short-paper

Personalized Stock Recommendation with Investors' Attention and Contextual Information

Authors:

Takehiro Takayanagi,

Naoyuki Tsunedomi,

Yukina AbeAuthors Info & Claims

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

Pages 3339 - 3343

https://doi.org/10.1145/3539618.3591850

Published: 18 July 2023 Publication History

Abstract

The personalized stock recommendation is a task to recommend suitable stocks for each investor. The personalized recommendations are valuable, especially in investment decision making as the objective of building a portfolio varies by each retail investor. In this paper, we propose a Personalized Stock Recommendation with Investors' Attention and Contextual Information (PSRIC). PSRIC aims to incorporate investors' financial decision-making process into a stock recommendation, and it consists of an investor modeling module and a context module. The investor modeling module models the investor's attention toward various stock information. The context module incorporates stock dynamics and investor profiles. The result shows that the proposed model outperforms the baseline models and verifies the usefulness of both modules in ablation studies.

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Cited By

Singhal SPal K(2024)State of art and emerging trends on group recommender system: a comprehensive reviewInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00329-513:2Online publication date: 2-May-2024
https://doi.org/10.1007/s13735-024-00329-5
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

Index Terms

Personalized Stock Recommendation with Investors' Attention and Contextual Information
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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Publication History

Published: 18 July 2023

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JSPS Core-to-Core Program

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SIGIR '23

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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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Cited By

Singhal SPal K(2024)State of art and emerging trends on group recommender system: a comprehensive reviewInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00329-513:2Online publication date: 2-May-2024
https://doi.org/10.1007/s13735-024-00329-5
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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