Collaborative filtering based on content addressing

Shlomo Berkovsky*, Yaniv Eytani, Larry Manevitz

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


Collaborative Filtering (CF) is one of the most popular recommendation techniques. It is based on the assumption that users with similar tastes prefer similar items. One of the major drawbacks of the CF is its limited scalability, as the complexity of the CF grows linearly both with the number of available users and items. This work proposes a new fast variant of the CF employed over multi-dimensional content-addressable space. Our approach heuristically decreases the computational effort required by the CF algorithm by limiting the search process only to potentially similar users. Experimental results demonstrate that our approach is capable of generate recommendations with high levels of accuracy, while significantly improving performance in comparison with the traditional implementation of the CF.

Original languageEnglish
Title of host publicationICEIS 2006 - 8th International Conference on Enterprise Information Systems, Proceedings
Number of pages8
Publication statusPublished - 1 Dec 2006
Externally publishedYes
Event8th International Conference on Enterprise Information Systems, ICEIS 2006 - Paphos, Cyprus
Duration: 23 May 200627 May 2006


Conference8th International Conference on Enterprise Information Systems, ICEIS 2006


  • Collaborative filtering
  • Content-addressable systems
  • Recommender systems


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