@inproceedings{park2007.2,
abstract = {The PageRank algorithm is used in Web information
retrieval to calculate a single list of popularity
scores for each page in the Web. These popularity
scores are used to rank query results when presented
to the user. By using the structure of the entire
Web to calculate one score per document, we are
calculating a general popularity score, not
particular to any community. Therefore, the PageRank
scores are more suited to general queries. In this
paper, we introduce a more general form of PageRank,
using Web multi-resolution community-based
popularity scores, where each document obtains a
popularity score dependent on a given Web
community. When a query is related to a specific
community, we choose the associated set of
popularity scores and order the query results
accordingly. Using Web-community based popularity
scores, we achieved an 11\% increase in precision
over PageRank.},
author = {Laurence A. F. Park and Kotagiri Ramamohanarao},
booktitle = {The Proceedings of the 2007 ACM Conference on Information and Knowledge Management},
date-added = {2008-06-27 11:41:05 +1000},
date-modified = {2008-06-30 15:32:42 +1000},
doi = {10.1145/1321440.1321517},
keywords = {pagerank; laurence},
month = {November},
pages = {545--552},
title = {Mining Web multi-resolution community-based popularity for information retrieval},
url = {http://www.csse.unimelb.edu.au/~lapark/peerRank-snmf.pdf},
year = {2007},
bdsk-url-1 = {http://www.csse.unimelb.edu.au/~lapark/peerRank-snmf.pdf},
bdsk-url-2 = {http://dx.doi.org/10.1145/1321440.1321517}
}
@inproceedings{park2007.4,
abstract = { Rank-biased precision (RBP) is a new method of
information retrieval system evaluation that takes
into account any uncertainty due to incomplete
relevance judgements for a given document and query
set. To do so, RBP uses a model of user persistence.
In this article, we will present a statistical
analysis of the RBP user persistence model to
observe how the user persistence value affects the
user persistence distribution. We also provide a
method of fitting data from existing users to the
persistence model, in order to compute their
persistence value. Using the Microsoft MSN query
log, we were able to demonstrate a typical
distribution of the user persistence value and show
that it closely resembles a reverse lognormal
distribution, with a mean of p = 0.78.},
author = {Laurence A.~F.~Park and Yuye Zhang},
booktitle = {The Proceedings of the Twelfth Australasian Document Computing Symposium},
date-added = {2007-11-16 15:37:03 +1100},
date-modified = {2008-06-30 15:32:53 +1000},
keywords = {laurence},
title = {On the Distribution of User Persistence for Rank-Biased Precision},
url = {http://www.csse.unimelb.edu.au/~lapark/rbp-persistence.pdf},
year = {2007},
bdsk-url-1 = {http://www.csse.unimelb.edu.au/~lapark/rbp-persistence.pdf}
}
@inproceedings{guo2007,
abstract = {Web page prefetching techniques are used to
address the access latency problem of the
Internet. To perform successful prefetching, we must
be able to predict the next set of pages that will
be accessed by users. The PageRank algorithm used by
Google is able to compute the popularity of a set of
Web pages based on their link structure. In this
paper, a novel PageRank-like algorithm is proposed
for conducting Web page predction. Two biasing
factors are adopted to personalize PageRank, so that
it favors the pages that are more important to
users. One factor is the length of time spent on
visiting a page and the other is the frequency that
a page was visited. The experiments conducted show
that using these two factors simultaneously to bias
PageRank results in more accurate Web page
prediction than other methods that use only one of
these two factors.},
author = {Yong Zhen Guo and Kotagiri Ramamohanarao and Laurence A.~F.~Park},
booktitle = {The Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence},
date-added = {2007-10-19 12:23:53 +1000},
date-modified = {2008-06-30 15:30:51 +1000},
doi = {10.1109/WI.2007.58},
keywords = {laurence},
month = {November},
organization = {IEEE Computer Society},
pages = {687-690},
title = {Personalized PageRank for Web Page Prediction Based on Access Time-Length and Frequency},
url = {http://www.cs.mu.oz.au/~lapark/pagerank-prediction.pdf},
year = {2007},
bdsk-url-1 = {http://www.cs.mu.oz.au/~lapark/pagerank-prediction.pdf},
bdsk-url-2 = {http://dx.doi.org/10.1109/WI.2007.58}
}
@inproceedings{DBLP:conf/pakdd/ParkR07,
abstract = {Many queries on collections of text documents are
too short to produce informative results. Automatic
query expansion is a method of adding terms to the
query without interaction from the user in order to
obtain more refined results. In this investigation,
we examine our novel automatic query expansion
method using the probabilistic latent semantic
thesaurus, which is based on probabilistic latent
semantic analysis. We show how to construct the
thesaurus by mining text documents for probabilistic
term relationships, and we show that by using the
latent semantic thesaurus, we can overcome many of
the problems associated to latent semantic analysis
on large document sets which were previously
identified. Experiments using TREC document sets
show that our term expansion method out performs the
popular probabilistic pseudo-relevance feedback
method by 7.3\%.},
author = {Laurence A. F. Park and Kotagiri Ramamohanarao},
bibsource = {DBLP, http://dblp.uni-trier.de},
booktitle = {The Eleventh Pacific-Asia Conference on Knowledge Discovery and Data Mining Workshop},
crossref = {DBLP:conf/pakdd/2007},
date-added = {2007-07-11 15:45:22 +1000},
date-modified = {2008-06-30 15:30:19 +1000},
doi = {10.1007/978-3-540-71701-0_24},
keywords = {probabilistic latent semantic analysis; laurence},
pages = {224-235},
title = {Query Expansion Using a Collection Dependent Probabilistic Latent Semantic Thesaurus.},
url = {http://www.csse.unimelb.edu.au/~lapark/plsThesaurus.pdf},
year = {2007},
bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-540-71701-0%5C_24},
bdsk-url-2 = {http://www.csse.unimelb.edu.au/~lapark/plsThesaurus.pdf},
bdsk-url-3 = {http://dx.doi.org/10.1007/978-3-540-71701-0_24}
}
@proceedings{DBLP:conf/pakdd/2007,
bibsource = {DBLP, http://dblp.uni-trier.de},
booktitle = {PAKDD},
date-added = {2007-07-11 15:45:22 +1000},
date-modified = {2007-07-24 06:15:21 +0200},
editor = {Zhi-Hua Zhou and Hang Li and Qiang Yang},
isbn = {978-3-540-71700-3},
keywords = {data mining},
publisher = {Springer},
series = {Lecture Notes in Computer Science},
title = {Advances in Knowledge Discovery and Data Mining, 11th Pacific-Asia Conference, PAKDD 2007, Nanjing, China, May 22-25, 2007, Proceedings},
volume = {4426},
year = {2007}
}
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