Michael P. O'Mahony Science and Informatics University College Dublin...  Home/Search
Context
Related
| fezzik.ucd.ie/bib/...fp8075omahony.pdf Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: fezzik.ucd.ie/bib/index (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: In this paper, we propose a framework that enables the detection of noise in recommender system databases. We consider two classes of noise: natural and malicious noise. The issue of natural noise arises from imperfect user behaviour (e.g. erroneous/careless preference selection) and the various rating collection processes that are employed. Malicious noise concerns the deliberate attempt to bias system output in some particular manner. We argue that both classes of noise are important and can... (Update)
Active bibliography (related documents): More All
0.7: User Profiling in Personal . . . - Godoy, al. (2005) (Correct)
0.6: Efficient and Secure Collaborative Filtering through - Intelligent Neighbour.. (Correct)
0.5: A Trust-enhanced Recommender System Application: Moleskiing - Avesani, Massa, Tiella (2004) (Correct)
Similar documents based on text:
0.0: Unknown - (Correct)
BibTeX entry: (Update)
@misc{ science-detecting,
author = "Michael Mahony Science",
title = "Detecting Noise in Recommender System Databases",
url = "citeseer.ist.psu.edu/746095.html" }
Citations (may not include all citations):254 Grouplens: An open architecture for collaborative filtering .. - Resnick, Iacovou et al. - 1994
188 Empirical analysis of predictive algorithms for collaborativ.. - Breese, Heckerman et al. - 1998
102 An algorithmic framework for performing collaborative filter.. (context) - Herlocker, Konstan et al. - 1999
24 Recommender systems in e--commerce - Schafer, Konstan et al. - 1999
13 Collaborative filtering with privacy via factor analysis - Canny - 2002
11 Shilling recommender systems for fun and profit - Lam, Riedl - 2004
5 Developing trust in recommender agents - Montaner, Lopez et al. - 2002
4 Recommender systems for large--scale e--commerce: Scalable n.. - Sarwar, Karypis et al. - 2002
4 Recommender systems -- introduction to the special section (context) - Resnick, Varian - 1997
3 An evaluation of the performance of collaborative filtering (context) - O'Mahony, Hurley et al. - 2003
2 Trust--aware collaborative filtering for recommender systems - Massa, Avesani - 2004
2 Trust in recommender systems (context) - O'Donovan, Smyth - 2005
1 Towards Robust and E#cient Automated Collaborative Filtering (context) - O'Mahony - 2004
1 Limited knowledge shilling attacks in collaborative filterin.. (context) - Burke, Mobasher et al. - 2005
1 cient and secure collaborative filtering through intelligent.. (context) - O'Mahony, Hurley et al. - 2004
1 Moleskiing: A trust--aware decentralised recommender system (context) - Avesani, Massa et al. - 2004
1 ective attack models for shilling item-based collaborative f.. (context) - Mobasher, Burke et al. - 2005
1 Recommender systems: Attack types and strategies (context) - O'Mahony, Hurley et al. - 2005
Documents on the same site (http://fezzik.ucd.ie/bib/index.html): More
Collaborative Recommendation: A Robustness Analysis - O'Mahony, Hurley.. (2003) (Correct)
Utility-Based Neighbourhood Formation for Efficient and - Robust Collaborative.. (Correct)
Recommender Systems: Attack Types and Strategies - Michael Mahony And (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC