Recommender systems have to select items for users to optimize for one or more objectives. We introduced several possible objectives in Chapter 1, reviewed classical methods in Chapter 2, described the explore-exploit trade-off and the key ideas to reduce dimensionality of the problem in Chapter 3, and discussed how to evaluate recommendation models in Chapter 4. In this and the five subsequent chapters of the book, we discuss various statistical methods used in some commonly encountered scenarios. In particular, we focus on problem settings where the main objective is to maximize some positive user response to the recommended items. In many application scenarios, clicks on items are the primary response. To maximize clicks, we have to recommend items with high click-through rates (CTRs). Thus, CTR estimation is our main focus. Although we use click and CTR as our primary objectives, other types of positive response (e.g., share, like) can be handled in a similar way. We defer the discussion of Multi objective optimization to Chapter 11.

The choice of statistical methods for a recommendation problem depends on the application. In this chapter, we provide a high-level overview of techniques to be introduced in the next four chapters. We start with an introduction to a variety of different problem settings in Section 5.1 and then describe an example system architecture in Section 5.2 to illustrate how web recommender systems work in practice, along with the role of statistical methods in such systems.

Problem Settings

A typical recommender system is usually implemented as a module on a web page. In this section, we introduce some common recommendation modules, provide details of the application settings, and conclude with a discussion of commonly used statistical methods for these settings.

Common Recommendation Modules

We classify websites into the following four categories: general portals, personal portals, domain-specific sites, and social network sites. Table 5.1 provides a summary.

General portals are websites that provide a wide range of different content. The home pages of content networks like Yahoo!, MSN, and AOL are examples of general portals.

Personal portals are websites that allow users to customize their home pages with desired content. For example, users of My Yahoo! customize their home pages by selecting content feeds from different sources or publishers and arranging the feeds on the pages according to their preferences.