NDLI: An Improved Apriori-Based Personal Recommendation Algorithm for E-commerce

Content Provider	IEEE Xplore Digital Library
Author	Zhongyi Hu Liangzhong Shen Shengkai Chen
Copyright Year	2008
Description	Author affiliation: Coll. of Comput. Sci. & Eng., Wenzhou Univ., Wenzhou (Zhongyi Hu; Liangzhong Shen; Shengkai Chen)
Abstract	In order to improve the quality of the recommended result, the personalized recommendation system should identify the similarity degree of visitor's accessing behavior so as to predict customer's interests. The key technology is to calculate the similar distance among different objects over either all or only a subset of the dimensions. This paper, first of all, analyses the commonly-used methods and points out their shortages, and then proposes an improved apriori-based personal recommendation algorithm for e-commerce. This algorithm considers overall the minable data source, users' similarity metric and k-support bound to get the data of those access Web pages, construct a matrix model having relatively high purchasing power about customer behavior, get the similar access behavior over the all or partial property space with high efficiency, help the customer find out the merchandise he wishes to buy through the mine of the similar pattern character between latent buyer and high buyer, promote customer satisfaction and truly promote the sale achievements for the enterprise.
Starting Page	60
Ending Page	64
File Size	348236
Page Count	5
File Format	PDF
ISBN	9781424420209
DOI	10.1109/ICPCA.2008.4783649
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-06
Publisher Place	Egypt
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Improved Apriori Algorithm Educational institutions E-commerce Information filtering Personal Recommendation Algorithm Mining Algorithm Computer science Collaboration Web pages Filtering algorithms Information filters Marketing and sales User Similarity Merchandise
Content Type	Text
Resource Type	Article

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