Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/24397
Title: Identifying the optimal set of parameters for new topic identification through experimental design
Authors: Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
Özmutllu, Seda
AAH-4480-2021
6603660605
Keywords: Topic identification
Neural network
Session identification
Session identification
Experimental design
ANOVA
WEB
Computer science
Engineering
Operations research & management science
Analysis of variance (ANOVA)
Design of experiments
Fire fighting equipment
Neural networks
Parameter estimation
Statistics
Neural network structures
Output levels
Performance measure
Search sessions
Session identification
Threshold-value
Topic identification
Transaction log
Search engines
Issue Date: Dec-2010
Publisher: Pergamon-Elsevier Science
Citation: Özmutlu, S. (2010). "Identifying the optimal set of parameters for new topic identification through experimental design". Expert Systems with Applications, 37(12), 7947-7968.
Abstract: Users are interested in multiple topics during a search session, and identifying the boundaries of search sessions is an important task. This study proposes to use neural networks for defining the topic boundaries in search engine transaction logs, and is a part of ongoing research on automatic new topic identification. The objective of the study is to determine the best set of parameters for neural networks that are designed to perform automatic new topic identification. Sample data logs from FAST (currently owned by Yahoo) and Excite (currently owned by IAC Search & Media) search engines were analyzed. The findings show that neural networks are fairly successful in identifying topic continuations and shifts in search engine transaction logs. The choice of the neural network structure depends on which performance measure is more important to the user. For a certain performance measure, there is a set of parameters of neural networks that will increase the performance of new topic identification in search engine transaction logs. In addition, the threshold value of the output level of neural networks is the most influential parameter on the performance of new topic identification.
URI: https://doi.org/10.1016/j.eswa.2010.04.040
https://www.sciencedirect.com/science/article/abs/pii/S0957417410003404
http://hdl.handle.net/11452/24397
ISSN: 0957-4174
1873-6793
Appears in Collections:Scopus
Web of Science

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