FinRec: The 3rd International Workshop on Personalization & Recommender Systems in Financial Services

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  • Toine Bogers
  • Cataldo Musto
  • David Wang
  • Alexander Felfernig
  • Bruun, Simone Borg
  • Giovanni Semeraro
  • Yong Zheng

The FinRec workshop series offers a central forum for the study and discussion of the domain-specific aspects, challenges, and opportunities of RecSys and other related technologies in the financial services domain. Six years after the second edition of the workshop, the recent advances in the area of personalization and recommendation in financial services fostered the need for a new workshop aiming at bringing together researchers and practitioners working in financial services-related areas. Accordingly, the third edition of the event aims to: (1) understand and discuss open research challenges, (2) provide an overview of existing technologies using recommender systems in the financial services domain, and (3) provide an interactive platform for information exchange between industry and academia.

Original languageEnglish
Title of host publicationRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems
Number of pages3
PublisherAssociation for Computing Machinery, Inc.
Publication date2022
Pages688-690
ISBN (Electronic)9781450392785
DOIs
Publication statusPublished - 2022
Event16th ACM Conference on Recommender Systems, RecSys 2022 - Seattle, United States
Duration: 18 Sep 202223 Sep 2022

Conference

Conference16th ACM Conference on Recommender Systems, RecSys 2022
LandUnited States
BySeattle
Periode18/09/202223/09/2022
SponsorACM Special Interest Group on Artificial Intelligence (SIGAI), ACM Special Interest Group on Computer-Human Interaction (SIGCHI), ACM Special Interest Group on Hypertext, Hypermedia, and Web (SIGWEB), ACM Special Interest Group on Information Retrieval (SIGIR), ACM Special Interest Group on Knowledge Discovery in Data (SIGKDD)

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Publisher Copyright:
© 2022 Owner/Author.

    Research areas

  • financial services, joint optimization, personalization, recommender systems, stakeholders

ID: 344981235