exposure positively influenced customer retention (e.g., repurchase rate, amount).

Supplementary Material

MP4 File (3340531.3412687.mp4)
This study examines the impact of the ?diversity? of product recommendations on the ?preference? of a customer. First, through interviews with fashion professionals, we categorized the characteristics of customers into four types. Then, using a hybrid filtering method, we increased the accuracy and diversity of recommended products. We derived 13 salient features that reflect customer behavior based on the Purchase Funnel model and built a classification model that predicts a customer?s preference rates. Second, we conducted large-scale user tests to verify the effectiveness of our system. Study results empirically demonstrated the importance of diversity of recommended products. The more diverse the product recommendations were, the higher the purchase rate, the average purchase amount, and the cross purchase rate were observed. In addition, we tracked the customers? purchase for two months after the user tests and found that diverse product exposure positively influenced customer retention.

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Cited By

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  • (2024)Result Diversification in Search and Recommendation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338226236:10(5354-5373)Online publication date: Oct-2024
  • (2023)A Review of Modern Fashion Recommender SystemsACM Computing Surveys10.1145/362473356:4(1-37)Online publication date: 21-Oct-2023

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        cover image ACM Conferences
        CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
        October 2020
        3619 pages
        ISBN:9781450368599
        DOI:10.1145/3340531
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        Published: 19 October 2020

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        Author Tags

        1. diversity
        2. fashion recommendation
        3. feature engineering
        4. large-scale user test
        5. preference modeling

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        • (2024)Result Diversification in Search and Recommendation: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.338226236:10(5354-5373)Online publication date: Oct-2024
        • (2023)A Review of Modern Fashion Recommender SystemsACM Computing Surveys10.1145/362473356:4(1-37)Online publication date: 21-Oct-2023

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