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http://hdl.handle.net/11452/22183
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DC Field | Value | Language |
---|---|---|
dc.date.accessioned | 2021-10-01T11:42:20Z | - |
dc.date.available | 2021-10-01T11:42:20Z | - |
dc.date.issued | 2006 | - |
dc.identifier.citation | Özmutlu, S. (2006). ''Automatic new topic identification using multiple linear regression''. Automatic new topic identification using multiple linear regression, 42(4), 934-950. | en_US |
dc.identifier.issn | 0306-4573 | - |
dc.identifier.issn | 1873-5371 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S0306457305001378 | - |
dc.identifier.uri | https://doi.org/10.1016/j.ipm.2005.10.002 | - |
dc.identifier.uri | http://hdl.handle.net/11452/22183 | - |
dc.description.abstract | The purpose of this study is to provide automatic new topic identification of search engine query logs, and estimate the effect of statistical characteristics of search engine queries on new topic identification. By applying multiple linear regression and multi-factor ANOVA on a sample data log from the Excite search engine, we demonstrated that the statistical characteristics of Web search queries, such as time interval, search pattern and position of a query in a user session, are effective on shifting to a new topic. Multiple linear regression is also a successful tool for estimating topic shifts and continuations. The findings of this study provide statistical proof for the relationship between the non-semantic characteristics of Web search queries and the occurrence of topic shifts and continuations. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Science | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Information science & library science | en_US |
dc.subject | Information analysis | en_US |
dc.subject | Topic identification | en_US |
dc.subject | Information retrievals | en_US |
dc.subject | Search engine | en_US |
dc.subject | Regression analysis | en_US |
dc.subject | Regression | en_US |
dc.subject | Search engines | en_US |
dc.subject | Information retrieval | en_US |
dc.subject | Semantic | en_US |
dc.subject | ANOVA | en_US |
dc.subject | Multiple linear regression | en_US |
dc.subject | FMSS | en_US |
dc.subject | Topic identification | en_US |
dc.subject | Minimizing mean flowtime | en_US |
dc.subject | Web search queries | en_US |
dc.subject | Life | en_US |
dc.subject | Identification (control systems) | en_US |
dc.subject | Users | en_US |
dc.subject | ReaL-time methodology | en_US |
dc.subject | Information-seeking | en_US |
dc.subject | Trends | en_US |
dc.subject | Users | en_US |
dc.subject | Automatic programming | en_US |
dc.subject | Data reduction | en_US |
dc.title | Automatic new topic identification using multiple linear regression | en_US |
dc.type | Article | en_US |
dc.identifier.wos | 000236006600005 | tr_TR |
dc.identifier.scopus | 2-s2.0-29244483716 | tr_TR |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi | tr_TR |
dc.contributor.department | Uludağ Üniversitesi/Mühendislik Mimarlık Fakültesi/Endüstri Mühendisliği Bölümü. | tr_TR |
dc.identifier.startpage | 934 | tr_TR |
dc.identifier.endpage | 950 | tr_TR |
dc.identifier.volume | 42 | tr_TR |
dc.identifier.issue | 4 | tr_TR |
dc.relation.journal | Information Processing and Management | en_US |
dc.contributor.buuauthor | Özmutlu, Seda | - |
dc.contributor.researcherid | AAH-4480-2021 | tr_TR |
dc.subject.wos | Computer science, information systems | en_US |
dc.subject.wos | Information science & library science | en_US |
dc.indexed.wos | SCIE | en_US |
dc.indexed.wos | SSCI | en_US |
dc.indexed.scopus | Scopus | en_US |
dc.wos.quartile | Q2 (Computer science, information systems) | en_US |
dc.wos.quartile | Q1 (Information science & library science) | en_US |
dc.contributor.scopusid | 6603660605 | tr_TR |
dc.subject.scopus | Query Reformulation; Image Indexing; Digital Libraries | en_US |
Appears in Collections: | Scopus Web of Science |
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