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New Index
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Item
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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
http://hdl.handle.net/20.500.12678/000000414658edfdea-f7ec-4c55-93f6-0e2f8b815c84
9dea5279-328c-4d2b-aaae-48a39d7190c8
Name / File | License | Actions |
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Publication type | ||||||
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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 |