In this paper, we describe a collaborative filtering approach that aims to use features of users and items to better represent the problem space and to provide better recommendations to users. The goal of the work is to show that a graph-based representation of the problem domain, and a constrained spreading activation approach to effect retrieval, has as good, or better, performance than a traditional collaborative filtering approach using Pearson Correlation. However, in addition, the representation and approach proposed can be easily extended to incorporate additional information.
%0 Book Section
%1 citeulike:5148268
%A Griffith, Josephine
%A O'riordan, Colm
%A Sorensen, Humphrey
%D 2006
%J Knowledge-Based Intelligent Information and Engineering Systems
%K recommender, spread-activation
%P 766--773
%R 10.1007/11893011_97
%T A Constrained Spreading Activation Approach to Collaborative Filtering
%U http://dx.doi.org/10.1007/11893011_97
%X In this paper, we describe a collaborative filtering approach that aims to use features of users and items to better represent the problem space and to provide better recommendations to users. The goal of the work is to show that a graph-based representation of the problem domain, and a constrained spreading activation approach to effect retrieval, has as good, or better, performance than a traditional collaborative filtering approach using Pearson Correlation. However, in addition, the representation and approach proposed can be easily extended to incorporate additional information.
@incollection{citeulike:5148268,
abstract = {{In this paper, we describe a collaborative filtering approach that aims to use features of users and items to better represent the problem space and to provide better recommendations to users. The goal of the work is to show that a graph-based representation of the problem domain, and a constrained spreading activation approach to effect retrieval, has as good, or better, performance than a traditional collaborative filtering approach using Pearson Correlation. However, in addition, the representation and approach proposed can be easily extended to incorporate additional information.}},
added-at = {2017-11-15T17:02:25.000+0100},
author = {Griffith, Josephine and O'riordan, Colm and Sorensen, Humphrey},
biburl = {https://www.bibsonomy.org/bibtex/277988cdbc694d512639187ffd9d626fb/brusilovsky},
citeulike-article-id = {5148268},
citeulike-linkout-0 = {http://dx.doi.org/10.1007/11893011_97},
citeulike-linkout-1 = {http://www.springerlink.com/content/d54849767304612n},
doi = {10.1007/11893011_97},
interhash = {764cbec50f1ab9d99d11d12945e661f0},
intrahash = {77988cdbc694d512639187ffd9d626fb},
journal = {Knowledge-Based Intelligent Information and Engineering Systems},
keywords = {recommender, spread-activation},
pages = {766--773},
posted-at = {2009-07-14 15:14:34},
priority = {2},
timestamp = {2017-11-15T17:02:25.000+0100},
title = {{A Constrained Spreading Activation Approach to Collaborative Filtering}},
url = {http://dx.doi.org/10.1007/11893011_97},
year = 2006
}