\title{Recommender Systems Using Linear Classifiers}


\author{\name Tong Zhang  \email tzhang@watson.ibm.com \\
  \name Vijay S. Iyengar \email vsi@us.ibm.com \\
  \addr   IBM Research Division, T. J. Watson Research Center  \\
  P.O. Box 218, Yorktown Heights, NY 10598, U.S.A. \\
}

\editor{Leslie Pack Kaelbling}

\date{}

\maketitle

\begin{abstract}%
Recommender systems
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 \textit{memory-based} if they operate
over the entire data to make predictions and as \textit{model-based} if
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.
\end{abstract}