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Fast online learning through offline initialization for time-sensitive recommendation

Published: 25 July 2010 Publication History

Abstract

Recommender problems with large and dynamic item pools are ubiquitous in web applications like content optimization, online advertising and web search. Despite the availability of rich item meta-data, excess heterogeneity at the item level often requires inclusion of item-specific "factors" (or weights) in the model. However, since estimating item factors is computationally intensive, it poses a challenge for time-sensitive recommender problems where it is important to rapidly learn factors for new items (e.g., news articles, event updates, tweets) in an online fashion. In this paper, we propose a novel method called FOBFM (Fast Online Bilinear Factor Model) to learn item-specific factors quickly through online regression. The online regression for each item can be performed independently and hence the procedure is fast, scalable and easily parallelizable. However, the convergence of these independent regressions can be slow due to high dimensionality. The central idea of our approach is to use a large amount of historical data to initialize the online models based on offline features and learn linear projections that can effectively reduce the dimensionality. We estimate the rank of our linear projections by taking recourse to online model selection based on optimizing predictive likelihood. Through extensive experiments, we show that our method significantly and uniformly outperforms other competitive methods and obtains relative lifts that are in the range of 10-15% in terms of predictive log-likelihood, 200-300% for a rank correlation metric on a proprietary My Yahoo! dataset; it obtains 9% reduction in root mean squared error over the previously best method on a benchmark MovieLens dataset using a time-based train/test data split.

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Cited By

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  • (2023)Online semi-supervised learning with mix-typed streaming featuresProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25596(4720-4728)Online publication date: 7-Feb-2023
  • (2023)Measuring Item Global Residual Value for Fair RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591724(269-278)Online publication date: 19-Jul-2023
  • (2023)A distributed real-time recommender system for big data streamsAin Shams Engineering Journal10.1016/j.asej.2022.10202614:8(102026)Online publication date: Aug-2023
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cover image ACM Conferences
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
July 2010
1240 pages
ISBN:9781450300551
DOI:10.1145/1835804
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: 25 July 2010

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Author Tags

  1. dyadic data
  2. factorization
  3. latent factor
  4. recommender systems
  5. reduced rank regression

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2023)Online semi-supervised learning with mix-typed streaming featuresProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i4.25596(4720-4728)Online publication date: 7-Feb-2023
  • (2023)Measuring Item Global Residual Value for Fair RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591724(269-278)Online publication date: 19-Jul-2023
  • (2023)A distributed real-time recommender system for big data streamsAin Shams Engineering Journal10.1016/j.asej.2022.10202614:8(102026)Online publication date: Aug-2023
  • (2021)Level-Based Learning Algorithm Based on the Difficulty Level of the Test ProblemApplied Sciences10.3390/app1110438011:10(4380)Online publication date: 12-May-2021
  • (2021)A Survey on Stream-Based Recommender SystemsACM Computing Surveys10.1145/345344354:5(1-36)Online publication date: 25-May-2021
  • (2021)Lambda Learner: Fast Incremental Learning on Data StreamsProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467172(3492-3502)Online publication date: 14-Aug-2021
  • (2019)Real-time incremental recommendation for streaming data based on apache flinkIntelligent Data Analysis10.3233/IDA-18433023:6(1421-1437)Online publication date: 8-Nov-2019
  • (2019)A Probabilistic Model for Collaborative FilteringProceedings of the 9th International Conference on Web Intelligence, Mining and Semantics10.1145/3326467.3326472(1-8)Online publication date: 26-Jun-2019
  • (2019)Coupled Variational Recurrent Collaborative FilteringProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330940(335-343)Online publication date: 25-Jul-2019
  • (2019)Recommendation over time: a probabilistic model of time-aware recommender systemsScience China Information Sciences10.1007/s11432-018-9915-862:11Online publication date: 9-Oct-2019
  • Show More Cited By

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