Developing a Meta-suggestion Engine for Search Query

by   Seungmin Kim, et al.

With the development of the Internet and the accumulation of information on the web, users use a search engine to easily obtain the desired information. A query suggestion is one of the main services provided by a search engine, and is very important for improving search performance, creating efficient queries, and reducing search time. However, there are search engines that do not support the query suggestion service. Under such engines, if users want to perform a search, they would have much difficulties in effectively performing the search. In this paper, to tackle the problem, we propose and develop a metasuggestion engine that crawls suggested search queries from search engines with a suggestion service, applies a re-ranking algorithm, and provides the suggested search queries in the form of an extension program on a web browser. Meta-suggestion engine are useful for users searching in engines that do not provide query suggestions, as they provide query suggestions wherever the user searches. We evaluate engines with relevance-based and predictive hit-based evaluation methods, showing that MSE produces good quality suggestions. We study improvements in target engine selection and re-ranking algorithms in future studies.


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