@inproceedings{bezdek2010.3,
address = {Piscataway, NJ},
author = {James Bezdek and Timothy Havens and James Keller and Christopher Leckie and Laurence Park and Marimuthu Palaniswami and Sutharshan Rajasegarar},
booktitle = {Proceedings of 2010 Conference on Fuzzy Systems (FUZZ-IEEE)},
date-added = {2010-04-06 10:02:50 +1000},
date-modified = {2010-04-06 10:05:22 +1000},
doi = {10.1109/FUZZY.2010.5584464},
keywords = {laurence},
pages = {909-916},
publisher = {IEEE Press},
title = {Clustering elliptical anomalies in sensor networks},
year = {2010},
url = {http://staff.scem.uws.edu.au/~lapark/publications/clust-ellipse-2010.pdf},
bdsk-url-1 = {http://dx.doi.org/10.1109/FUZZY.2010.5584464}
}
@article{zhang2008,
abstract = { Search effectiveness metrics are used to evaluate
the quality of the answer lists returned by search
services, usually based on a set of relevance
judgments. One plausible way of calculating an
effectiveness score for a system run is to compute
the inner-product of the run's relevance
vector and a ``utility'' vector, where
the ith element in the utility vector represents the
relative benefit obtained by the user of the system
if they encounter a relevant document at depth i in
the ranking. This paper uses such a framework to
examine the user behavior patterns-and hence
utility weightings-that can be inferred from
a web query log. We describe a process for
extrapolating user observations from query log
clickthroughs, and employ this user model to measure
the quality of effectiveness weighting
distributions. Our results show that for measures
with static distributions (that is, utility
weighting schemes for which the weight vector is
independent of the relevance vector), the geometric
weighting model employed in the rank-biased
precision effectiveness metric offers the closest
fit to the user observation model. In addition,
using past TREC data as to indicate likelihood of
relevance, we also show that the distributions
employed in the BPref and MRR metrics are the best
fit out of the measures for which static
distributions do not exist.},
author = {Yuye Zhang and Laurence A.~F.~Park and Alistair Moffat},
journal = {The Journal of Information Retrieval},
date-added = {2008-02-05 08:08:44 +1100},
date-modified = {2009-06-19 15:35:53 +1000},
doi = {10.1007/s10791-009-9099-7},
issn = {1573-7659},
keywords = {laurence},
pages = {1-24},
publisher = {Springer Netherlands},
title = {Click-Based Evidence for Decaying Weight Distributions in Search Effectiveness Metrics},
year = {2010},
url = {http://staff.scem.uws.edu.au/~lapark/publications/metric-dist-2010.pdf},
contribution = {70\%},
era2010rank = {B},
sniprank = {2.92},
citations = {22},
bdsk-url-1 = {http://dx.doi.org/10.1007/s10791-009-9099-7}
}
@inproceedings{park2010.6,
author = {Laurence A.~F.~Park},
booktitle = {Proceedings of the Fifteenth Australasian Document Computing Symposium},
editor = {Andrew Turpin and Falk Scholer and Andrew Trotman},
keywords = {laurence},
title = {Confidence Intervals for Information Retrieval Evaluation},
url = {http://staff.scm.uws.edu.au/~lapark/publications/confidence-2010.pdf},
year = {2010},
bdsk-url-1 = {http://staff.scm.uws.edu.au/~lapark/publications/confidence-2010.pdf}
}
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