Next Article in Journal
Chinese Text Classification Model Based on Deep Learning
Previous Article in Journal
Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Future Internet 2018, 10(11), 112;

Query Recommendation Using Hybrid Query Relevance

The School of computer engineering and Science, Shanghai University, Shanghai 200444, China
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
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)
Full-Text   |   PDF [591 KB, uploaded 22 November 2018]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Xu, J.; Ye, F. Query Recommendation Using Hybrid Query Relevance. Future Internet 2018, 10, 112.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Future Internet EISSN 1999-5903 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top