Recommending Garment Products in E-Shopping ...
Document type :
Article dans une revue scientifique: Article original
Permalink :
Title :
Recommending Garment Products in E-Shopping Environment by Exploiting an Evolutionary Knowledge Base
Author(s) :
Zhang, Junjie [Auteur]
Zeng, Xianyi [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Koehl, Ludovic [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Dong, Min [Auteur]
Zeng, Xianyi [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Koehl, Ludovic [Auteur]
Génie des Matériaux Textiles - ULR 2461 [GEMTEX]
Dong, Min [Auteur]
Abbreviated title :
Int. J. Comput. Intell. Syst.
Volume number :
11
Pages :
340-354
Publication date :
2018-05-18
ISSN :
1875-6891
English keyword(s) :
recommendation system
knowledge base
self-learning
human-machine interaction
feedback
knowledge base
self-learning
human-machine interaction
feedback
HAL domain(s) :
Sciences de l'ingénieur [physics]
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Audience :
Non spécifiée
Popular science :
Non
Administrative institution(s) :
Université de Lille
ENSAIT
Junia HEI
ENSAIT
Junia HEI
Collections :
Submission date :
2023-06-20T02:28:37Z
2023-08-29T06:18:13Z
2024-01-31T11:10:37Z
2023-08-29T06:18:13Z
2024-01-31T11:10:37Z
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