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Article Dans Une Revue International Journal of Computational Intelligence Systems Année : 2018

Recommending Garment Products in E-Shopping Environment by Exploiting an Evolutionary Knowledge Base

Résumé

Garment purchasing through the e-shopping platforms has become an important trend for consumers of all parts of the world. More and more e-shopping platforms have proposed recommendation functions to consumers in order to make them to obtain more easily desired products and then increase shopping sales. However, there are two main drawbacks in the existing recommendation systems. First, it systematically lacks feedback processing in these systems. If a consumer is not satisfied with the recommendation result, there is no self-adjustment function. The other drawback is that the existing recommendation systems are mostly closed, without considering the possibility of data and knowledge updating. Considering the above drawbacks, we propose a new recommendation system integrating the following features: 1) automatic adjustment of the knowledge according to the consumers’ feedback, 2) making the system open and adaptive so that the consumer can easily add or replace criteria and data. This proposed recommendation system can effectively help consumers to choose garments on the Internet. Compared with the other systems, the proposed one is more robust and more interpretable owing to its capacity of handling uncertainty.
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Origine : Publication financée par une institution
Licence : CC BY NC - Paternité - Pas d'utilisation commerciale

Dates et versions

hal-04428583 , version 1 (14-02-2024)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

  • HAL Id : hal-04428583 , version 1

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Junjie Zhang, Xianyi Zeng, Ludovic Koehl, Min Dong. Recommending Garment Products in E-Shopping Environment by Exploiting an Evolutionary Knowledge Base. International Journal of Computational Intelligence Systems, 2018, 11, pp.340-354. ⟨hal-04428583⟩

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