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Collaborative Filtering with Behavioral Models

Published: 03 July 2018 Publication History

Abstract

Collaborative filtering (CF) has made it possible to build personalized recommendation models leveraging the collective data of large user groups, albeit with prescribed models that cannot easily leverage the existence of known behavioral models in particular settings. In this paper, we facilitate the combination of CF with existing behavioral models by introducing Bayesian Behavioral Collaborative Filtering (BBCF). BBCF works by embedding arbitrary (black-box) probabilistic models of human behavior in a latent variable Bayesian framework capable of collectively leveraging behavioral models trained on all users for personalized recommendation. There are three key advantages of BBCF compared to traditional CF and non-CF methods: (1) BBCF can leverage highly specialized behavioral models for specific CF use cases that may outperform existing generic models used in standard CF, (2) the behavioral models used in BBCF may offer enhanced intepretability and explainability compared to generic CF methods, and (3) compared to non-CF methods that would train a behavioral model per specific user and thus may suffer when individual user data is limited, BBCF leverages the data of all users thus enabling strong performance across the data availability spectrum including the near cold-start case. Experimentally, we compare BBCF to individual and global behavioral models as well as CF techniques; our evaluation domains span sequential and non-sequential tasks with a range of behavioral models for individual users, tasks, or goal-oriented behavior. Our results demonstrate that BBCF is competitive if not better than existing methods while still offering the interpretability and explainability benefits intrinsic to many behavioral models.

References

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Robert M. Bell and Yehuda Koren. 2007. Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. In ICDM. IEEE Computer Society, 43--52.
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Graeme Best and Robert Fitch. 2015. Bayesian intention inference for trajectory prediction with an unknown goal destination. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2015, Hamburg, Germany, September 28 - October 2, 2015. 5817--5823.
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Graeme Best, Wolfram Martens, and Robert Fitch. 2017. Path Planning With Spatiotemporal Optimal Stopping for Stochastic Mission Monitoring. IEEE Trans. Robotics Vol. 33, 3 (2017), 629--646.
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John S Breese, David Heckerman, and Carl Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 43--52.
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Herbert Buchner, Karim Helwani, Bashar I. Ahmad, and Simon J. Godsill. 2017. Efficient adaptive filtering in compressive domains for sparse systems and relation to transform-domain adaptive filtering. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2017, New Orleans, LA, USA, March 5--9, 2017. 3859--3863.
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Bertrand Clarke. 2003. Comparing Bayes Model Averaging and Stacking When Model Approximation Error Cannot Be Ignored. Journal of Machine Learning Research Vol. 4 (2003), 683--712.

Cited By

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  • (2023)Heterogeneous Multi-Behavior Recommendation Based on Graph Convolutional NetworksIEEE Access10.1109/ACCESS.2023.325199411(22574-22584)Online publication date: 2023
  • (2022)Social Media Profiling Continues to Partake in the Development of Formalistic Self-Concepts. Social Media Users Think So, Too.Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3514094.3534192(238-252)Online publication date: 26-Jul-2022
  • (2022)A probabilistic perspective on nearest neighbor for implicit recommendationInternational Journal of Data Science and Analytics10.1007/s41060-022-00367-416:2(217-235)Online publication date: 29-Oct-2022
  • Show More Cited By

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Information & Contributors

Information

Published In

cover image ACM Conferences
UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
July 2018
393 pages
ISBN:9781450355896
DOI:10.1145/3209219
  • General Chairs:
  • Tanja Mitrovic,
  • Jie Zhang,
  • Program Chairs:
  • Li Chen,
  • David Chin
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 July 2018

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Author Tags

  1. Bayesian model averaging
  2. behavioral modeling
  3. collaborative filtering

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  • Research-article

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  • Ontario Centres of Excellence

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UMAP '18
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UMAP '18 Paper Acceptance Rate 26 of 93 submissions, 28%;
Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

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Cited By

View all
  • (2023)Heterogeneous Multi-Behavior Recommendation Based on Graph Convolutional NetworksIEEE Access10.1109/ACCESS.2023.325199411(22574-22584)Online publication date: 2023
  • (2022)Social Media Profiling Continues to Partake in the Development of Formalistic Self-Concepts. Social Media Users Think So, Too.Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3514094.3534192(238-252)Online publication date: 26-Jul-2022
  • (2022)A probabilistic perspective on nearest neighbor for implicit recommendationInternational Journal of Data Science and Analytics10.1007/s41060-022-00367-416:2(217-235)Online publication date: 29-Oct-2022
  • (2021)A probabilistic perspective on nearest neighbor for implicit recommendation2021 International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW53433.2021.00011(27-35)Online publication date: Dec-2021
  • (2019)Setting the StageAdjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization10.1145/3314183.3323846(301-307)Online publication date: 6-Jun-2019

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