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» Predicting user satisfaction from subject satisfaction
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SIGIR
2009
ACM
15 years 4 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
WWW
2008
ACM
15 years 10 months ago
Online learning from click data for sponsored search
Sponsored search is one of the enabling technologies for today's Web search engines. It corresponds to matching and showing ads related to the user query on the search engine...
Massimiliano Ciaramita, Vanessa Murdock, Vassilis ...
86
Voted
KDD
2007
ACM
178views Data Mining» more  KDD 2007»
15 years 9 months ago
Practical learning from one-sided feedback
In many data mining applications, online labeling feedback is only available for examples which were predicted to belong to the positive class. Such applications include spam filt...
D. Sculley
BCSHCI
2008
14 years 11 months ago
Heterogeneity in the usability evaluation process
Current prediction models for usability evaluations are based on stochastic distributions derived from series of Bernoulli processes. The underlying assumption of these models is ...
Martin Schmettow
CORR
2010
Springer
169views Education» more  CORR 2010»
14 years 3 months ago
Recommender Systems by means of Information Retrieval
In this paper we present a method for reformulating the Recommender Systems problem in an Information Retrieval one. In our tests we have a dataset of users who give ratings for s...
Alberto Costa, Fabio Roda