A Case-Based Reasoning Approach to Collaborative Filtering
R. Burke. Advances in Case-Based Reasoning, (2000)
Abstract
Collaborative filtering systems make recommendations based on the accumulation of ratings by many users. The process has a
case-based reasoning flavor: recommendations are generated by looking at the behavior of other users who are considered similar.However, the features associated with a user are semantically weak compared with those used by CBR systems. This researchexamines multi- dimensional or semantic ratings in which a system gets information about the reason behind a preference. Experiments show that metrics in which thesemantic meaning of each rating is taken into account have markedly superior performance than simpler techniques.
%0 Journal Article
%1 paper:burke:2000
%A Burke, Robin
%D 2000
%J Advances in Case-Based Reasoning
%K case-based collaborative-filtering filtering reasoning
%P 49--71
%T A Case-Based Reasoning Approach to Collaborative Filtering
%U http://dx.doi.org/10.1007/3-540-44527-7_32
%X Collaborative filtering systems make recommendations based on the accumulation of ratings by many users. The process has a
case-based reasoning flavor: recommendations are generated by looking at the behavior of other users who are considered similar.However, the features associated with a user are semantically weak compared with those used by CBR systems. This researchexamines multi- dimensional or semantic ratings in which a system gets information about the reason behind a preference. Experiments show that metrics in which thesemantic meaning of each rating is taken into account have markedly superior performance than simpler techniques.
@article{paper:burke:2000,
abstract = {Collaborative filtering systems make recommendations based on the accumulation of ratings by many users. The process has a
case-based reasoning flavor: recommendations are generated by looking at the behavior of other users who are considered similar.However, the features associated with a user are semantically weak compared with those used by CBR systems. This researchexamines multi- dimensional or semantic ratings in which a system gets information about the reason behind a preference. Experiments show that metrics in which thesemantic meaning of each rating is taken into account have markedly superior performance than simpler techniques.},
added-at = {2009-05-08T13:45:39.000+0200},
author = {Burke, Robin},
biburl = {https://www.bibsonomy.org/bibtex/2e9b80dcff4a52759fe5b4bc5453811f7/mschuber},
description = {SpringerLink - Book Chapter},
interhash = {32699ca95ee7feb041640d0b45ed7381},
intrahash = {e9b80dcff4a52759fe5b4bc5453811f7},
journal = {Advances in Case-Based Reasoning},
keywords = {case-based collaborative-filtering filtering reasoning},
pages = {49--71},
timestamp = {2009-05-08T13:45:39.000+0200},
title = {A Case-Based Reasoning Approach to Collaborative Filtering},
url = {http://dx.doi.org/10.1007/3-540-44527-7_32},
year = 2000
}