short-paper

Agent-based computational investing recommender system

Authors:

Mona Taghavi,

Kaveh Bakhtiyari,

Edgar ScavinoAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 455 - 458

https://doi.org/10.1145/2507157.2508072

Published: 12 October 2013 Publication History

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Abstract

The fast development of computing and communication has reformed the financial markets' dynamics. Nowadays many people are investing and trading stocks through online channels and having access to real-time market information efficiently. There are more opportunities to lose or make money with all the stocks information available throughout the World; however, one should spend a lot of effort and time to follow those stocks and the available instant information. This paper presents a preliminary regarding a multi-agent recommender system for computational investing. This system utilizes a hybrid filtering technique to adaptively recommend the most profitable stocks at the right time according to investor's personal favour. The hybrid technique includes collaborative and content-based filtering. The content-based model uses investor preferences, influencing macro-economic factors, stocks profiles and the predicted trend to tailor to its advices. The collaborative filter assesses the investor pairs' investing behaviours and actions that are proficient in economic market to recommend the similar ones to the target investor.

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Cited By

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Wang JHu R(2024)A Two-stage Recommendation Optimization Algorithm Based on Item Popularity and User FeaturesHeliyon10.1016/j.heliyon.2024.e38195(e38195)Online publication date: Sep-2024
https://doi.org/10.1016/j.heliyon.2024.e38195
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://doi.org/10.1007/s00354-024-00241-w
TAKAYANAGI TIZUMI K(2023)Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' TraitsIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7017E106.D:10(1732-1741)Online publication date: 1-Oct-2023
https://doi.org/10.1587/transinf.2023EDP7017
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Index Terms

Agent-based computational investing recommender system
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information

Published In

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

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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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2013

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Short-paper

Conference

RecSys '13

Sponsor:

SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

View all

Wang JHu R(2024)A Two-stage Recommendation Optimization Algorithm Based on Item Popularity and User FeaturesHeliyon10.1016/j.heliyon.2024.e38195(e38195)Online publication date: Sep-2024
https://doi.org/10.1016/j.heliyon.2024.e38195
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://doi.org/10.1007/s00354-024-00241-w
TAKAYANAGI TIZUMI K(2023)Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' TraitsIEICE Transactions on Information and Systems10.1587/transinf.2023EDP7017E106.D:10(1732-1741)Online publication date: 1-Oct-2023
https://doi.org/10.1587/transinf.2023EDP7017
Takayanagi TChen CIzumi KChen HDuh WHuang HKato MMothe JPoblete B(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
https://dl.acm.org/doi/10.1145/3539618.3592035
Takayanagi TIzumi KKato ATsunedomi NAbe YChen HDuh WHuang HKato MMothe JPoblete B(2023)Personalized Stock Recommendation with Investors' Attention and Contextual InformationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591850(3339-3343)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591850
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
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Wijerathne NSamarathunge JRathnayake KJayasinghe SAhangama SPerera IDhananjaya VSivaneasharajah L(2023)Deep Learning Based Personalized Stock Recommender SystemNeural Information Processing10.1007/978-981-99-8148-9_29(362-374)Online publication date: 26-Nov-2023
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Kubota KSato HYamada WOchiai KKawakami H(2022)Content-based Stock Recommendation Using Smartphone DataJournal of Information Processing10.2197/ipsjjip.30.36130(361-371)Online publication date: 2022
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Chou YChen CHuang S(2022)Modeling behavior sequence for personalized fund recommendation with graphical deep collaborative filteringExpert Systems with Applications: An International Journal10.1016/j.eswa.2021.116311192:COnline publication date: 6-May-2022
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Hung THuang S(2022)Addressing the cold-start problem of recommendation systems for financial products by using few-shot deep learningApplied Intelligence10.1007/s10489-022-03374-x52:13(15529-15546)Online publication date: 16-Mar-2022
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