This chapter proposes an introduction to recommendation techniques and suggests some exercises and projects. We do not present a recommendation system in particular but rather focus on the general methodology. As an illustrative example, we will use the MovieLens data set to construct movie recommendations.

The chapter successively introduces recommendation, user-based collaborative filtering and item-based collaborative filtering. It discusses different methods parameterizations and evaluates their result with respect to the quality of the data set. We show how to generate recommendations using SQL queries on the Movie-Lens data set. Finally, we suggest some projects for students who want to investigate further the realm of recommendation systems.

INTRODUCTION TO RECOMMENDATION SYSTEMS

Given a set of ratings of items by a set of users, a recommendation system produces a list of items for a particular user, possibly in a given context. Such systems are widely used in Web applications. For example, content sites like Yahoo! Movies (movies), Zagat (restaurants), Library Thing (books), Pandora (music), Stumble Upon (Web site) suggest a list of items of interest by predicting the ratings of their users. E-commerce sites such as Amazon (books) or Netflix (movies) use recommendations to suggest new products to their users and construct bundle sales. Usually, they exploit the recent browsing history as a limited context. Finally, advertisement companies need to find a list of advertisements targeted for their users. Some of them, like Google AdSense, rely more on the context (e.g., keywords) than on an estimation of the user's taste based on her/his recent browsing history.