Recommending High Utility Queries via Query-Reformulation Graph Article Swipe
Jianguo Wang
,
Joshua Zhexue Huang
,
Dingming Wu
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1155/2015/956468
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1155/2015/956468
Query recommendation is an essential part of modern search engine which aims at helping users find useful information. Existing query recommendation methods all focus on recommending similar queries to the users. However, the main problem of these similarity-based approaches is that even some very similar queries may return few or even no useful search results, while other less similar queries may return more useful search results, especially when the initial query does not reflect user’s search intent correctly. Therefore, we propose recommending high utility queries, that is, useful queries with more relevant documents, rather than similar ones. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. At last, we propagate the document utilities back to queries and rank candidate queries with their utilities for recommendation. Extensive experiments were conducted on real query logs, and the experimental results have shown that our method significantly outperformed the state-of-the-art methods in recommending high utility queries.
Related Topics
Concepts
Computer science
Information retrieval
Web query classification
Web search query
Query optimization
Query expansion
Sargable
Graph
Query language
Search engine
Spatial query
Online aggregation
Data mining
Theoretical computer science
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2015/956468
- https://downloads.hindawi.com/journals/mpe/2015/956468.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1544763578
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W1544763578Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2015/956468Digital Object Identifier
- Title
-
Recommending High Utility Queries via Query-Reformulation GraphWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-01-01Full publication date if available
- Authors
-
Jianguo Wang, Joshua Zhexue Huang, Dingming WuList of authors in order
- Landing page
-
https://doi.org/10.1155/2015/956468Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/mpe/2015/956468.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://downloads.hindawi.com/journals/mpe/2015/956468.pdfDirect OA link when available
- Concepts
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Computer science, Information retrieval, Web query classification, Web search query, Query optimization, Query expansion, Sargable, Graph, Query language, Search engine, Spatial query, Online aggregation, Data mining, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2017: 1, 2016: 2Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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