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UBC Theses and Dissertations
Symmetric collaborative filtering using the noisy sensor model Sharma, Rita
Abstract
Collaborative filtering is the process of making recommendations regarding the potential preference of a user, for example shopping on the Internet, based on the preference ratings of the user and a number of other users for various items. This thesis considers the problem of symmetric collaborative filtering based on explicit ratings. To evaluate the algorithms, we consider only pure collaborative filtering, using given ratings and excluding other information about the people or items. Our approach is to predict an active user's preferences regarding a particular item by using other people's ratings of that item and other items rated by the active user as noisy sensors. The noisy sensor model uses Bayes' theorem to compute the probability distribution for the active user's rating of a new item. We give two variants for learning the noisy sensor model: one, for explicit binary rating data; and the second, for explicit multi-valued rating data. The model for binary rating data is based on Bayesian learning. Its performance motivate us to further explore the use of noisy sensor model for multi-valued rating data. We give two variant models for multi-valued rating data: in one, we learn a linear model of how users rate items; in another, we assume different users rate items identically, but that the accuracy of the sensors must be learned. We compare the two models of multi-valued rating data with stateof- the-art techniques and show how they are significantly better whether a user has rated only two items or many. We report empirical results using the EachMovie database of movie ratings. We also show that by considering the items similarity along with the users similarity the accuracy of the prediction increases.
Item Metadata
Title |
Symmetric collaborative filtering using the noisy sensor model
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2001
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Description |
Collaborative filtering is the process of making recommendations regarding
the potential preference of a user, for example shopping on the Internet, based
on the preference ratings of the user and a number of other users for various
items. This thesis considers the problem of symmetric collaborative filtering
based on explicit ratings. To evaluate the algorithms, we consider only pure
collaborative filtering, using given ratings and excluding other information
about the people or items.
Our approach is to predict an active user's preferences regarding a particular
item by using other people's ratings of that item and other items rated
by the active user as noisy sensors. The noisy sensor model uses Bayes' theorem
to compute the probability distribution for the active user's rating of a
new item. We give two variants for learning the noisy sensor model: one, for
explicit binary rating data; and the second, for explicit multi-valued rating
data. The model for binary rating data is based on Bayesian learning. Its
performance motivate us to further explore the use of noisy sensor model for
multi-valued rating data.
We give two variant models for multi-valued rating data: in one, we
learn a linear model of how users rate items; in another, we assume different
users rate items identically, but that the accuracy of the sensors must be
learned. We compare the two models of multi-valued rating data with stateof-
the-art techniques and show how they are significantly better whether a
user has rated only two items or many. We report empirical results using the
EachMovie database of movie ratings. We also show that by considering the
items similarity along with the users similarity the accuracy of the prediction
increases.
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Extent |
2463482 bytes
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Genre | |
Type | |
File Format |
application/pdf
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Language |
eng
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Date Available |
2009-07-29
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Provider |
Vancouver : University of British Columbia Library
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Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
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DOI |
10.14288/1.0051313
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2001-05
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Campus | |
Scholarly Level |
Graduate
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Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.