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Category-Based Filtering in Recommender Systems for Improved Performance in Dynamic Domains

Authors: Mikael Sollenborn (former), Peter Funk
Source: 2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems, p 4, Malaga, Spain, Springer
 

Abstract

In Recommender systems, collaborative filtering is the most commonly used technique. Although often successful, collaborative filtering encounters the latency problem in domains where items are frequently added, as the users have to review new items before they can be recommended. In this paper a novel approach to reduce the latency problem is proposed, based on category-based filtering and user stereotypes.
 

BibTeX entry

@inproceedings{Sollenborn_0365:2002,
   author = {Mikael Sollenborn and Peter Funk},
   title = {Category-Based Filtering in Recommender Systems for Improved Performance in Dynamic Domains},
   booktitle = {2nd International Conference on Adaptive Hypermedia and Adaptive Web Based Systems},
   month = {May},
   year = {2002},
   pages = {4},
   publisher = {Malaga, Spain},
   url = {http://www.mrtc.mdh.se/index.php?choice=publications&id=0365},
}

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