Predictive auto-completion for query in search engine

Vinay Singh, Dheeraj Kumar Purohit, Vimal Kumar, Pratima Verma and Ankita Malviya

International Journal of Business Information Systems, 2018, vol. 28, issue 3, 299-314

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.

Keywords: auto-complete; prefix tree; hashing; auto-correction; internet movie database; IMDB; query auto-completion; QAC; prefix; search engine. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=92528 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:28:y:2018:i:3:p:299-314

Access Statistics for this article

More articles in International Journal of Business Information Systems from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().