Towards efficient top-k fuzzy auto-completion queries

AbdelNaby, Magdy; Khalefa, Mohamed E.; Taha, Yousry; Hassan, Ahmed

doi:10.1016/j.aej.2020.06.012

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…Gog et al [31] introduced a solution to QAC leveraging the combination of an inverted index with succinct data structures and discussed its efficiency and effectiveness. AbdelNaby et al [32] presented a new depth-oriented instant type-ahead fuzzy search method that is much faster than others.…”

Section: Related Workmentioning

confidence: 99%

TIPS: Time-aware Personalised Semantic-based query auto-completion

Tahery

Farzi

2020

Journal of Information Science

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With the rapid growth of the Internet, search engines play vital roles in meeting the users’ information needs. However, formulating information needs to simple queries for canonical users is a problem yet. Therefore, query auto-completion, which is one of the most important characteristics of the search engines, is leveraged to provide a ranked list of queries matching the user’s entered prefix. Although query auto-completion utilises useful information provided by search engine logs, time-, semantic- and context-aware features are still important resources of extra knowledge. Specifically, in this study, a hybrid query auto-completion system called TIPS ( Time-aware Personalised Semantic-based query auto-completion) is introduced to combine the well-known systems performing based on popularity and neural language model. Furthermore, this system is supplemented by time-aware features that blend both context and semantic information in a collaborative manner. Experimental studies on the standard AOL dataset are conducted to compare our proposed system with state-of-the-art methods, that is, FactorCell, ConcatCell and Unadapted. The results illustrate the significant superiorities of TIPS in terms of mean reciprocal rank (MRR), especially for short-length prefixes.

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Section: Related Workmentioning

confidence: 99%