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ISMIS
2005
Springer

Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms

9 years 3 months ago
Incremental Collaborative Filtering for Highly-Scalable Recommendation Algorithms
Most recommendation systems employ variations of Collaborative Filtering (CF) for formulating suggestions of items relevant to users’ interests. However, CF requires expensive computations that grow polynomially with the number of users and items in the database. Methods proposed for handling this scalability problem and speeding up recommendation formulation are based on approximation mechanisms and, even if they improve performance, most of the time result in accuracy degradation. We propose a method for addressing the scalability problem based on incremental updates of user-to-user similarities. Our Incremental Collaborative Filtering (ICF) algorithm (i) is not based on any approximation method and gives the potential for high-quality recommendation formulation (ii) provides recommendations orders of magnitude faster than classic CF and thus, is suitable for online application.
Manos Papagelis, Ioannis Rousidis, Dimitris Plexou
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ISMIS
Authors Manos Papagelis, Ioannis Rousidis, Dimitris Plexousakis, Elias Theoharopoulos
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