Investigators
Obradovic Zoran
Vucetic Slobodan
Problem
In today’s society there is an increasing need for automated
systems providing personalized recommendations to a user faced with
a large number of choices. The products customization trend coupled
with E-commerce where customers were not provided with an option
to examine the products “off-shelf” in a traditional sense,
make the problem of providing accurate personalized recommendations
very important. Automated methods are needed to provide a large
number of users with the ability to efficiently locate and retrieve
information according to their preferences. The task of collaborative
filtering is to predict preferences of an active user on unseen
items given preferences of other users, typically expressed as numerical
ratings.
Results
We proposed [vucetic00d] a novel regression-based approach that
learns a number of experts describing relationships in ratings between
pairs of items. Based on ratings provided by an active user for
some of the items, the experts are combined efficiently to predict
user’s preferences on the remaining items. Extensive experiments
on Eachmovie and Jester benchmark collaborative filtering data showed
that the proposed regression-based approach achieves improved accuracy
and is orders of magnitude faster than the popular neighbor-based
alternative.
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