Search (2 results, page 1 of 1)

  • × author_ss:"Ozmutlu, H.C."
  • × theme_ss:"Suchmaschinen"
  1. Ozmutlu, S.; Spink, A.; Ozmutlu, H.C.: ¬A day in the life of Web searching : an exploratory study (2004) 0.08 
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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.
  2. Gencosman, B.C.; Ozmutlu, H.C.; Ozmutlu, S.: Character n-gram application for automatic new topic identification (2014) 0.02 
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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.

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