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Recommender System Using Item-Based Collaborative Filtering

http://hdl.handle.net/20.500.12678/0000003502
6d8bea5e-4fbb-4958-9faa-76a1ef86adfc
23c61d9e-529e-4c64-911f-acf41b83c09c
None
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psc2010paper psc2010paper (43).pdf (474 Kb)
Publication type
Article
Upload type
Publication
Title
Title Recommender System Using Item-Based Collaborative Filtering
Language en
Publication date 2010-12-16
Authors
Soe, Cherry
Khaing, Thet Thet
Description
The tremendous growth in the amount of available information and the number of visitors to Web sites in recent years poses some key challenges for recommender systems. Recommender systems form a specific type of information filtering (IF) technique that attempts to present information items ( movies, music, books, news, images, web pages, etc., ) that are likely of interest to the user. They produce high quality recommendation and perform many recommendations per second for millions of users and items and achieving high coverage in the face of data sparsity. Most systems are implemented using the k-nearest neighbor collaborative filtering but have some weakness in searching on the Web. To address these issues, item-based collaborative filtering techniques have been explored. Firstly, item-based techniques analyze the user-item matrix to identify relationship between different items and then use these relationships to compute indirectly the user’s profile to some reference characteristics, and seek to predict the ‘rating’ that a user would give to an item they had not yet considered.
Keywords
Collaborative Filtering, Recommender, e-Commerce, Data-Mining, knowledge Discovery
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1246
Journal articles
Fifth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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