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User Recommendation in Content Curation Platforms

Authors:

James CaverleeAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 627 - 635

https://doi.org/10.1145/3336191.3371822

Published: 22 January 2020 Publication History

Abstract

We propose a personalized user recommendation framework for content curation platforms that models preferences for both users and the items they engage with simultaneously. In this way, user preferences for specific item types (e.g., fantasy novels) can be balanced with user specialties (e.g., reviewing novels with strong female protagonists). In particular, the proposed model has three unique characteristics: (i) it simultaneously learns both user-item and user-user preferences through a multi-aspect autoencoder model; (ii) it fuses the latent representations of user preferences on users and items to construct shared factors through an adversarial framework; and (iii) it incorporates an attention layer to produce weighted aggregations of different latent representations, leading to improved personalized recommendation of users and items. Through experiments against state-of-the-art models, we find the proposed framework leads to a 18.43% (Goodreads) and 6.14% (Spotify) improvement in top-k user recommendation.

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Ayestarán Crespo RLavilla Muñoz DVega de la Faya O(2024)Los hilos de X como estímulo al aprendizaje para los alumnos universitariosX-threads as a learning stimulus for universitary studentsVISUAL REVIEW. International Visual Culture Review / Revista Internacional de Cultura Visual10.62161/revvisual.v16.529016:5(251-260)Online publication date: 29-Jul-2024
https://doi.org/10.62161/revvisual.v16.5290
Shanthalakshmi Revathy JMangaiyarkkarasi J(2024)Integration of Metaverse and Machine Learning in the Education SectorImpact and Potential of Machine Learning in the Metaverse10.4018/979-8-3693-5762-0.ch004(74-99)Online publication date: 26-Jul-2024
https://doi.org/10.4018/979-8-3693-5762-0.ch004
Cui YWang X(2023)Travel Recommendation Model Integrating Long-term and Short-term User Preferences2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST)10.1109/IAECST60924.2023.10503005(504-508)Online publication date: 8-Dec-2023
https://doi.org/10.1109/IAECST60924.2023.10503005
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Index Terms

User Recommendation in Content Curation Platforms
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
,
Xia "Ben" Hu
Texas A&M University
,
Program Chairs:
Mounia Lalmas
Spotify
,
Wei Wang
University of California, Los Angeles

Copyright © 2020 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 ACM 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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Published: 22 January 2020

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WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining

February 3 - 7, 2020

TX, Houston, USA

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Cited By

Ayestarán Crespo RLavilla Muñoz DVega de la Faya O(2024)Los hilos de X como estímulo al aprendizaje para los alumnos universitariosX-threads as a learning stimulus for universitary studentsVISUAL REVIEW. International Visual Culture Review / Revista Internacional de Cultura Visual10.62161/revvisual.v16.529016:5(251-260)Online publication date: 29-Jul-2024
https://doi.org/10.62161/revvisual.v16.5290
Shanthalakshmi Revathy JMangaiyarkkarasi J(2024)Integration of Metaverse and Machine Learning in the Education SectorImpact and Potential of Machine Learning in the Metaverse10.4018/979-8-3693-5762-0.ch004(74-99)Online publication date: 26-Jul-2024
https://doi.org/10.4018/979-8-3693-5762-0.ch004
Cui YWang X(2023)Travel Recommendation Model Integrating Long-term and Short-term User Preferences2023 5th International Academic Exchange Conference on Science and Technology Innovation (IAECST)10.1109/IAECST60924.2023.10503005(504-508)Online publication date: 8-Dec-2023
https://doi.org/10.1109/IAECST60924.2023.10503005
T. B. LP. S. S(2021)An Analysis on E-Learning and Its RecommendationsNew Opportunities for Sentiment Analysis and Information Processing10.4018/978-1-7998-8061-5.ch009(166-187)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-8061-5.ch009
Antoniak MWalsh MMimno D(2021)Tags, Borders, and CatalogsProceedings of the ACM on Human-Computer Interaction10.1145/34491035:CSCW1(1-29)Online publication date: 22-Apr-2021
https://dl.acm.org/doi/10.1145/3449103
Ban YHe J(2021)Local Clustering in Contextual Multi-Armed BanditsProceedings of the Web Conference 202110.1145/3442381.3450058(2335-2346)Online publication date: 19-Apr-2021
https://dl.acm.org/doi/10.1145/3442381.3450058
Mazinan ENaderi HMirzarezaee MSaati S(2021)Microblogs recommendations based on implicit similarity in content social networksThe Journal of Supercomputing10.1007/s11227-021-03864-8Online publication date: 2-Jun-2021
https://doi.org/10.1007/s11227-021-03864-8

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