| Authors: |
Barry Smyth
|
| Editors: |
Peter Brusilovsky
and Alfred Kobsa
and Wolfgang Nejdl
|
| URL: |
http://dx.doi.org/10.1007/978-3-540-72079-9_11 |
| Tags: |
adaptive
ai
assist
information
interaction
interface
management
paper
requirements
springer
user
v0805
web
|
| Abstract: |
Recommender systems try to help users access complex information spaces.
A good example is when they are used to help users to access online
product catalogs, where recommender systems have proven to be especially
useful for making product suggestions in response to evolving user
needs and preferences. Case-based recommendation is a form of content-based
recommendation that is well suited to many product recommendation
domains where individual products are described in terms of a well
defined set of features (e.g., price, colour, make, etc.). These
representations allow case-based recommenders to make judgments about
product similarities in order to improve the quality of their recommendations
and as a result this type of approach has proven to be very successful
in many e-commerce settings, especially when the needs and preferences
of users are ill-defined, as they often are. In this chapter we will
describe the basic approach to case-based recommendation, highlighting
how it differs from other recommendation technologies, and introducing
some recent advances that have led to more powerful and flexible
recommender systems. |
@incollection{Smyth07p342,
title = {Case-Based Recommendation},
address = {Berlin, Heidelberg},
author = {Barry Smyth},
booktitle = {The Adaptive Web: Methods and Strategies of Web Personalization},
chapter = {11},
crossref = {BrusilovskyKobsaNejdl2007},
editor = {Peter Brusilovsky and Alfred Kobsa and Wolfgang Nejdl},
pages = {342-376},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
url = {http://dx.doi.org/10.1007/978-3-540-72079-9_11},
volume = {4321},
year = {2007},
abstract = {Recommender systems try to help users access complex information spaces.
A good example is when they are used to help users to access online
product catalogs, where recommender systems have proven to be especially
useful for making product suggestions in response to evolving user
needs and preferences. Case-based recommendation is a form of content-based
recommendation that is well suited to many product recommendation
domains where individual products are described in terms of a well
defined set of features (e.g., price, colour, make, etc.). These
representations allow case-based recommenders to make judgments about
product similarities in order to improve the quality of their recommendations
and as a result this type of approach has proven to be very successful
in many e-commerce settings, especially when the needs and preferences
of users are ill-defined, as they often are. In this chapter we will
describe the basic approach to case-based recommendation, highlighting
how it differs from other recommendation technologies, and introducing
some recent advances that have led to more powerful and flexible
recommender systems.},
timestamp = {2008.02.10}, file = {SpringerLink:2007/Smyth07p342.pdf:PDF}, isbn = {978-3-540-72078-2}, owner = {flint},
keywords = {adaptive ai assist information interaction interface management paper requirements springer user v0805 web }
}
::