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Relevant bibliographies by topics / Search query auto-Completion

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Academic literature on the topic 'Search query auto-Completion'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Search query auto-Completion"

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Vuong, Tung, Salvatore Andolina, Giulio Jacucci, and Tuukka Ruotsalo. "Spoken Conversational Context Improves Query Auto-completion in Web Search." ACM Transactions on Information Systems 39, no. 3 (May 6, 2021): 1–32. http://dx.doi.org/10.1145/3447875.

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Web searches often originate from conversations in which people engage before they perform a search. Therefore, conversations can be a valuable source of context with which to support the search process. We investigate whether spoken input from conversations can be used as a context to improve query auto-completion. We model the temporal dynamics of the spoken conversational context preceding queries and use these models to re-rank the query auto-completion suggestions. Data were collected from a controlled experiment and comprised conversations among 12 participant pairs conversing about movies or traveling. Search query logs during the conversations were recorded and temporally associated with the conversations. We compared the effects of spoken conversational input in four conditions: a control condition without contextualization; an experimental condition with the model using search query logs; an experimental condition with the model using spoken conversational input; and an experimental condition with the model using both search query logs and spoken conversational input. We show the advantage of combining the spoken conversational context with the Web-search context for improved retrieval performance. Our results suggest that spoken conversations provide a rich context for supporting information searches beyond current user-modeling approaches.

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Singh, Vinay, Dheeraj Kumar Purohit, Vimal Kumar, Pratima Verma, and Ankita Malviya. "Predictive auto-completion for query in search engine." International Journal of Business Information Systems 28, no. 3 (2018): 299. http://dx.doi.org/10.1504/ijbis.2018.092528.

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Malviya, Ankita, Pratima Verma, Vinay Singh, Dheeraj Kumar Purohit, and Vimal Kumar. "Predictive auto-completion for query in search engine." International Journal of Business Information Systems 28, no. 3 (2018): 299. http://dx.doi.org/10.1504/ijbis.2018.10013682.

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Li, Ying, Jizhou Huang, Miao Fan, Jinyi Lei, Haifeng Wang, and Enhong Chen. "Personalized Query Auto-Completion for Large-Scale POI Search at Baidu Maps." ACM Transactions on Asian and Low-Resource Language Information Processing 19, no. 5 (August 25, 2020): 1–16. http://dx.doi.org/10.1145/3394137.

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Rossiello, Gaetano, Annalina Caputo, Pierpaolo Basile, and Giovanni Semeraro. "Modeling concepts and their relationships for corpus-based query auto-completion." Open Computer Science 9, no. 1 (October 11, 2019): 212–25. http://dx.doi.org/10.1515/comp-2019-0015.

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AbstractQuery auto-completion helps users to formulate their information needs by providing suggestion lists at every typed key. This task is commonly addressed by exploiting query logs and the approaches proposed in the literature fit well in web scale scenarios, where usually huge amounts of past user queries can be analyzed to provide reliable suggestions. However, when query logs are not available, e.g. in enterprise or desktop search engines, these methods are not applicable at all. To face these challenging scenarios, we present a novel corpus-based approach which exploits the textual content of an indexed document collection in order to dynamically generate query completions. Our method extracts informative text fragments from the corpus and it combines them using a probabilistic graphical model in order to capture the relationships between the extracted concepts. Using this approach, it is possible to automatically complete partial queries with significant suggestions related to the keywords already entered by the user without requiring the analysis of the past queries. We evaluate our system through a user study on two different real-world document collections. The experiments show that our method is able to provide meaningful completions outperforming the state-of-the art approach.

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Shahidi, Niloofar, Xuanzhi Lin, Yuda Munarko, Laila Rasmy, and Tram Ngo. "AQUA: an Advanced QUery Architecture for the SPARC Portal." F1000Research 10 (September 16, 2021): 930. http://dx.doi.org/10.12688/f1000research.73018.1.

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The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program integrates biological and neural information to create anatomical and functional maps of the peripheral nervous system. The SPARC Portal hosts a dynamic storage for the datasets, models, and resources to help the researchers find and produce data. Currently, the SPARC Portal provides a primary search tool, which lacks some features to improve the search experience. To purposefully retrieve the required information from the stored datasets and resources, we have developed an Advanced QUery Architecture (AQUA) for the SPARC Portal. Near-real-time auto-completion of the queries, close-matches suggestions, and multiple filters to narrow or sort the results are the major features of AQUA with the goal to enhance the usability of the SPARC search engine. AQUA is available from: https://github.com/SPARC-FAIR-Codeathon/aqua

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Smith, Catherine L., Jacek Gwizdka, and Henry Feild. "The use of query auto-completion over the course of search sessions with multifaceted information needs." Information Processing & Management 53, no. 5 (September 2017): 1139–55. http://dx.doi.org/10.1016/j.ipm.2017.05.001.

