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Extracting interesting association rules from toolbar data

Published: 29 October 2012 Publication History

Abstract

Toolbar navigation logs provide rich data for enhancing information discovery on the Web. The value of this data resides in its scope, which goes beyond that of traditional query-mining data sources, such as search-engine logs. In this paper we present a methodology for extracting relevant association rules for queries, based on historic user navigational data. In addition, we propose a graph-based approach for extracting related queries and URLs for a given query.

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Cited By

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  • (2023)PredictionMiner: mining the latest individual behavioral rules for personalized contextual pattern predictionsSoft Computing10.1007/s00500-023-08572-4Online publication date: 17-Jul-2023
  • (2021)Contextual Mobile Datasets, Pre-processing and Feature SelectionContext-Aware Machine Learning and Mobile Data Analytics10.1007/978-3-030-88530-4_4(59-73)Online publication date: 20-Sep-2021
  • (2020)Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research DirectionsMobile Networks and Applications10.1007/s11036-020-01650-zOnline publication date: 14-Sep-2020
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  1. Extracting interesting association rules from toolbar data

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    cover image ACM Conferences
    CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management
    October 2012
    2840 pages
    ISBN:9781450311564
    DOI:10.1145/2396761
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 29 October 2012

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    Author Tags

    1. association rules
    2. toolbar data
    3. web data mining

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    Cited By

    View all
    • (2023)PredictionMiner: mining the latest individual behavioral rules for personalized contextual pattern predictionsSoft Computing10.1007/s00500-023-08572-4Online publication date: 17-Jul-2023
    • (2021)Contextual Mobile Datasets, Pre-processing and Feature SelectionContext-Aware Machine Learning and Mobile Data Analytics10.1007/978-3-030-88530-4_4(59-73)Online publication date: 20-Sep-2021
    • (2020)Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research DirectionsMobile Networks and Applications10.1007/s11036-020-01650-zOnline publication date: 14-Sep-2020
    • (2019)Context-aware rule learning from smartphone data: survey, challenges and future directionsJournal of Big Data10.1186/s40537-019-0258-46:1Online publication date: 31-Oct-2019
    • (2019)RecencyMiner: mining recency-based personalized behavior from contextual smartphone dataJournal of Big Data10.1186/s40537-019-0211-66:1Online publication date: 6-Jun-2019
    • (2016)Behavior-Oriented Time Segmentation for Mining Individualized Rules of Mobile Phone Users2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2016.60(488-497)Online publication date: Oct-2016

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