Recommendation Systems 9.1 a Model for Recommendation Systems
In this section a model for recommendation systems, based on a utility matrix of preferences is introduced, and the concept of a " long-tail, clustering-based recommendation system", which can use the groundwork laid in Chapter 3 on similarity search and Chapter 7 on clustering.
Abstract
There is an extensive class of Web applications that involve predicting user responses to options. Such a facility is called a recommendation system. We shall begin this chapter with a survey of the most important examples of these systems. However, to bring the problem into focus, two good examples of recommendation systems are: 1. Offering news articles to on-line newspaper readers, based on a prediction of reader interests. 2. Offering customers of an on-line retailer suggestions about what they might like to buy, based on their past history of purchases and/or product searches. Recommendation systems use a number of different technologies. We can classify these systems into two broad groups. • Content-based systems examine properties of the items recommended. For instance, if a Netflix user has watched many cowboy movies, then recommend a movie classified in the database as having the " cowboy " genre. • Collaborative filtering systems recommend items based on similarity measures between users and/or items. The items recommended to a user are those preferred by similar users. This sort of recommendation system can use the groundwork laid in Chapter 3 on similarity search and Chapter 7 on clustering. However, these technologies by themselves are not sufficient , and there are some new algorithms that have proven effective for recommendation systems. In this section we introduce a model for recommendation systems, based on a utility matrix of preferences. We introduce the concept of a " long-tail, " 307