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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\u2019s 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.<\/jats:p>","DOI":"10.1177\/0165551520968690","type":"journal-article","created":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:35:47Z","timestamp":1604882147000},"page":"524-543","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["TIPS:\n                    <i>Ti<\/i>\n                    me-aware\n                    <i>P<\/i>\n                    ersonalised\n                    <i>S<\/i>\n                    emantic-based query auto-completion"],"prefix":"10.1177","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9373-9629","authenticated-orcid":false,"given":"Saedeh","family":"Tahery","sequence":"first","affiliation":[{"name":"Faculty of Computer Engineering, K. N. 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