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Recommendations in social media for brand monitoring

Published: 23 October 2011 Publication History

Abstract

We present a recommendation system for social media that draws upon monitoring and prediction methods. We use historical posts on some focal topic or historical links to a focal blog channel to recommend a set of authors to follow. Such a system would be useful for brand managers interested in monitoring conversations about their products. Our recommendations are based on a prediction system that trains a ranking Support Vector Machine (RSVM) using multiple features including the content of a post, similarity between posts, links between posts and/or blog channels, and links to external websites. We solve two problems, Future Author Prediction (FAP) and Future Link Prediction (FLP), and apply the prediction outcome to make recommendations. Using an extensive experimental evaluation on a blog dataset, we demonstrate the quality and value of our recommendations.

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Cited By

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  • (2024)A Descriptive Analysis of Social Media Use by a Selected Sample of Generation Z TravellersTourism and Hospitality for Sustainable Development10.1007/978-3-031-63073-6_13(213-230)Online publication date: 28-Aug-2024
  • (2023)Monitoring Events of Market Competitors: A Text Mining Method for Analyzing Massive Firm-Generated Social MediaJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1802004718:2(908-927)Online publication date: 27-Apr-2023
  • (2019)What Makes the Indian Youths to Engage with Online Retail Brands: An Empirical StudyGlobal Business Review10.1177/097215091882210622:6(1507-1529)Online publication date: 4-Apr-2019
  • Show More Cited By

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    cover image ACM Conferences
    RecSys '11: Proceedings of the fifth ACM conference on Recommender systems
    October 2011
    414 pages
    ISBN:9781450306836
    DOI:10.1145/2043932
    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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    New York, NY, United States

    Publication History

    Published: 23 October 2011

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    Author Tags

    1. blog
    2. brand monitoring
    3. recommendation
    4. social media

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    RecSys '11
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    RecSys '11: Fifth ACM Conference on Recommender Systems
    October 23 - 27, 2011
    Illinois, Chicago, USA

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    Overall Acceptance Rate 254 of 1,295 submissions, 20%

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    Cited By

    View all
    • (2024)A Descriptive Analysis of Social Media Use by a Selected Sample of Generation Z TravellersTourism and Hospitality for Sustainable Development10.1007/978-3-031-63073-6_13(213-230)Online publication date: 28-Aug-2024
    • (2023)Monitoring Events of Market Competitors: A Text Mining Method for Analyzing Massive Firm-Generated Social MediaJournal of Theoretical and Applied Electronic Commerce Research10.3390/jtaer1802004718:2(908-927)Online publication date: 27-Apr-2023
    • (2019)What Makes the Indian Youths to Engage with Online Retail Brands: An Empirical StudyGlobal Business Review10.1177/097215091882210622:6(1507-1529)Online publication date: 4-Apr-2019
    • (2019)Harnessing the Power of the General Public for Crowdsourced Business Intelligence: A SurveyIEEE Access10.1109/ACCESS.2019.29010277(26606-26630)Online publication date: 2019
    • (2019)Exploring market competition over topics in spatio-temporal document collectionsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0522-928:1(123-145)Online publication date: 1-Feb-2019
    • (2015)Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image DataProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2783293(1425-1434)Online publication date: 10-Aug-2015
    • (2015)SUPERProceedings of the 24th International Conference on World Wide Web10.1145/2740908.2741723(1217-1220)Online publication date: 18-May-2015

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