A Study on New Product Recommendation Using Multi-Label CVAE for Fresh Flowers

Aya Kitasato, Gendo Kumoi, Masayuki Goto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In recent years, it has become very popular to use purchase history data on e-commerce sites for marketing measures to increase sales. Under such a situation, this paper considers measures using the purchase history data of a company providing delivering services of fresh flower products through an e-commerce site. This site deals mainly with fresh flowers, and the majority of items are purchased for gifts. The demands of flower gifts are usually strongly related with certain events, such as birthday, Mother's day, opening celebration, etc. Since each customer often makes purchase only at certain event when purchasing a flower gift, and it is important to encourage them to make purchases at other events from marketing viewpoint. In addition, the appearance of fresh flowers is important, so product recommendation with product images is necessary. It is relatively easy to develop floral gifts because they consist of certain patterns such as types of fresh flowers and shapes such as bouquets. However, there is no development of product which quantitatively uses purchase history information, The purpose of this research is, therefore, to generate product images that are preferred by customers in another event, considering the characteristics of product images purchased in individual event, where it is also possible to create new product images that are not contained in existing items. The proposed model is based on Conditional Variational Auto Encoader (CVAE) and can generate image outputs by inputting product images as multi-labels of events and attributes such as age and gender of customers that greatly affect product selection. Then, after learning a generator model, we consider to analyze what kinds of new products a customer with certain attributes who purchased at certain event would newly prefer at other events by changing the labels. Furthermore, in this study, we demonstrate the validity of the model by analyzing an actual data set.

Original languageEnglish
Title of host publication2021 IEEE 12th International Workshop on Computational Intelligence and Applications, IWCIA 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665444255
DOIs
Publication statusPublished - 2021
Event12th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2021 - Virtual, Online, Japan
Duration: 2021 Nov 62021 Nov 7

Publication series

Name2021 IEEE 12th International Workshop on Computational Intelligence and Applications, IWCIA 2021 - Proceedings

Conference

Conference12th IEEE International Workshop on Computational Intelligence and Applications, IWCIA 2021
Country/TerritoryJapan
CityVirtual, Online
Period21/11/621/11/7

Keywords

  • Conditional Variational Autoencoder
  • Fresh Flower
  • Image Generation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software

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