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A Dynamic Influence Keyword Model for Identifying Implicit User Interests on Social Networks

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ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

Pages 1160 - 1163

https://doi.org/10.1145/3110025.3120987

Published: 31 July 2017 Publication History

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Abstract

The rapid growth of social networks have enabled users to instantly share what is happening around them. With the character-limitation and other feature constraints imposed by microblogs, users are obliged to express their intentions in implicit forms. This behavior poses many challenges for contextual approaches that aim to identify user intentions. Furthermore, users have the tendency to display different degree of preferences towards specific interests, simultaneously in time, making it difficult for models to rank the discovered interests. We propose a dynamic interest keyword model, a graph-based ranking mechanism, that identifies the different degrees of interests of a user. Our results show that the proposed system detects human-inferred interests, 94% of the time, showing that the model is feasible and contributes various insights that can be used to improve user intention identification systems.

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Cited By

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Likmeta AMetelli ARamponi GTirinzoni AGiuliani MRestelli M(2021)Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problemsMachine Learning10.1007/s10994-020-05939-8Online publication date: 14-Mar-2021
https://doi.org/10.1007/s10994-020-05939-8

A Dynamic Influence Keyword Model for Identifying Implicit User Interests on Social Networks
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

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ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017

July 2017

698 pages

ISBN:9781450349932

DOI:10.1145/3110025

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Association for Computing Machinery

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Published: 31 July 2017

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SIGKDD

ASONAM '17: Advances in Social Networks Analysis and Mining 2017

July 31 - August 3, 2017

Sydney, Australia

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Likmeta AMetelli ARamponi GTirinzoni AGiuliani MRestelli M(2021)Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problemsMachine Learning10.1007/s10994-020-05939-8Online publication date: 14-Mar-2021
https://doi.org/10.1007/s10994-020-05939-8

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Discerning individual interests and shared interests for social user profiling

Inferring user interests in microblogging social networks: a survey

Mining the interests of Chinese microbloggers via keyword extraction

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Discerning individual interests and shared interests for social user profiling

Inferring user interests in microblogging social networks: a survey

Mining the interests of Chinese microbloggers via keyword extraction

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