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Search shortcuts: a new approach to the recommendation of queries

Published: 23 October 2009 Publication History

Abstract

The recommendation of queries, known as query suggestion, is a common practice on major Web Search Engines. It aims to help users to find the information they are looking for, and is usually based on the knowledge learned from past interactions with the search engine. In this paper we propose a new model for query suggestion, the Search Shortcut Problem, that consists in recommending "successful" queries that allowed other users to satisfy, in the past, similar information needs. This new model has several advantages with respect to traditional query suggestion approaches. First, it allows a straightforward evaluation of algorithms from available query log data. Moreover, it simplifies the application of several recommendation techniques from other domains. Particularly, in this work we applied Collaborative Filtering to this problem, and evaluated the interesting results achieved on large query logs from AOL and Microsoft. Different techniques for analyzing and extracting information from query logs, as well as new metrics and techniques for measuring the effectiveness of recommendations are proposed and evaluated. The results obtained clearly show the importance of several of our contributions, and open an interesting field for future research.

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Cited By

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  • (2023)Graph Learning for Exploratory Query Suggestions in an Instant Search SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615481(4780-4786)Online publication date: 21-Oct-2023
  • (2023)Bootstrapping Query Suggestions in Spotify's Instant Search SystemProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591827(3230-3234)Online publication date: 19-Jul-2023
  • (2021)Evaluating Recommender SystemsInternational Journal of Intelligent Information Technologies10.4018/ijiit.202104010217:2(25-45)Online publication date: Apr-2021
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    cover image ACM Conferences
    RecSys '09: Proceedings of the third ACM conference on Recommender systems
    October 2009
    442 pages
    ISBN:9781605584355
    DOI:10.1145/1639714
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    Publication History

    Published: 23 October 2009

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    Author Tags

    1. collaborative filtering
    2. evaluation
    3. query suggestion model
    4. search shortcut

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    RecSys '09: Third ACM Conference on Recommender Systems
    October 23 - 25, 2009
    New York, New York, USA

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    View all
    • (2023)Graph Learning for Exploratory Query Suggestions in an Instant Search SystemProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615481(4780-4786)Online publication date: 21-Oct-2023
    • (2023)Bootstrapping Query Suggestions in Spotify's Instant Search SystemProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591827(3230-3234)Online publication date: 19-Jul-2023
    • (2021)Evaluating Recommender SystemsInternational Journal of Intelligent Information Technologies10.4018/ijiit.202104010217:2(25-45)Online publication date: Apr-2021
    • (2018)Cross-lingual analysis of English and Chinese web searchInternational Journal of Web and Grid Services10.5555/3292946.329294914:4(376-399)Online publication date: 1-Jan-2018
    • (2018)Social SearchSocial Information Access10.1007/978-3-319-90092-6_7(213-276)Online publication date: 3-May-2018
    • (2018)Weblog AnalysisEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4939-7131-2_129(3387-3395)Online publication date: 12-Jun-2018
    • (2017)Development and empirical user-centered evaluation of semantically-based query recommendation for an electronic health record search engineJournal of Biomedical Informatics10.1016/j.jbi.2017.01.01367:C(1-10)Online publication date: 1-Mar-2017
    • (2017)Weblog AnalysisEncyclopedia of Social Network Analysis and Mining10.1007/978-1-4614-7163-9_129-1(1-9)Online publication date: 7-Aug-2017
    • (2016)Generating query suggestions by exploiting latent semantics in query logsJournal of Information Science10.1177/016555151559472342:4(437-448)Online publication date: 1-Aug-2016
    • (2016)How Writers SearchProceedings of the 2016 ACM on Conference on Human Information Interaction and Retrieval10.1145/2854946.2854969(193-202)Online publication date: 13-Mar-2016
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