Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

article · Future Internet · 2018

Query Recommendation Using Hybrid Query Relevance

Jialu Xu, Feiyue Ye

Cited by 1Open access

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.

Cite this paper

APA 7

Xu, J., & Ye, F. (2018). Query Recommendation Using Hybrid Query Relevance. Future Internet, 10(11), 112. https://doi.org/10.3390/fi10110112

MLA 9

Xu, Jialu, and Feiyue Ye. “Query Recommendation Using Hybrid Query Relevance.” Future Internet, vol. 10, no. 11, 2018, p. 112, https://doi.org/10.3390/fi10110112.

Chicago

Xu, Jialu, and Feiyue Ye. 2018. “Query Recommendation Using Hybrid Query Relevance.” Future Internet 10 (11): 112. https://doi.org/10.3390/fi10110112.

Harvard

Xu, J. and Ye, F. (2018) ‘Query Recommendation Using Hybrid Query Relevance’, Future Internet, 10(11), p. 112. Available at: https://doi.org/10.3390/fi10110112.

BibTeX
@article{xu2018query,
  title = {{Query Recommendation Using Hybrid Query Relevance}},
  author = {Xu, Jialu and Ye, Feiyue},
  journal = {Future Internet},
  volume = {10},
  number = {11},
  pages = {112},
  year = {2018},
  doi = {10.3390/fi10110112}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.

Related papers