research-article

Understanding and improving relational matrix factorization in recommender systems

Authors:

Boi FaltingsAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 41 - 48

https://doi.org/10.1145/2507157.2507178

Published: 12 October 2013 Publication History

Abstract

Matrix factorization techniques such as the singular value decomposition (SVD) have had great success in recommender systems. We present a new perspective of SVD for constructing a latent space from the training data, which is justified by the theory of hypergraph model. We show that the vectors representing the items in the latent space can be grouped into (approximately) orthogonal clusters which correspond to the vertex clusters in the co-rating hypergraph, and the lengths of the vectors are indicators of the representativeness of the items. These properties are used for making top-$N$ recommendations in a two-phase algorithm. In this work, we provide a new explanation for the significantly better performance of the asymmetric SVD approaches and a novel algorithm for better diversity in top-N recommendations.

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Cited By

Pereira FRodrigues LHenklain MFreitas HOliveira DCristea ACarvalho LIsotani SBenedict ADorodchi Mde Oliveira E(2023)Toward Human–AI Collaboration: A Recommender System to Support CS1 Instructors to Select Problems for Assignments and ExamsIEEE Transactions on Learning Technologies10.1109/TLT.2022.322412116:3_Part_2(457-472)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TLT.2022.3224121
She YOuyang C(2023)Self-Supervised Learning Recommendation Algorithm Based on Meta-Paths2023 IEEE 5th International Conference on Advanced Information and Communication Technologies (AICT)10.1109/AICT61584.2023.10452672(1-4)Online publication date: 21-Nov-2023
https://doi.org/10.1109/AICT61584.2023.10452672
Kohli MKar ABangalore AAP P(2022)Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory studyBrain Informatics10.1186/s40708-022-00164-69:1Online publication date: 25-Jul-2022
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Index Terms

Understanding and improving relational matrix factorization in recommender systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 ACM.

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: 12 October 2013

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RecSys '13

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SIGCHI

RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

Acceptance Rates

RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

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

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RecSys '24

Sponsor:
sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

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Cited By

Pereira FRodrigues LHenklain MFreitas HOliveira DCristea ACarvalho LIsotani SBenedict ADorodchi Mde Oliveira E(2023)Toward Human–AI Collaboration: A Recommender System to Support CS1 Instructors to Select Problems for Assignments and ExamsIEEE Transactions on Learning Technologies10.1109/TLT.2022.322412116:3_Part_2(457-472)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TLT.2022.3224121
She YOuyang C(2023)Self-Supervised Learning Recommendation Algorithm Based on Meta-Paths2023 IEEE 5th International Conference on Advanced Information and Communication Technologies (AICT)10.1109/AICT61584.2023.10452672(1-4)Online publication date: 21-Nov-2023
https://doi.org/10.1109/AICT61584.2023.10452672
Kohli MKar ABangalore AAP P(2022)Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory studyBrain Informatics10.1186/s40708-022-00164-69:1Online publication date: 25-Jul-2022
https://doi.org/10.1186/s40708-022-00164-6
Zhang YLiu X(2022)Learning attention embeddings based on memory networks for neural collaborative recommendation▪Expert Systems with Applications: An International Journal10.1016/j.eswa.2021.115439183:COnline publication date: 3-Jan-2022
https://dl.acm.org/doi/10.1016/j.eswa.2021.115439
Ban YLee K(2021)How the Multiplicity of Suggested Information Affects the Behavior of a User in a Recommender SystemElectronics10.3390/electronics1006074110:6(741)Online publication date: 20-Mar-2021
https://doi.org/10.3390/electronics10060741
Xia PJiang WWu JXiao SWang G(2021)Exploiting Temporal Dynamics in Product Reviews for Dynamic Sentiment Prediction at the Aspect LevelACM Transactions on Knowledge Discovery from Data10.1145/344145115:4(1-29)Online publication date: 18-Apr-2021
https://dl.acm.org/doi/10.1145/3441451
Nguyen VHuynh VSriboonchitta S(2020)Integrating Community Context Information Into a Reliably Weighted Collaborative Filtering System Using Soft RatingsIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2017.272654750:4(1318-1330)Online publication date: Apr-2020
https://doi.org/10.1109/TSMC.2017.2726547
Wang HWu J(2020)Collaborative Filtering-Based Film Recommendation Technique Utilizing Time and Film GenresJournal of Physics: Conference Series10.1088/1742-6596/1631/1/0121051631(012105)Online publication date: 24-Sep-2020
https://doi.org/10.1088/1742-6596/1631/1/012105
Naseer MZhang WZhu W(2020)A Framework to Strengthen up Business Interests in Students by Using Matrix Factorization on Web LogIntelligent Technologies and Applications10.1007/978-981-15-5232-8_28(322-332)Online publication date: 9-May-2020
https://doi.org/10.1007/978-981-15-5232-8_28
Wegba KLu ALi YWang WBerkovsky SHijikata YRekimoto JBurnett MBillinghurst MQuigley A(2018)Interactive Storytelling for Movie Recommendation through Latent Semantic AnalysisProceedings of the 23rd International Conference on Intelligent User Interfaces10.1145/3172944.3172979(521-533)Online publication date: 5-Mar-2018
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