This link has been bookmarked by 32 people . It was first bookmarked on 02 Mar 2006, by Joel Liu.
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Scott Lesliea free Java recommendation engine for collaborative filtering
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free Java recommendation engine for collaborative filtering
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19 Jan 07
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23 Sep 06
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The Intelligent Information Systems (IIS) research group of Oregon State University is pleased to announce Version 0.4 of CoFE: a free Java recommendation engine for collaborative filtering. CoFE is short for "COllaborative Filtering Engine". CoFE was formerly known as CFEngine, but has been renamed due to a popular unrelated software package with the same name
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12 Sep 06
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17 Jun 06
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The Intelligent Information Systems (IIS) research group of Oregon State University is pleased to announce Version 0.4 of CoFE: a free Java recommendation engine for collaborative filtering. CoFE is short for "COllaborative Filtering Engine". CoFE was formerly known as CFEngine, but has been renamed due to a popular unrelated software package with the same name.
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The Intelligent Information Systems (IIS) research group of Oregon State University is pleased to announce Version 0.4 of CoFE: a free Java recommendation engine for collaborative filtering. CoFE is short for "COllaborative Filtering Engine". CoFE was formerly known as CFEngine, but has been renamed due to a popular unrelated software package with the same name.
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16 Jun 06
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31 May 06
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This free server (including source code) allows anyone to easily set up a recommendation system. It should run on any platform that supports Java 1.4. We hope it will enhance academic research and commercial application of such systems by removing the need for developers to create such an engine themselves.
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Tim ShadelCoFE is a free Java recommendation engine for collaborative filtering from the The Intelligent Information Systems (IIS) research group of Oregon State University.
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Collaborative filtering is the process of computing personalized recommendations by finding users with similar taste. Such techniques have been successfully used to recommend newsgroup articles, movies, music, and e-commerce products. Collaborative filtering is most known for its use on popular e-commerce sites such as Amazon.com or NetFlix.com. Today, most recommender systems are proprietary.
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01 Jan 05
Dan HowardHow to power "people who bought this book also bought these..." searches. Just like on Amazon.
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Furl uses this to power their "people who furled this page, also liked...".
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Open source software! -
21 May 04
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