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Recommender Systems: An Introduction

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“Which digital camera should I buy? What is the best holiday for me and my family? Which is the best investment for supporting the education of my children? Which movie should I rent? Which web sites will I find interesting? Which book should I buy for my next vacation? Which degree and university are the best for my future?”

It is easy to expand this list with many examples in which people have to make decisions about how they want to spend their money or, on a broader level, about their future.

Traditionally, people have used a variety of strategies to solve such decisionmaking problems: conversations with friends, obtaining information from a trusted third party, hiring an expert team, consulting the Internet, using various methods from decision theory (if one tries to be rational), making a gut decision, or simply following the crowd.

However, almost everyone has experienced a situation in which the advice of a friendly sales rep was not really useful, in which the gut decision to follow the investments of our rich neighbor was not really in our interest, or in which spending endless hours on the Internet led to confusion rather than to quick and good decisions. To sum up, good advice is difficult to receive, is in most cases time-consuming or costly, and even then is often of questionable quality. Wouldn’t it be great to have an affordable personal advisor who helps us make good decisions efficiently?

The construction of systems that support users in their (online) decision making is the main goal of the field of recommender systems. In particular, the goal of recommender systems is to provide easily accessible, high-quality recommendations for a large user community.

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