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Collaborative Recommender System for Online Book Retailing by using Associative Rule Mining Approach

http://hdl.handle.net/20.500.12678/0000004146
58edfdea-f7ec-4c55-93f6-0e2f8b815c84
9dea5279-328c-4d2b-aaae-48a39d7190c8
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55281.pdf 55281.pdf (270 Kb)
Publication type
Article
Upload type
Publication
Title
Title Collaborative Recommender System for Online Book Retailing by using Associative Rule Mining Approach
Language en
Publication date 2009-12-30
Authors
Phyo, Thet Mon
Oo, Nhinn Minn
Description
Recommender systems help the users to find and evaluate items of interest. They connect user with items to consume by associating the opinions of other individuals with the consuming user’s actions or opinions. Such system has become powerful tool in e-commerce sites to facilitate the buying process. In this paper, we present the application of recommender system for online book retailing. We use a hybrid model of Association Rule Mining approach to find the frequent book-itemsets and Collaborative filtering techniques to generate recommendations base on the past buying behavior of the consumers and the items in the rules observed by association rule mining approach. We use the Apriori algorithm as the underlying techniques of association rule mining approach.
Keywords
Recommendation, Online retailing, Association rules mining, Collaborative filtering
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1845
Journal articles
Fourth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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