chapter

Content-based recommendation systems

Authors:

Michael J. Pazzani,

Daniel BillsusAuthors Info & Claims

The adaptive web: methods and strategies of web personalization

January 2007

Pages 325 - 341

Published: 01 January 2007 Publication History

Abstract

This chapter discusses content-based recommendation systems, i.e., systems that recommend an item to a user based upon a description of the item and a profile of the user's interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.

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Published In

cover image Guide books

The adaptive web: methods and strategies of web personalization

January 2007

763 pages

ISBN:9783540720782

Editors:
Peter Brusilovsky
School of Information Sciences, University of Pittsburgh, Pittsburgh, PA
,
Alfred Kobsa
Donald Bren School of Information and Computer Science, University of California, Irvine, CA
,
Wolfgang Nejdl
L3S Research Center, University of Hannover, Hannover, Germany

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2007

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https://dl.acm.org/doi/10.1109/TKDE.2023.3332671
Cavenaghi EZanga AStella FZanker M(2023)Towards a Causal Decision-Making Framework for Recommender SystemsACM Transactions on Recommender Systems10.1145/36291692:2(1-34)Online publication date: 26-Oct-2023
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Cavenaghi ESottocornola GStella FZanker M(2023)A Systematic Study on Reproducibility of Reinforcement Learning in Recommendation SystemsACM Transactions on Recommender Systems10.1145/35965191:3(1-23)Online publication date: 14-Jul-2023
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