Collaborative Filtering with Maximum Entropy
| Autor: | Dmitry Pavlov, Eren Manavoglu, C.L. Giles, David M. Pennock |
|---|---|
| Rok vydání: | 2004 |
| Předmět: |
Web server
Information retrieval Computer Networks and Communications business.industry Computer science Process (engineering) Principle of maximum entropy Word of mouth Recommender system Mixture model computer.software_genre Artificial Intelligence Collaborative filtering The Internet business computer |
| Zdroj: | IEEE Intelligent Systems. 19:40-48 |
| ISSN: | 1541-1672 |
| DOI: | 10.1109/mis.2004.59 |
| Popis: | As users navigate through online document collections on high-volume Web servers, they depend on good recommendations. We present a novel maximum-entropy algorithm for generating accurate recommendations and a data-clustering approach for speeding up model training. Recommender systems attempt to automate the process of "word of mouth" recommendations within a community. Typical application environments such as online shops and search engines have many dynamic aspects. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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