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Ozmutlu, S.; Spink, A.; Ozmutlu, H.C.: ¬A day in the life of Web searching : an exploratory study (2004)
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- Abstract
- Understanding Web searching behavior is important in developing more successful and cost-efficient Web search engines. We provide results from a comparative time-based Web study of US-based Excite and Norwegian-based Fast Web search logs, exploring variations in user searching related to changes in time of the day. Findings suggest: (1) fluctuations in Web user behavior over the day, (2) user investigations of query results are much longer, and submission of queries and number of users are much higher in the mornings, and (3) some query characteristics, including terms per query and query reformulation, remain steady throughout the day. Implications and further research are discussed.
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Ozmutlu, S.; Spink, A.; Ozmutlu, H.C.: Multimedia Web searching trends : 1997-2001 (2003)
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- Abstract
- Multimedia is proliferating on Web sites, as the Web continues to enhance the integration of multimedia and textual information. In this paper we examine trends in multimedia Web searching by Excite users from 1997 to 2001. Results from an analysis of 1,025,910 Excite queries from 2001 are compared to similar Excite datasets from 1997 to 1999. Findings include: (1) queries per multimedia session have decreased since 1997 as a proportion of general queries due to the introduction of multimedia buttons near the query box, (2) multimedia queries identified are longer than non-multimedia queries, and (3) audio queries are more prevalent than image or video queries in identified multimedia queries. Overall, we see multimedia Web searching undergoing major changes as Web content and searching evolves.
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Spink, A.; Ozmutlu, H.C.; Ozmutlu, S.: Multitasking information seeking and searching processes (2002)
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- Abstract
- Recent studies show that humans engage in multitasking behaviors as they seek and search information retrieval (IR) systems for information on more than one topic at the same time. For example, a Web search session by a single user may consist of searching on single topics or multitasking. Findings are presented from four separate studies of the prevalence of multitasking information seeking and searching by Web, IR system, and library users. Incidence of multitasking identified in the four different studies included: (1) users of the Excite Web search engine who completed a survey form, (2) Excite Web search engine users filtered from an Excite transaction log from 20 December 1999, (3) mediated on-line databases searches, and (4) academic library users. Findings include: (1) multitasking information seeking and searching is a common human behavior, (2) users may conduct information seeking and searching on related or unrelated topics, (3) Web or IR multitasking search sessions are longer than single topic sessions, (4) mean number of topics per Web search ranged of 1 to more than 10 topics with a mean of 2.11 topic changes per search session, and (4) many Web search topic changes were from hobbies to shopping and vice versa. A more complex model of human seeking and searching levels that incorporates multitasking information behaviors is presented, and a theoretical framework for human information coordinating behavior (HICB) is proposed. Multitasking information seeking and searching is developing as major research area that draws together IR and information seeking studies toward a focus on IR within the context of human information behavior. Implications for models of information seeking and searching, IR/Web systems design, and further research are discussed.
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Ozmutlu, H.C.; Cavdur, F.; Ozmutlu, S.: Cross-validation of neural network applications for automatic new topic identification (2008)
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- Abstract
- The purpose of this study is to provide results from experiments designed to investigate the 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. Sample data logs from the FAST and Excite search engines are used in this study. The results of the study show that identification of topic shifts and continuations on a particular Web search engine user session can be achieved with neural networks that are trained on a different Web search engine data log. Although FAST and Excite search engine users differ with respect to some user characteristics (e.g., number of queries per session, number of topics per session), the results of this study demonstrate that both search engine users display similar characteristics as they shift from one topic to another during a single search session. The key finding of this study is that a neural network that is trained on a selected data log could be universal; that is, it can be applicable on all Web search engine transaction logs regardless of the source of the training data log.
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Gencosman, B.C.; Ozmutlu, H.C.; Ozmutlu, S.: Character n-gram application for automatic new topic identification (2014)
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- Abstract
- The widespread availability of the Internet and the variety of Internet-based applications have resulted in a significant increase in the amount of web pages. Determining the behaviors of search engine users has become a critical step in enhancing search engine performance. Search engine user behaviors can be determined by content-based or content-ignorant algorithms. Although many content-ignorant studies have been performed to automatically identify new topics, previous results have demonstrated that spelling errors can cause significant errors in topic shift estimates. In this study, we focused on minimizing the number of wrong estimates that were based on spelling errors. We developed a new hybrid algorithm combining character n-gram and neural network methodologies, and compared the experimental results with results from previous studies. For the FAST and Excite datasets, the proposed algorithm improved topic shift estimates by 6.987% and 2.639%, respectively. Moreover, we analyzed the performance of the character n-gram method in different aspects including the comparison with Levenshtein edit-distance method. The experimental results demonstrated that the character n-gram method outperformed to the Levensthein edit distance method in terms of topic identification.
