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» User evaluation of a market-based recommender system
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TKDD
2010
121views more  TKDD 2010»
13 years 6 months ago
Factor in the neighbors: Scalable and accurate collaborative filtering
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are ana...
Yehuda Koren
SIGIR
2009
ACM
14 years 2 months ago
Temporal collaborative filtering with adaptive neighbourhoods
Recommender Systems, based on collaborative filtering (CF), aim to accurately predict user tastes, by minimising the mean error achieved on hidden test sets of user ratings, afte...
Neal Lathia, Stephen Hailes, Licia Capra
SAICSIT
2009
ACM
14 years 2 months ago
A lightweight methodology to improve web accessibility
This paper introduces a methodology to improve the accessibility of websites with the use of free so-called automatic tools. The methodology has three iterative phases, namely ass...
Mardé Greeff, Paula Kotzé
IUI
2003
ACM
14 years 29 days ago
Personal choice point: helping users visualize what it means to buy a BMW
How do we know if we can afford a particular purchase? We can find out what the payments might be and check our balances on various accounts, but does this answer the question? Wh...
Andrew E. Fano, Scott W. Kurth
KDD
2012
ACM
187views Data Mining» more  KDD 2012»
11 years 10 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...