research-article

Beyond Globally Optimal: Focused Learning for Improved Recommendations

Authors:

John AndersonAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 203 - 212

https://doi.org/10.1145/3038912.3052713

Published: 03 April 2017 Publication History

Abstract

When building a recommender system, how can we ensure that all items are modeled well? Classically, recommender systems are built, optimized, and tuned to improve a global prediction objective, such as root mean squared error. However, as we demonstrate, these recommender systems often leave many items badly-modeled and thus under-served. Further, we give both empirical and theoretical evidence that no single matrix factorization, under current state-of-the-art methods, gives optimal results for each item.

As a result, we ask: how can we learn additional models to improve the recommendation quality for a specified subset of items? We offer a new technique called focused learning, based on hyperparameter optimization and a customized matrix factorization objective. Applying focused learning on top of weighted matrix factorization, factorization machines, and LLORMA, we demonstrate prediction accuracy improvements on multiple datasets. For instance, on MovieLens we achieve as much as a 17% improvement in prediction accuracy for niche movies, cold-start items, and even the most badly-modeled items in the original model.

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Cited By

Vargas Mesquita dos Santos RVentorim Comarela GCesar Guimarães de Lima Junior M(2024)Implementação de Medidas de Justiça Social: Um Estudo de Caso no Contexto Cultural para Diferentes Estratégias em Sistemas de RecomendaçãoiSys - Brazilian Journal of Information Systems10.5753/isys.2024.421417:1Online publication date: 27-Dec-2024
https://doi.org/10.5753/isys.2024.4214
Gong YZhang C(2024)Research on Labeling System for Radio and Television Intelligent Recommendation SystemProceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms10.1145/3690407.3690509(599-603)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1145/3690407.3690509
Balasubramanian KAlshabanah AMarkowitz EVer Steeg GAnnavaram M(2024)Biased User History Synthesis for Personalized Long-Tail Item RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688141(189-199)Online publication date: 8-Oct-2024
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Index Terms

Beyond Globally Optimal: Focused Learning for Improved Recommendations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

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WWW '17

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IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Vargas Mesquita dos Santos RVentorim Comarela GCesar Guimarães de Lima Junior M(2024)Implementação de Medidas de Justiça Social: Um Estudo de Caso no Contexto Cultural para Diferentes Estratégias em Sistemas de RecomendaçãoiSys - Brazilian Journal of Information Systems10.5753/isys.2024.421417:1Online publication date: 27-Dec-2024
https://doi.org/10.5753/isys.2024.4214
Gong YZhang C(2024)Research on Labeling System for Radio and Television Intelligent Recommendation SystemProceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms10.1145/3690407.3690509(599-603)Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1145/3690407.3690509
Balasubramanian KAlshabanah AMarkowitz EVer Steeg GAnnavaram M(2024)Biased User History Synthesis for Personalized Long-Tail Item RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688141(189-199)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688141
Chang BMeng CMa HChang SGu YPeng YFeng JZhang YBi SChi EChen MChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Cluster Anchor Regularization to Alleviate Popularity Bias in Recommender SystemsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3648312(151-160)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589335.3648312
Lin AWang JZhu ZCaverlee J(2024)Federated Conversational Recommender SystemsAdvances in Information Retrieval10.1007/978-3-031-56069-9_4(50-65)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56069-9_4
Zhao ZZhou KWang XZhao WPan FCao ZWen J(2023)Alleviating the Long-Tail Problem in Conversational Recommender SystemsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608812(374-385)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608812
Gong ZWu XChen LZheng ZWang SXu AWang CWu F(2023)Full Index Deep Retrieval: End-to-End User and Item Structures for Cold-start and Long-tail Item RecommendationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608773(47-57)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608773
Tomlinson KWan MLu CHecht BTeevan JYang L(2023)Targeted Training for Multi-organization RecommendationACM Transactions on Recommender Systems10.1145/36035081:3(1-18)Online publication date: 14-Jul-2023
https://dl.acm.org/doi/10.1145/3603508
Wang H(2023)Analysis and visualization of the parameter space of matrix factorization-based recommender systems3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)10.1117/12.2686703(209)Online publication date: 28-Jul-2023
https://doi.org/10.1117/12.2686703
Wang H(2023)LogitMat: Zeroshot Learning Algorithm for Recommender Systems without Transfer Learning or Pretrained Models2023 8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA56900.2023.10154697(138-142)Online publication date: 26-Apr-2023
https://doi.org/10.1109/ICCCBDA56900.2023.10154697
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