Investigating the performance of automatic new topic identification across multiple datasets

Özmutlu, H. Cenk; Çavdur, Fatih; Spink, Amanda; Ozmutlu, Seda

doi:10.1002/meet.1450430129

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Another observation is that the results obtained in this study also support the results published in a previous study [86]. The data set that has more topic shifts would be expected to be more successful in terms of shift-based measures.…”

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confidence: 88%

Markovian analysis for automatic new topic identification in search engine transaction logs

Özmutlu¹

2009

Appl Stoch Models Bus & Ind

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Topic analysis of search engine user queries is an important task, since successful exploitation of the topic of queries can result in the design of new information retrieval algorithms for more efficient search engines. Identification of topic changes within a user search session is a key issue in analysis of search engine user queries. This study presents an application of Markov chains in the area of search engine research to automatically identify topic changes in a user session by using statistical characteristics of queries, such as time intervals, query reformulation patterns and the continuation/shift status of the previous query. The findings show that Markov chains provide fairly successful results for automatic new topic identification with a high level of estimation for topic continuations and shifts.

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supporting

confidence: 88%

Markovian analysis for automatic new topic identification in search engine transaction logs

Özmutlu¹

2009

Appl Stoch Models Bus & Ind

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“…We also use seven categories of search patterns in this study, which are as follows (Ozmutlu, H. et al , 2004, 2006a,b; Ozmutlu, S. et al , 2006), Ozmutlu and Cavdur (2005): Unique (new) . The second query has no common term compared to the first query:Example: Query j : Automobile.Query j+1 : Harry Potter. Next page (browsing) .…”

Section: Methodsmentioning

confidence: 99%

Using conditional probabilities for automatic new topic identification

Ozmutlu

Özmutlu

Buyuk

2007

Online Information Review

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PurposeOne of the most important dimensions of search engine user information seeking behaviour is content‐based behaviour. One of the main elements in developing a personalised intelligent search engine is new topic identification. The purpose of this study is to perform automatic new topic identification in search engine transaction logs using conditional probabilities of new topic arrivals.Design/methodology/approachSample data logs from FAST (currently owned by Yahoo!) and Excite (currently owned by IAC Search & Media) are used in the study. Conditional probabilities of new topic arrivals and topic continuations given query category are used to estimate new topic arrivals.FindingsThe findings of this study show that the conditional probability approach reduced overestimation of topic shifts, increasing some performance measures to their highest ever value compared to previous studies. A straightforward procedure such as the conditional probability approach can be as successful as, and for some measures more successful than, more complex methods applied in previous automatic new topic identification studies.Originality/valueA straightforward procedure that can enable fast automatic new topic identification, a problem not yet solved, and an important step towards personalised search engines.

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“…The researchers reported that topic shifts were estimated correctly, with a 77.8% precision in the overall database. Özmutlu, Çavdur, Spink, and Özmutlu (2005) have shown that one can train neural networks using multiple search logs. Özmutlu, Çavdur, and Özmutlu (2008) conducted a cross-validation of an artificial neural network application to automatically identify topic changes in Web search engine user sessions by using data logs of different Web search engines for training and testing the neural network.…”

Section: Review Of Literaturementioning

confidence: 99%

Identification of factors predicting clickthrough in Web searching using neural network analysis

Zhang

Jansen

Spink

2008

J. Am. Soc. Inf. Sci.

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In this research, we aim to identify factors that significantly affect the clickthrough of Web searchers. Our underlying goal is determine more efficient methods to optimize the clickthrough rate. We devise a clickthrough metric for measuring customer satisfaction of search engine results using the number of links visited, number of queries a user submits, and rank of clicked links. We use a neural network to detect the significant influence of searching characteristics on future user clickthrough. Our results show that high occurrences of query reformulation, lengthy searching duration, longer query length, and the higher ranking of prior clicked links correlate positively with future clickthrough. We provide recommendations for leveraging these findings for improving the performance of search engine retrieval and result ranking, along with implications for search engine marketing.

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Investigating the performance of automatic new topic identification across multiple datasets

Abstract: Recent studies on automatic new topic identification in

Cited by 4 publications

References 26 publications

Markovian analysis for automatic new topic identification in search engine transaction logs

Markovian analysis for automatic new topic identification in search engine transaction logs

Using conditional probabilities for automatic new topic identification

Identification of factors predicting clickthrough in Web searching using neural network analysis

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