Query Suggestion for Struggling Search by Struggling Flow Graph

Zebang, Chen; Yamamoto, Takehiro; Tanaka, Katsumi

doi:10.1109/wi.2016.0040

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Much research has explored the area of query recommendation based on query logs [4][5][6]. Chen et al [4] proposed a query suggestion method by constructing struggling flow graph to identify the struggle phrases and mine effective representation based on query log. Zahera et al [6] proposed a method based on clustering processes in which groups of semantically similar queries are detected.…”

Section: Related Workmentioning

confidence: 99%

“…(3) RW UQ [15]: This is a method calculating the bidirectional transition probability-based query-URL graph and making a strength metric of the query-URL edges. (4) CQM [6]:This is a method based on clustering processes in which groups of semantically similar queries are detected. (5) QRSR: Our method considers query information and URL information.…”

Section: Evaluation Of Efficiencymentioning

confidence: 99%

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Query Recommendation Using Hybrid Query Relevance

2018

Future Internet

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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.

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