Query Recommendation Using Hybrid Query Relevance

Jialu Xu and Feiyue Ye
Additional contact information
Jialu Xu: The School of computer engineering and Science, Shanghai University, Shanghai 200444, China
Feiyue Ye: The School of computer engineering and Science, Shanghai University, Shanghai 200444, China

Future Internet, 2018, vol. 10, issue 11, 1-13

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.

Keywords: query recommendation; query relevance; query embedding (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/10/11/112/pdf (application/pdf)
https://www.mdpi.com/1999-5903/10/11/112/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:10:y:2018:i:11:p:112-:d:183978

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().