A comparison of LSA, wordNet and PMI-IR for predicting user click behavior
I. Kaur, and A. Hornof. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, page 51--60. New York, NY, USA, ACM, (2005)
DOI: 10.1145/1054972.1054980
Abstract
A predictive tool to simulate human visual search behavior would help interface designers inform and validate their design. Such a tool would benefit from a semantic component that would help predict search behavior even in the absence of exact textual matches between goal and target. This paper discusses a comparison of three semantic systems-LSA, WordNet and PMI-IR-to evaluate their performance in predicting the link that people would select given an information goal and a webpage. PMI-IR best predicted human performance as observed in a user study.
Description
A comparison of LSA, wordNet and PMI-IR for predicting user click behavior
%0 Conference Paper
%1 Kaur:2005:CLW:1054972.1054980
%A Kaur, Ishwinder
%A Hornof, Anthony J.
%B Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
%C New York, NY, USA
%D 2005
%I ACM
%K lsa relatedness semantic toread wordnet
%P 51--60
%R 10.1145/1054972.1054980
%T A comparison of LSA, wordNet and PMI-IR for predicting user click behavior
%U http://doi.acm.org/10.1145/1054972.1054980
%X A predictive tool to simulate human visual search behavior would help interface designers inform and validate their design. Such a tool would benefit from a semantic component that would help predict search behavior even in the absence of exact textual matches between goal and target. This paper discusses a comparison of three semantic systems-LSA, WordNet and PMI-IR-to evaluate their performance in predicting the link that people would select given an information goal and a webpage. PMI-IR best predicted human performance as observed in a user study.
%@ 1-58113-998-5
@inproceedings{Kaur:2005:CLW:1054972.1054980,
abstract = {A predictive tool to simulate human visual search behavior would help interface designers inform and validate their design. Such a tool would benefit from a semantic component that would help predict search behavior even in the absence of exact textual matches between goal and target. This paper discusses a comparison of three semantic systems-LSA, WordNet and PMI-IR-to evaluate their performance in predicting the link that people would select given an information goal and a webpage. PMI-IR best predicted human performance as observed in a user study.},
acmid = {1054980},
added-at = {2013-04-23T17:50:39.000+0200},
address = {New York, NY, USA},
author = {Kaur, Ishwinder and Hornof, Anthony J.},
biburl = {https://www.bibsonomy.org/bibtex/2f8c070cb738ea82a40838b1eb8257e31/hotho},
booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
description = {A comparison of LSA, wordNet and PMI-IR for predicting user click behavior},
doi = {10.1145/1054972.1054980},
interhash = {ea35528c6c3ea3ca64cbbd6c6ae631ae},
intrahash = {f8c070cb738ea82a40838b1eb8257e31},
isbn = {1-58113-998-5},
keywords = {lsa relatedness semantic toread wordnet},
location = {Portland, Oregon, USA},
numpages = {10},
pages = {51--60},
publisher = {ACM},
series = {CHI '05},
timestamp = {2013-04-23T17:50:40.000+0200},
title = {A comparison of LSA, wordNet and PMI-IR for predicting user click behavior},
url = {http://doi.acm.org/10.1145/1054972.1054980},
year = 2005
}