Lemire (2005) Scale and Translation Invariant Collaborative Filtering Systems

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Scale and Translation Invariant Collaborative Filtering Systems

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CONTRIBUTORS:

Author:

Lemire, Daniel (Université du Québec à Montréal (UQAM))

JOURNAL:

Information retrieval, 8(1), 129 - 150.

YEAR:

2005

PUB TYPE:

Journal Article

SUBJECT(S):

Recommender System, Incomplete Vectors, Regression, e-Commerce.

DISCIPLINE:

Computer Science

HTTP:

http://www.ondelette.com/lemire/abstracts/IR2003.html

LANGUAGE:

English

PUB ID:

103-399-842 (Last edited on 2004/12/08 07:39:08 US/Mountain)

SPONSOR(S):

ABSTRACT:
Collaborative filtering systems are prediction algorithms over sparse data sets of user preferences. We modify a wide range of state-of-the-art collaborative filtering systems to make them scale and translation invariant and generally improve their accuracy without increasing their computational cost. Using the EachMovie and the Jester data sets, we show that learning-free constant time scale and translation invariant schemes outperforms other learning-free constant time schemes by at least 3% and perform as well as expensive memory-based schemes (within 4%). Over the Jester data set, we show that a scale and translation invariant Eigentaste algorithm outperforms Eigentaste 2.0 by 20%. These results suggest that scale and translation invariance is a desirable property.

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