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Media type:
E-Article
Title:
Forgetting mechanisms for scalable collaborative filtering
Contributor:
Vinagre, João;
Jorge, Alípio Mário
imprint:
Springer Science and Business Media LLC, 2012
Published in:Journal of the Brazilian Computer Society
Language:
English
DOI:
10.1007/s13173-012-0077-3
ISSN:
0104-6500;
1678-4804
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title><jats:p>Collaborative filtering (CF) has been an important subject of research in the past few years. Many achievements have been made in this field, however, many challenges still need to be faced, mainly related to scalability and predictive ability. One important issue is how to deal with old and potentially obsolete data in order to avoid unnecessary memory usage and processing time. Our proposal is to use forgetting mechanisms. In this paper, we present and evaluate the impact of two forgetting mechanisms—sliding windows and fading factors—in user-based and item-based CF algorithms with implicit binary ratings under a scenario of abrupt change. Our results suggest that forgetting mechanisms reduce time and space requirements, improving scalability, while not significantly affecting the predictive ability of the algorithms.</jats:p>