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Mitra, Bhaskar. "Neural methods for effective, efficient, and exposure-aware information retrieval." ACM SIGIR Forum 55, no. 1 (June 2021): 1–2. http://dx.doi.org/10.1145/3476415.3476434.

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Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

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Tahery, Saedeh, and Saeed Farzi. "TIPS: Time-aware Personalised Semantic-based query auto-completion." Journal of Information Science, November 8, 2020, 016555152096869. http://dx.doi.org/10.1177/0165551520968690.

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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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Dissertations / Theses on the topic "Search query auto-Completion"

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Lully, Vincent. "Vers un meilleur accès aux informations pertinentes à l’aide du Web sémantique : application au domaine du e-tourisme." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUL196.

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Cette thèse part du constat qu’il y a une infobésité croissante sur le Web. Les deux types d’outils principaux, à savoir le système de recherche et celui de recommandation, qui sont conçus pour nous aider à explorer les données du Web, connaissent plusieurs problématiques dans : (1) l’assistance de la manifestation des besoins d’informations explicites, (2) la sélection des documents pertinents, et (3) la mise en valeur des documents sélectionnés. Nous proposons des approches mobilisant les technologies du Web sémantique afin de pallier à ces problématiques et d’améliorer l’accès aux informations pertinentes. Nous avons notamment proposé : (1) une approche sémantique d’auto-complétion qui aide les utilisateurs à formuler des requêtes de recherche plus longues et plus riches, (2) des approches de recommandation utilisant des liens hiérarchiques et transversaux des graphes de connaissances pour améliorer la pertinence, (3) un framework d’affinité sémantique pour intégrer des données sémantiques et sociales pour parvenir à des recommandations qualitativement équilibrées en termes de pertinence, diversité et nouveauté, (4) des approches sémantiques visant à améliorer la pertinence, l’intelligibilité et la convivialité des explications des recommandations, (5) deux approches de profilage sémantique utilisateur à partir des images, et (6) une approche de sélection des meilleures images pour accompagner les documents recommandés dans les bannières de recommandation. Nous avons implémenté et appliqué nos approches dans le domaine du e-tourisme. Elles ont été dûment évaluées quantitativement avec des jeux de données vérité terrain et qualitativement à travers des études utilisateurs
This thesis starts with the observation that there is an increasing infobesity on the Web. The two main types of tools, namely the search engine and the recommender system, which are designed to help us explore the Web data, have several problems: (1) in helping users express their explicit information needs, (2) in selecting relevant documents, and (3) in valuing the selected documents. We propose several approaches using Semantic Web technologies to remedy these problems and to improve the access to relevant information. We propose particularly: (1) a semantic auto-completion approach which helps users formulate longer and richer search queries, (2) several recommendation approaches using the hierarchical and transversal links in knowledge graphs to improve the relevance of the recommendations, (3) a semantic affinity framework to integrate semantic and social data to yield qualitatively balanced recommendations in terms of relevance, diversity and novelty, (4) several recommendation explanation approaches aiming at improving the relevance, the intelligibility and the user-friendliness, (5) two image user profiling approaches and (6) an approach which selects the best images to accompany the recommended documents in recommendation banners. We implemented and applied our approaches in the e-tourism domain. They have been properly evaluated quantitatively with ground-truth datasets and qualitatively through user studies

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Book chapters on the topic "Search query auto-Completion"

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Li, Liangda, Hongbo Deng, and Yi Chang. "Query Auto-Completion." In Query Understanding for Search Engines, 145–70. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58334-7_7.

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Conference papers on the topic "Search query auto-Completion"

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Jiang, Jyun-Yu, and Pu-Jen Cheng. "Classifying User Search Intents for Query Auto-Completion." In ICTIR '16: ACM SIGIR International Conference on the Theory of Information Retrieval. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2970398.2970400.

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Ramachandran, Lakshmi, and Uma Murthy. "Ghosting: Contextualized Query Auto-Completion on Amazon Search." In SIGIR '19: The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3331184.3331432.

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Vargas, Saúl, Roi Blanco, and Peter Mika. "Term-by-Term Query Auto-Completion for Mobile Search." In WSDM 2016: Ninth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2835776.2835813.

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Hawking, David, and Kathy Griffiths. "An enterprise search paradigm based on extended query auto-completion." In the 18th Australasian Document Computing Symposium. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2537734.2537743.

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Li, Liangda, Hongbo Deng, Jianhui Chen, and Yi Chang. "Learning Parametric Models for Context-Aware Query Auto-Completion via Hawkes Processes." In WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3018661.3018698.

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Li, Liangda, Hongbo Deng, Anlei Dong, Yi Chang, Ricardo Baeza-Yates, and Hongyuan Zha. "Exploring Query Auto-Completion and Click Logs for Contextual-Aware Web Search and Query Suggestion." In WWW '17: 26th International World Wide Web Conference. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2017. http://dx.doi.org/10.1145/3038912.3052593.

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