Location-Sensitive Personalized Query Auto-Completion

Jiang, Danyang; Cai, Fei; Chen, Honghui

doi:10.1109/ihmsc.2018.00012

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…A study that was done on the Excite search engine shows that between every five queries there is one query which have a geographical context [1]. Web users searching for information that are spatially located often require information, that are geographically specific, such as geographic terms in Web pages and user queries or even user location [2]. However, retrieval systems currently have limited support to operationalize a user's geospatial queries.…”

Section: Introductionmentioning

confidence: 99%

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Midaoui

Ghali

Qadi

et al. 2021

IJECE

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Geographical queries need a special process of reformulation by information retrieval systems (IRS) due to their specificities and hierarchical structure. This fact is ignored by most of web search engines. In this paper, we propose an automatic approach for building a spatial taxonomy, that models’ the notion of adjacency that will be used in the reformulation of the spatial part of a geographical query. This approach exploits the documents that are in top of the retrieved list when submitting a spatial entity, which is composed of a spatial relation and a noun of a city. Then, a transactional database is constructed, considering each document extracted as a transaction that contains the nouns of the cities sharing the country of the submitted query’s city. The algorithm frequent pattern growth (FP-growth) is applied to this database in his parallel version (parallel FP-growth: PFP) in order to generate association rules, that will form the country’s taxonomy in a Big Data context. Experiments has been conducted on Spark and their results show that query reformulation using the taxonomy constructed based on our proposed approach improves the precision and the effectiveness of the IRS.

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Section: Introductionmentioning

confidence: 99%

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Midaoui

Ghali

Qadi

et al. 2021

IJECE

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Sequence Graph-Based Query Auto-Suggestion (SGQAS)

George¹

2023

Advances in Business Information Systems and Analytics

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Query autosuggestion or auto-completion is a query prediction service that returns suggested queries for text-based queries when users type in the search box. It is a search-assistant feature of almost all search engines that helps users complete the queries without typing the entire search query. The process of query auto-suggestion typically involves analyzing the user's partial query and generating a list of suggestions based on factors such as popular search terms, the user's search history, and the search context. The suggestions are then displayed to the user in real-time, often in a drop-down menu or other interface types, allowing them to select and refine their search query easily. This chapter proposes a content-based query auto-suggestion using a graph-based word sequence representation of documents using a knowledge graph. It uses the whole sequence of all entered query terms to retrieve the names of all nodes connected to the end node of the entered path sequence of query terms to provide user suggestion queries.

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Location-Sensitive Personalized Query Auto-Completion

Cited by 2 publications

References 8 publications

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Geographical queries reformulation using a parallel association rules generator to build spatial taxonomies

Sequence Graph-Based Query Auto-Suggestion (SGQAS)

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