Predictive auto-completion for query in search engine
Vinay Singh, Dheeraj Kumar Purohit, Vimal Kumar, Pratima Verma, Ankita Malviya · International Journal of Business Information Systems · 2018
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.