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Predictive auto-completion for query in search engine

Author

Listed:
  • Vinay Singh
  • Dheeraj Kumar Purohit
  • Vimal Kumar
  • Pratima Verma
  • Ankita Malviya

Abstract

The main goal of this research is to model an approach to give top-k predictive search results in search engine by the use of a combination of algorithmic and probabilistic approach and compare their processing time. Modified edit distance algorithm is used for spell auto-correction and prefix tree is used for auto-completion. Intersecting union list algorithm is also used for multi-query predictive results. Wikipedia dictionary words are used for a single word query dataset and Internet Movie Database (IMDB) movie list is crawl by a python crawler, which is built for this research. And the rating of the movie provided by IMDB and frequency of each word is used to rank words.

Suggested Citation

  • Vinay Singh & Dheeraj Kumar Purohit & Vimal Kumar & Pratima Verma & Ankita Malviya, 2018. "Predictive auto-completion for query in search engine," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 28(3), pages 299-314.
    • Handle: RePEc:ids:ijbisy:v:28:y:2018:i:3:p:299-314
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