%0 Book Section %1 pazzani2007contentbased %A Pazzani, Michael J. %A Billsus, Daniel %B The Adaptive Web %C Berlin/Heidelberg %D 2007 %E Brusilovsky, Peter %E Kobsa, Alfred %E Nejdl, Wolfgang %I Springer %K recommender content %P 325--341 %R 10.1007/978-3-540-72079-9_10 %T Content-Based Recommendation Systems %U http://dx.doi.org/10.1007/978-3-540-72079-9_10 %V 4321 %X 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 re commend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user. %@ 978-3-540-72078-2

@incollection{pazzani2007contentbased, 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 re commend. The profile is often created and updated automatically in response to feedback on the desirability of items that have been presented to the user.}, added-at = {2012-12-14T10:01:01.000+0100}, address = {Berlin/Heidelberg}, author = {Pazzani, Michael J. and Billsus, Daniel}, biburl = {https://www.bibsonomy.org/bibtex/278e31f13da63399178ec232e4f2f341d/stair}, booktitle = {The Adaptive Web}, doi = {10.1007/978-3-540-72079-9_10}, editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang}, interhash = {ddb744ce8c0d20a881db91060b63ff4e}, intrahash = {78e31f13da63399178ec232e4f2f341d}, isbn = {978-3-540-72078-2}, keywords = {recommender content}, pages = {325--341}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2013-01-14T14:35:34.000+0100}, title = {Content-Based Recommendation Systems}, url = {http://dx.doi.org/10.1007/978-3-540-72079-9_10}, volume = 4321, year = 2007 }