Recommender Technologies and Emerging Applications

Recommender Technologies and Emerging Applications

Young Park (Bradley University, USA)
DOI: 10.4018/978-1-5225-7598-6.ch034

Abstract

This chapter presents a brief overview of the field of recommender technologies and their emerging application domains. The authors explain the current major recommender system approaches within a unifying model, discuss emerging applications of recommender systems beyond traditional e-commerce, and outline emerging trends and future research topics, along with additional readings in the area of recommender technologies and applications. They believe that personalized recommender technologies will continue to advance and be applied in a variety of traditional and emerging application domains to assist users in the age of information overload.
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Background

Since the first major recommender systems emerged in the mid-1990s (Resnick & Varian, 1997), a large number of recommender systems have been developed and used in a wide range of e-commerce environments and improved by continuing research.

A typical recommender system provides users with personalized recommendations of items such as goods, services or information to guide users to find items that are relevant to them. Recommendations are based on past and present profiles of users with respect to items. The personalized recommendation problem can be described as follows:

Given a target user, produce personalized recommendations of items relevant to the target user.

To solve this recommendation problem, a recommender system generally uses three types of data—data about the users (U_data), data about the items such as goods, services or information (I_data), and data about the relevance (such as rating, evaluation, purchase, or interest) relation between the users and the items (R_data):

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