research-article

Localized factor models for multi-context recommendation

Authors:

Deepak Agarwal,

Bee-Chung Chen,

Bo LongAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 609 - 617

https://doi.org/10.1145/2020408.2020504

Published: 21 August 2011 Publication History

Abstract

Combining correlated information from multiple contexts can significantly improve predictive accuracy in recommender problems. Such information from multiple contexts is often available in the form of several incomplete matrices spanning a set of entities like users, items, features, and so on. Existing methods simultaneously factorize these matrices by sharing a single set of factors for entities across all contexts. We show that such a strategy may introduce significant bias in estimates and propose a new model that ameliorates this issue by positing local, context-specific factors for entities. To avoid over-fitting in contexts with sparse data, the local factors are connected through a shared global model. This sharing of parameters allows information to flow across contexts through multivariate regressions among local factors, instead of enforcing exactly the same factors for an entity, everywhere. Model fitting is done in an EM framework, we show that the E-step can be fitted through a fast multi-resolution Kalman filter algorithm that ensures scalability. Experiments on benchmark and real-world Yahoo! datasets clearly illustrate the usefulness of our approach. Our model significantly improves predictive accuracy, especially in cold-start scenarios.

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Cited By

Tian CXie YChen XLi YZhao X(2024)Privacy-preserving Cross-domain Recommendation with Federated Graph LearningACM Transactions on Information Systems10.1145/365344842:5(1-29)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3653448
Du JYe ZGuo BYu ZYao LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Identifiability of Cross-Domain Recommendation via Causal Subspace DisentanglementProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657758(2091-2101)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657758
Zaman NJana A(2024)Cross domain recommendation using dual inductive transfer learningMultimedia Tools and Applications10.1007/s11042-024-19967-2Online publication date: 8-Aug-2024
https://doi.org/10.1007/s11042-024-19967-2
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Index Terms

Localized factor models for multi-context recommendation
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Canonical correlation analysis
2. Mathematics of computing
  1. Information theory
  2. Probability and statistics
    1. Multivariate statistics
    2. Statistical paradigms
      1. Contingency table analysis
      2. Regression analysis

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cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 ACM.

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Published: 21 August 2011

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

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

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Cited By

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https://dl.acm.org/doi/10.1145/3653448
Du JYe ZGuo BYu ZYao LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Identifiability of Cross-Domain Recommendation via Causal Subspace DisentanglementProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657758(2091-2101)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657758
Zaman NJana A(2024)Cross domain recommendation using dual inductive transfer learningMultimedia Tools and Applications10.1007/s11042-024-19967-2Online publication date: 8-Aug-2024
https://doi.org/10.1007/s11042-024-19967-2
Guo XLi SGuo NCao JLiu XMa QGan RZhao Y(2023)Disentangled Representations Learning for Multi-target Cross-domain RecommendationACM Transactions on Information Systems10.1145/357283541:4(1-27)Online publication date: 23-Mar-2023
https://dl.acm.org/doi/10.1145/3572835
Sajadi SSadjadi SChahooki M(2023)AutoMF: A hybrid matrix factorization model with deep learning to select anti-viral drugs for Covid-19Journal of Computational Science10.1016/j.jocs.2023.10215474(102154)Online publication date: Dec-2023
https://doi.org/10.1016/j.jocs.2023.102154
Huang NHu RWang XDing HHuang X(2023)Cross-platform sequential recommendation with sharing item-level relevance dataInformation Sciences10.1016/j.ins.2022.11.112621(265-286)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2022.11.112
Wang YLi CLiu ZLi MTang JXie XChen LYu P(2022)An Adaptive Graph Pre-training Framework for Localized Collaborative FilteringACM Transactions on Information Systems10.1145/355537241:2(1-27)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1145/3555372
Zang TZhu YLiu HZhang RYu J(2022)A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future DirectionsACM Transactions on Information Systems10.1145/354845541:2(1-39)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1145/3548455
Wu CZhang RGuo JFan YCheng X(2022)Are Neural Ranking Models Robust?ACM Transactions on Information Systems10.1145/353492841:2(1-36)Online publication date: 21-Dec-2022
https://dl.acm.org/doi/10.1145/3534928
Wang CChen YXi WHuang LXie G(2022)Cross-Domain Explicit–Implicit-Mixed Collaborative Filtering Neural NetworkIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2021.312926152:11(6983-6997)Online publication date: Nov-2022
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