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Future Internet 2018, 10(11), 112; https://doi.org/10.3390/fi10110112

Query Recommendation Using Hybrid Query Relevance

†,* and †,*
The School of computer engineering and Science, Shanghai University, Shanghai 200444, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Received: 22 October 2018 / Revised: 16 November 2018 / Accepted: 17 November 2018 / Published: 19 November 2018
(This article belongs to the Section Big Data and Augmented Intelligence)
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Abstract

With the explosion of web information, search engines have become main tools in information retrieval. However, most queries submitted in web search are ambiguous and multifaceted. Understanding the queries and mining query intention is critical for search engines. In this paper, we present a novel query recommendation algorithm by combining query information and URL information which can get wide and accurate query relevance. The calculation of query relevance is based on query information by query co-concurrence and query embedding vector. Adding the ranking to query-URL pairs can calculate the strength between query and URL more precisely. Empirical experiments are performed based on AOL log. The results demonstrate the effectiveness of our proposed query recommendation algorithm, which achieves superior performance compared to other algorithms. View Full-Text
Keywords: query recommendation; query relevance; query embedding query recommendation; query relevance; query embedding
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Xu, J.; Ye, F. Query Recommendation Using Hybrid Query Relevance. Future Internet 2018, 10, 112.

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