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QFRDBF: Query Facet Recommendation Using Knowledge Centric DBSCAN and Firefly Optimization

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Digital Technologies and Applications (ICDTA 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 211))

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Abstract

The Internet contains approximately 40 trillion gigabytes of data. A vast amount of information is presented to the user for their query. With such enormous search results, it becomes impossible for the user to refine the results to find the contents that match their interest. Almost 92% of search traffic clicks of Google are confined only to the first page. Also, Google identifies that about 16% to 20% of searches are new each year. Hence, a method that enables the users to filter the search results based on some attributes to obtain the web content that matches their intention is vital. One such method is query facets. Query facets enable the user to filter the web results, making it easy for them to seek their desired results. This paper presents a novel approach to extract query facets. Query words are obtained from the queries upon query preprocessing. TF-IDF is applied to the preprocessed dataset and the query words to reorder the terms based on the frequency. The reordered terms are sorted based on concept similarity, and a knowledge centric DBSCAN algorithm is employed on the sorted items to generate facets.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

    Deepak Surya, Gerard Deepak & A. Santhanavijayan

Authors
  1. Deepak Surya
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  2. Gerard Deepak
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  3. A. Santhanavijayan
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Editor information

Editors and Affiliations

  1. ENSA, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

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Surya, D., Deepak, G., Santhanavijayan, A. (2021). QFRDBF: Query Facet Recommendation Using Knowledge Centric DBSCAN and Firefly Optimization. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2021. Lecture Notes in Networks and Systems, vol 211. Springer, Cham. https://doi.org/10.1007/978-3-030-73882-2_73

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