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Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach
David Pennock, Eric Horvitz, Steve Lawrence, C. Giles
Abstract:
The growth of Internet commerce has stimulated the use of collaborative
filtering (CF) algorithms as recommender systems. Such systems leverage
knowledge about the known preferences of multiple users to recommend
items of interest to other users. CF methods have been harnessed to
make recommendations about such items as web pages, movies, books, and
toys. Researchers have proposed and evaluated many approaches for
generating recommendations. We describe and evaluate a new method
called emph{personality diagnosis (PD)}. Given a user's preferences
for some items, we compute the probability that he or she is of the
same ``personality type'' as other users, and, in turn, the probability
that he or she will like new items. PD retains some of the advantages
of traditional similarity-weighting techniques in that all data is
brought to bear on each prediction and new data can be added easily and
incrementally. Additionally, PD has a meaningful probabilistic
interpretation, which may be leveraged to justify, explain, and augment
results. We report empirical results on the EachMovie database of movie
ratings, and on user profile data collected from the CiteSeer digital
library of Computer Science research papers. The probabilistic
framework naturally supports a variety of descriptive measurements---in
particular, we consider the applicability of a value of information
(VOI) computation.
Keywords: collaboraive filtering, recommender systems, probabilistic diagnosis, value of inform
Pages: 473-4
PS Link: http://www.neci.nec.com/homepages/dpennock/papers/pd-uai-00.ps
PDF Link: http://www.neci.nec.com/homepages/dpennock/papers/pd-uai-00.pdf
BibTex:
@INPROCEEDINGS{Pennock00,
AUTHOR = "David Pennock
and Eric Horvitz and Steve Lawrence and C. Giles",
TITLE = "Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach",
BOOKTITLE = "Proceedings of the Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "473-4"
}
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