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  • Print publication year: 2016
  • Online publication date: February 2016

1 - Introduction



Recommender systems (or recommendation systems) are computer programs that recommend the “best” items to users in different contexts. The notion of a best match is typically obtained by optimizing for objectives like total clicks, total revenue, and total sales. Such systems are ubiquitous on the web and form an integral part of our daily lives. Examples include product recommendations to users on an e-commerce site to maximize sales; content recommendations to users visiting a news site to maximize total clicks; movie recommendations to maximize user engagement and increase subscriptions; or job recommendations on a professional network site to maximize job applications. Input to these algorithms typically consists of information about users, items, contexts, and feedback that is obtained when users interact with items.

Figure 1.1 shows an example of a typical web application that is powered by a recommender system. A user uses a web browser to visit a web page. The browser then submits an HTTP request to the web server that hosts the page. To serve recommendations on the page (e.g., popular news stories on a news portal page), the web server makes a call to a recommendation service that retrieves a set of items and renders them on the web page. Such a service typically performs a large number of different types of computations to select the best items. These computations are often a hybrid of both offline and real-time computations, but they must adhere to strict efficiency requirements to ensure quick page load time (typically hundreds of milliseconds). Once the page loads, the user may interact with items through actions like clicks, likes, or shares. Data obtained through such interactions provide a feedback loop to update the parameters of the underlying recommendation algorithm and to improve the performance of the algorithm for future user visits. The frequency of such parameter updates depends on the application. For instance, if items are time sensitive or ephemeral, as in the case of news recommendations, parameter updates must be done frequently (e.g., every few minutes). For other applications where items have a relatively longer lifetime (e.g., movie recommendations), parameter updates can happen less frequently (e.g., daily) without significant degradation in overall performance.

Algorithms that facilitate selection of best items are crucial to the success of recommender systems. This book provides a comprehensive description of statistical and machine learning methods on which we believe such algorithms should be based.