short-paper

Personalized Dynamic Recommender System for Investors

Authors:

Takehiro Takayanagi,

Chung-Chi Chen,

Kiyoshi IzumiAuthors Info & Claims

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2246 - 2250

https://doi.org/10.1145/3539618.3592035

Published: 18 July 2023 Publication History

Abstract

With the development of online platforms, people can share and obtain opinions quickly. It also makes individuals' preferences change dynamically and rapidly because they may change their minds when getting convincing opinions from other users. Unlike representative areas of recommendation research such as e-commerce platforms where items' features are fixed, in investment scenarios financial instruments' features such as stock price, also change dynamically over time. To capture these dynamic features and provide a better-personalized recommendation for amateur investors, this study proposes a Personalized Dynamic Recommender System for Investors, PDRSI. The proposed PDRSI considers two investor's personal features: dynamic preferences and historical interests, and two temporal environmental properties: recent discussions on the social media platform and the latest market information. The experimental results support the usefulness of the proposed PDRSI, and the ablation studies show the effect of each module. For reproduction, we follow Twitter's developer policy to share our dataset for future work.

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Cited By

Lee YKim YSanz-Cruzado JMccreadie RLee Y(2024)Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient SamplingProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698662(795-803)Online publication date: 14-Nov-2024
https://dl.acm.org/doi/10.1145/3677052.3698662
Ueda KSuwa HUmehara EOgawa YYamashita TTsubouchi KYasumoto K(2024)ForumPFN: Online Forum Post Fusion Network for Volatility Index Movement Prediction2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825644(6680-6689)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825644
Takayanagi TIzumi K(2024)Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial ApplicationsNew Generation Computing10.1007/s00354-024-00241-w42:4(635-649)Online publication date: 25-Feb-2024
https://dl.acm.org/doi/10.1007/s00354-024-00241-w
Show More Cited By

Index Terms

Personalized Dynamic Recommender System for Investors
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications

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Published In

cover image ACM Conferences

SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2023

3567 pages

ISBN:9781450394086

DOI:10.1145/3539618

General Chairs:
Hsin-Hsi Chen
National Taiwan University
,
Wei-Jou (Edward) Duh
National Taiwan University
,
Hen-Hsen Huang
Academia Sinica
,
Program Chairs:
Makoto P. Kato
Spotify
,
Josiane Mothe
Universite de Toulouse
,
Barbara Poblete
University of Chile and Amazon Visiting Academic

Copyright © 2023 ACM.

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 the author(s) 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 July 2023

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Short-paper

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JSPS KAKENHI
JSPS Core-to-Core Program
the New Energy and Industrial Technology Development Organization (NEDO)

Conference

SIGIR '23

Sponsor:

SIGIR

SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 23 - 27, 2023

Taipei, Taiwan

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Lee YKim YSanz-Cruzado JMccreadie RLee Y(2024)Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient SamplingProceedings of the 5th ACM International Conference on AI in Finance10.1145/3677052.3698662(795-803)Online publication date: 14-Nov-2024
https://dl.acm.org/doi/10.1145/3677052.3698662
Ueda KSuwa HUmehara EOgawa YYamashita TTsubouchi KYasumoto K(2024)ForumPFN: Online Forum Post Fusion Network for Volatility Index Movement Prediction2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825644(6680-6689)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825644
Takayanagi TIzumi K(2024)Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial ApplicationsNew Generation Computing10.1007/s00354-024-00241-w42:4(635-649)Online publication date: 25-Feb-2024
https://dl.acm.org/doi/10.1007/s00354-024-00241-w
Takayanagi TIzumi K(2023)Harnessing Behavioral Traits to Enhance Financial Stock Recommender Systems: Tackling the User Cold Start Problem2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386644(5694-5703)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386644

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