Title: Predictive auto-completion for query in search engine

Authors: Vinay Singh; Dheeraj Kumar Purohit; Vimal Kumar; Pratima Verma; Ankita Malviya

Addresses: ABV-Indian Institute of Information Technology and Management, Gwalior, M.P.-474010, India ' ABV-Indian Institute of Information Technology and Management, Gwalior, M.P.-474010, India ' Department of Industrial and Management Engineering, Indian Institute of Technology, Kanpur; U.P. 208016, India ' Department of Industrial and Management Engineering, Indian Institute of Technology, Kanpur; U.P. 208016, India ' Department of Industrial and Management Engineering, Indian Institute of Technology, Kanpur; U.P. 208016, India

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.

DOI: 10.1504/IJBIS.2018.092528

International Journal of Business Information Systems, 2018 Vol.28 No.3, pp.299 - 314

Received: 24 Jul 2016
Accepted: 14 Nov 2016
Published online: 24 Jun 2018 *

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