Recommender Systems Using Linear Classifiers
Tong Zhang, Vijay S. Iyengar;
use historical data on user preferences and other available
data on users (for example, demographics) and items (for example, taxonomy)
to predict items a new user might like. Applications of these methods include
recommending items for purchase and personalizing the
browsing experience on a web-site.
Collaborative filtering methods have focused on using just the
history of user preferences to make the recommendations.
These methods have been categorized as memory-based
if they operate
over the entire data to make predictions and as model-based
they use the data to build a model which is then used for predictions.
In this paper, we propose the use of linear classifiers in a
model-based recommender system.
We compare our method with another model-based method using
decision trees and with
memory-based methods using data from various domains.
Our experimental results indicate that these linear models
are well suited for this application.
They outperform a commonly proposed
memory-based method in accuracy and also
have a better tradeoff between off-line and on-line computational requirements.