Book contents
- Frontmatter
- Contents
- Foreword
- Preface
- 1 Introduction
- PART I INTRODUCTION TO BASIC CONCEPTS
- 2 Collaborative recommendation
- 3 Content-based recommendation
- 4 Knowledge-based recommendation
- 5 Hybrid recommendation approaches
- 6 Explanations in recommender systems
- 7 Evaluating recommender systems
- 8 Case study: Personalized game recommendations on the mobile Internet
- PART II RECENT DEVELOPMENTS
- Bibliography
- Index
3 - Content-based recommendation
from PART I - INTRODUCTION TO BASIC CONCEPTS
Published online by Cambridge University Press: 05 August 2012
- Frontmatter
- Contents
- Foreword
- Preface
- 1 Introduction
- PART I INTRODUCTION TO BASIC CONCEPTS
- 2 Collaborative recommendation
- 3 Content-based recommendation
- 4 Knowledge-based recommendation
- 5 Hybrid recommendation approaches
- 6 Explanations in recommender systems
- 7 Evaluating recommender systems
- 8 Case study: Personalized game recommendations on the mobile Internet
- PART II RECENT DEVELOPMENTS
- Bibliography
- Index
Summary
From our discussion so far we see that for applying collaborative filtering techniques, except for t he user ratings, nothing has to be known about the items to be recommended. The main advantage of this is, of course, that the costly task of providing detailed and up-to-date item descriptions to the system is avoided. The other side of the coin, however, is that with a pure collaborative filtering approach, a very intuitive way of selecting recommendable products based on their characteristics and the specific preferences of a user is not possible: in the real world, it would be straightforward to recommend the new Harry Potter book to Alice, if we know that (a) this book is a fantasy novel and (b) Alice has always liked fantasy novels. An electronic recommender system can accomplish this task only if two pieces of information are available: a description of the item characteristics and a user profile that somehow describes the (past) interests of a user, maybe in terms of preferred item characteristics. The recommendation task then consists of determining the items that match the user's preferences best. This process is commonly called content-based recommendation. Although such an approach must rely on additional information about items and user preferences, it does not require the existence of a large user community or a rating history – that is, recommendation lists can be generated even if there is only one single user.
- Type
- Chapter
- Information
- Recommender SystemsAn Introduction, pp. 51 - 80Publisher: Cambridge University PressPrint publication year: 2010
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