NDLI: A Recommender System Based on a Machine Learning Algorithm for B2C Portals

Content Provider	IEEE Xplore Digital Library
Author	Lopez-Lopez, L. M. Castro-Schez, J. J. Vallejo-Fernandez, D. Albusac, J.
Copyright Year	2009
Abstract	Users of B2C (Business-to-Consumer) portals often lack of detailed knowledge about the state of the market related to the product they want to purchase. This leads to consumers purchasing the most popular product of a category, although perhaps that is neither the best suited for their requirements nor at the best cost. In this paper, a methodology to develop a recommender system is proposed. Our proposal is based on a supervised learning approach to infer knowledge that allows consumers to unveil what the existing patterns among the features that describe the searched product are. Such knowledge allows consumers to learn what they can buy and at what cost.
Starting Page	524
Ending Page	531
File Size	307180
Page Count	8
File Format	PDF
ISBN	9780769538013
DOI	10.1109/WI-IAT.2009.87
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Costs Conferences Humans fuzzy logic association rules Proposals machine learning Intelligent agent Learning systems e-Commerce recommender system B2C product selection Prototypes Recommender systems Portals Business-to-Consumer
Content Type	Text
Resource Type	Article

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A Recommender System Based on a Machine Learning Algorithm for B2C Portals