research-article

Generalizing matrix factorization through flexible regression priors

Authors:

Deepak Agarwal,

Bee-Chung ChenAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 13 - 20

https://doi.org/10.1145/2043932.2043940

Published: 23 October 2011 Publication History

Abstract

Predicting user "ratings" on items is a crucial task in recommender systems. Matrix factorization methods that computes a low-rank approximation of the incomplete user-item rating matrix provide state-of-the-art performance, especially for users and items with several past ratings (warm starts). However, it is a challenge to generalize such methods to users and items with few or no past ratings (cold starts). Prior work [4][32] have generalized matrix factorization to include both user and item features for performing better regularization of factors as well as provide a model for smooth transition from cold starts to warm starts. However, the features were incorporated via linear regression on factor estimates. In this paper, we generalize this process to allow for arbitrary regression models like decision trees, boosting, LASSO, etc. The key advantage of our approach is the ease of computing --- any new regression procedure can be incorporated by "plugging" in a standard regression routine into a few intermediate steps of our model fitting procedure. With this flexibility, one can leverage a large body of work on regression modeling, variable selection, and model interpretation. We demonstrate the usefulness of this generalization using the MovieLens and Yahoo! Buzz datasets.

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Cited By

Abakarim SQassimi SRakrak S(2024)Leveraging Deep Collaborative Filtering for Advanced Recommender SystemsDigital Technologies and Applications10.1007/978-3-031-68650-4_7(62-71)Online publication date: 17-Aug-2024
https://doi.org/10.1007/978-3-031-68650-4_7
Behara GYannam VNayyar ABagal D(2023)Integrating metadata into deep autoencoder for handling prediction task of collaborative recommender systemMultimedia Tools and Applications10.1007/s11042-023-17029-783:14(42125-42147)Online publication date: 16-Oct-2023
https://doi.org/10.1007/s11042-023-17029-7
Mishra NChaturvedi SVij ATripathi S(2021)Research Problems in Recommender systemsJournal of Physics: Conference Series10.1088/1742-6596/1717/1/0120021717(012002)Online publication date: 10-Jan-2021
https://doi.org/10.1088/1742-6596/1717/1/012002
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Index Terms

Generalizing matrix factorization through flexible regression priors
1. Computing methodologies
  1. Modeling and simulation
    1. Simulation theory
      1. Systems theory
2. Mathematics of computing
  1. Information theory
  2. Probability and statistics
    1. Statistical paradigms
      1. Statistical graphics

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Published In

cover image ACM Conferences

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

Copyright © 2011 ACM.

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Publication History

Published: 23 October 2011

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SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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

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Cited By

Abakarim SQassimi SRakrak S(2024)Leveraging Deep Collaborative Filtering for Advanced Recommender SystemsDigital Technologies and Applications10.1007/978-3-031-68650-4_7(62-71)Online publication date: 17-Aug-2024
https://doi.org/10.1007/978-3-031-68650-4_7
Behara GYannam VNayyar ABagal D(2023)Integrating metadata into deep autoencoder for handling prediction task of collaborative recommender systemMultimedia Tools and Applications10.1007/s11042-023-17029-783:14(42125-42147)Online publication date: 16-Oct-2023
https://doi.org/10.1007/s11042-023-17029-7
Mishra NChaturvedi SVij ATripathi S(2021)Research Problems in Recommender systemsJournal of Physics: Conference Series10.1088/1742-6596/1717/1/0120021717(012002)Online publication date: 10-Jan-2021
https://doi.org/10.1088/1742-6596/1717/1/012002
Gwadabe TLiu Y(2021)Improving graph neural network for session-based recommendation system via non-sequential interactionsNeurocomputing10.1016/j.neucom.2021.10.034468:C(111-122)Online publication date: 30-Dec-2021
https://dl.acm.org/doi/10.1016/j.neucom.2021.10.034
Chen YWang YZhao XYin HMarkov IRijke M(2020)Local Variational Feature-Based Similarity Models for Recommending Top-N New ItemsACM Transactions on Information Systems10.1145/337215438:2(1-33)Online publication date: 11-Feb-2020
https://dl.acm.org/doi/10.1145/3372154
Chen YZhao XLiu JGe BZhang W(2020)Item Cold-Start Recommendation with Personalized Feature SelectionJournal of Computer Science and Technology10.1007/s11390-020-9864-z35:5(1217-1230)Online publication date: 30-Sep-2020
https://doi.org/10.1007/s11390-020-9864-z
Wang HZhao YWang QZhou B(2019)Latent Factor Models Fusing User & Item Attributes2019 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI44817.2019.9002724(3201-3206)Online publication date: Dec-2019
https://doi.org/10.1109/SSCI44817.2019.9002724
Chen YZhao XLiu JGe BZhang W(2019)Learning to Select User-Specific Features for Top-N Recommendation of New Items2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW.2019.00-19(141-147)Online publication date: Apr-2019
https://doi.org/10.1109/ICDEW.2019.00-19
Deldjoo YFerrari Dacrema MConstantin MEghbal-Zadeh HCereda SSchedl MIonescu BCremonesi P(2019)Movie genomeUser Modeling and User-Adapted Interaction10.1007/s11257-019-09221-y29:2(291-343)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s11257-019-09221-y
Lian DZheng KGe YCao LChen EXie X(2018)GeoMF++ACM Transactions on Information Systems10.1145/318216636:3(1-29)Online publication date: 23-Mar-2018
https://dl.acm.org/doi/10.1145/3182166
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