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Next cashtag prediction on social trading platforms with auxiliary tasks

Published: 15 January 2020 Publication History

Abstract

Social trading platforms provide a forum for investors to share their analysis and opinions. Posts on these platforms are characterized by narrative styles which are much different from posts on general social platforms, for instance tweets. As a result, recommendation systems for social trading platforms should leverage tailor-made latent features. This paper presents a representation for these latent features in both textual data and market information. A real-world dataset is adopted to conduct experiments involving a novel task called next cashtag prediction. We propose a joint learning model with an attentive capsule network. Experimental results show positive results with the proposed methods and the corresponding auxiliary tasks.

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Cited By

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  • (2023)Personalized Dynamic Recommender System for InvestorsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592035(2246-2250)Online publication date: 19-Jul-2023
  • (2023)Detecting Buying and Selling Territories in the Foreign Currency Exchange Market2023 16th International Conference on Signal Processing and Communication System (ICSPCS)10.1109/ICSPCS58109.2023.10261149(01-06)Online publication date: 6-Sep-2023
  • (2021)FinTech ApplicationsFrom Opinion Mining to Financial Argument Mining10.1007/978-981-16-2881-8_6(73-87)Online publication date: 21-May-2021
  • Show More Cited By

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cover image ACM Conferences
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
August 2019
1228 pages
ISBN:9781450368681
DOI:10.1145/3341161
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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New York, NY, United States

Publication History

Published: 15 January 2020

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Author Tags

  1. interest prediction
  2. joint learning
  3. social trading

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;
Overall Acceptance Rate 116 of 549 submissions, 21%

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View all
  • (2023)Personalized Dynamic Recommender System for InvestorsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3592035(2246-2250)Online publication date: 19-Jul-2023
  • (2023)Detecting Buying and Selling Territories in the Foreign Currency Exchange Market2023 16th International Conference on Signal Processing and Communication System (ICSPCS)10.1109/ICSPCS58109.2023.10261149(01-06)Online publication date: 6-Sep-2023
  • (2021)FinTech ApplicationsFrom Opinion Mining to Financial Argument Mining10.1007/978-981-16-2881-8_6(73-87)Online publication date: 21-May-2021
  • (2019)Final Report of the NTCIR-14 FinNum Task: Challenges and Current Status of Fine-Grained Numeral Understanding in Financial Social Media DataNII Testbeds and Community for Information Access Research10.1007/978-3-030-36805-0_14(183-192)Online publication date: 28-Nov-2019

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