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Online-updating regularized kernel matrix factorization models for large-scale recommender systems

Published: 23 October 2008 Publication History

Abstract

Regularized matrix factorization models are known to generate high quality rating predictions for recommender systems. One of the major drawbacks of matrix factorization is that once computed, the model is static. For real-world applications dynamic updating a model is one of the most important tasks. Especially when ratings on new users or new items come in, updating the feature matrices is crucial.
In this paper, we generalize regularized matrix factorization (RMF) to regularized kernel matrix factorization (RKMF). Kernels provide a flexible method for deriving new matrix factorization methods. Furthermore with kernels nonlinear interactions between feature vectors are possible. We propose a generic method for learning RKMF models. From this method we derive an online-update algorithm for RKMF models that allows to solve the new-user/new-item problem. Our evaluation indicates that our proposed online-update methods are accurate in approximating a full retrain of a RKMF model while the runtime of online-updating is in the range of milliseconds even for huge datasets like Netflix.

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cover image ACM Conferences
RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems
October 2008
348 pages
ISBN:9781605580937
DOI:10.1145/1454008
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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Publication History

Published: 23 October 2008

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Author Tags

  1. matrix factorization
  2. online-update
  3. recommender system

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RecSys08: ACM Conference on Recommender Systems
October 23 - 25, 2008
Lausanne, Switzerland

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

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Cited By

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  • (2025)An introduction to collaborative filtering through the lens of the Netflix PrizeKnowledge and Information Systems10.1007/s10115-024-02315-zOnline publication date: 10-Jan-2025
  • (2024)Preliminary Study on Incremental Learning for Large Language Model-based Recommender SystemsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679922(4051-4055)Online publication date: 21-Oct-2024
  • (2024)TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657791(1659-1669)Online publication date: 10-Jul-2024
  • (2024)GPT4Rec: Graph Prompt Tuning for Streaming RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657720(1774-1784)Online publication date: 10-Jul-2024
  • (2023)Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking ApplicationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608824(744-749)Online publication date: 14-Sep-2023
  • (2023)Dynamically Expandable Graph Convolution for Streaming RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583237(1457-1467)Online publication date: 30-Apr-2023
  • (2023)Be Causal: De-Biasing Social Network Confounding in RecommendationACM Transactions on Knowledge Discovery from Data10.1145/353372517:1(1-23)Online publication date: 20-Feb-2023
  • (2023)RØROS: Building a Responsive Online Recommender System via Meta-Gradients UpdatingICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP49357.2023.10096336(1-5)Online publication date: 4-Jun-2023
  • (2023)MbSRS: A multi-behavior streaming recommender systemInformation Sciences10.1016/j.ins.2023.01.101631(145-163)Online publication date: Jun-2023
  • (2022)Neural Matrix Factorization Recommendation for User Preference Prediction Based on Explicit and Implicit FeedbackComputational Intelligence and Neuroscience10.1155/2022/95939572022Online publication date: 1-Jan-2022
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