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Extracting, Mining and Predicting Users' Interests from Social Networks

Published: 18 July 2019 Publication History

Abstract

The abundance of user generated content on social networks provides the opportunity to build models that are able to accurately and effectively extract, mine and predict users' interests with the hopes of enabling more effective user engagement, better quality delivery of appropriate services and higher user satisfaction. While traditional methods for building user profiles relied on AI-based preference elicitation techniques that could have been considered to be intrusive and undesirable by the users, more recent advances are focused on a non-intrusive yet accurate way of determining users' interests and preferences. In this tutorial, we cover five important aspects related to the effective mining of user interests: (1) we introduce the information sources that are used for extracting user interests, (2) various types of user interest profiles that have been proposed in the literature, (3) techniques that have been adopted or proposed for mining user interests, (4) the scalability and resource requirements of the state of the art methods, and finally (5) the evaluation methodologies that are adopted in the literature for validating the appropriateness of the mined user interest profiles. We also introduce existing challenges, open research question and exciting opportunities for further work.

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Cited By

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  • (2023)Multi-granularity user interest modeling and interest drift detectionIntelligent Data Analysis10.3233/IDA-21651727:2(555-577)Online publication date: 15-Mar-2023
  • (2023)Modelo para la recuperación de información con expansión de consulta y perfil de preferencia de los usuariosRevista Facultad de Ingeniería10.19053/01211129.v32.n64.2023.1520832:64(e15208)Online publication date: 31-May-2023
  • (2022)The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social MediaApplied Sciences10.3390/app1204215712:4(2157)Online publication date: 18-Feb-2022
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cover image ACM Conferences
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval
July 2019
1512 pages
ISBN:9781450361729
DOI:10.1145/3331184
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 18 July 2019

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Author Tags

  1. social media
  2. user interest modeling
  3. user profile

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SIGIR'19 Paper Acceptance Rate 84 of 426 submissions, 20%;
Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

View all
  • (2023)Multi-granularity user interest modeling and interest drift detectionIntelligent Data Analysis10.3233/IDA-21651727:2(555-577)Online publication date: 15-Mar-2023
  • (2023)Modelo para la recuperación de información con expansión de consulta y perfil de preferencia de los usuariosRevista Facultad de Ingeniería10.19053/01211129.v32.n64.2023.1520832:64(e15208)Online publication date: 31-May-2023
  • (2022)The Impact of Personality and Demographic Variables in Collaborative Filtering of User Interest on Social MediaApplied Sciences10.3390/app1204215712:4(2157)Online publication date: 18-Feb-2022
  • (2022)Social Media Profiling Continues to Partake in the Development of Formalistic Self-Concepts. Social Media Users Think So, Too.Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3514094.3534192(238-252)Online publication date: 26-Jul-2022
  • (2021)A Review of Extracting and Mining User Interest from Social Media Based on Personality2021 3rd International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)10.1109/REEPE51337.2021.9388014(1-6)Online publication date: 11-Mar-2021
  • (2020)Modeling User Behavior for Vertical Search: Images, Apps and ProductsProceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3397271.3401423(2440-2443)Online publication date: 25-Jul-2020

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