skip to main content
10.1145/3366423.3380133acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems

Published: 20 April 2020 Publication History

Abstract

Recommender systems (RSs) commonly describe its user-item preferences with a high-dimensional and sparse (HiDS) matrix filled with non-negative data. A non-negative latent factor (NLF) model relying on a single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is frequently adopted to process such an HiDS matrix. However, an NLF model mostly adopts Euclidean distance for its objective function, which is naturally a special case of α-β-divergence. Moreover, it frequently suffers slow convergence. For addressing these issues, this study proposes a generalized and fast-converging non-negative latent factor (GFNLF) model. Its main idea is two-fold: a) adopting α-β-divergence for its objective function, thereby enhancing its representation ability for HiDS data; b) deducing its momentum-incorporated non-negative multiplicative update (MNMU) algorithm, thereby achieving its fast convergence. Empirical studies on two HiDS matrices emerging from real RSs demonstrate that with carefully-tuned hyperparameters, a GFNLF model outperforms state-of-the-art models in both computational efficiency and prediction accuracy for missing data of an HiDS matrix.

References

[1]
Paul Resnick and Hal R. Varian. 1997. Recommender systems. Communications of the ACM 40, 3 (1997), 56-59.
[2]
Chao Wang, Qi Liu, Runze Wu, Enhong Chen, Chuanren Liu, Xunpeng Huang, and Zhenya Huang. 2018. Confidence-Aware Matrix Factorization for Recommender Systems. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI). 434-442.
[3]
Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings Of the 26th International Conference on World Wide Web (WWW). 173-182.
[4]
Gábor Takács, István Pilászy, Bottyán Németh, and Domonkos Tikk. 2009. Scalable Collaborative Filtering Approaches for Large Recommender Systems. Journal of Machine Learning Research 10, 623-656.
[5]
Tianqiao Liu, Zhiwei Wang, Jiliang Tang, Songfan Yang, Gale Yan Huang, and Zitao Liu. 2019. Recommender Systems with Heterogeneous Side Information. In Proceedings Of the 28th International Conference on World Wide Web (WWW). 3027-3033.
[6]
Cen Chen, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li, and Minghui Qiu. 2017. Locally connected deep learning framework for industrial-scale recommender systems. In Proceedings Of the 26th International Conference on World Wide Web (WWW). 769-770.
[7]
Xin Luo, Mengchu Zhou, Shuai Li, Zhuhong You, Yunni Xia, and Qingsheng Zhu. 2016. A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method. IEEE Transactions on Neural Networks and Learning Systems 27, 3 (2016), 579-592.
[8]
Trong Dinh Thac Do and Longbing Cao. 2018. Coupled poisson factorization integrated with user/item metadata for modeling popular and sparse ratings in scalable recommendation. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI). 2918-2925.
[9]
Jing Lin, Weike Pan, and Zhong Ming. 2018. MF-DMPC: Matrix Factorization with Dual Multiclass Preference Context for Rating Prediction. In Proceedings of the International Conference on Web Services (ICWS). 337-349.
[10]
James Chambua, Zhendong Niu, and Yifan Zhu. 2019. User preferences prediction approach based on embedded deep summaries. Expert Systems with Applications 132, 87-98.
[11]
Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, and Tat-Seng Chua. 2019. Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences. In Proceedings of the 28th International Conference on World Wide Web (WWW). 151-161.
[12]
Qiang Liu, Shu Wu, Liang Wang. 2017. DeepStyle: Learning user preferences for visual recommendation. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 841-844.
[13]
Xin Luo, MengChu Zhou, Yunni Xia, Qingsheng Zhu, Ahmed Chiheb Ammari, and Ahmed Alabdulwahab. 2016. Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models. IEEE Transactions on Neural Networks and Learning Systems 27, 3 (2016), 524-537.
[14]
Xin Dong, Lei Yu, Zhonghuo Wu, Yuxia Sun, Lingfeng Yuan, and Fangxi Zhang. 2017. A hybrid collaborative filtering model with deep structure for recommender systems. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI). 1309-1315.
[15]
Hao Ma, Irwin King, and Michael R. Lyu. 2009. Learning to recommend with social trust ensemble. in Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 203-210.
[16]
Tavi Nathanson, Ephrat Bitton, and Kenneth Y. Goldberg. 2007. Eigentaste 5.0: constant-time adaptability in a recommender system using item clustering. In Proceedings of the ACM conference on Recommender systems (RecSys). 149-152.
[17]
Dimitrios Rafailidis and Alexandros Nanopoulos. 2015. Modeling users preference dynamics and side information in recommender systems. IEEE Transactions on Systems, Man, and Cybernetics 46, 6 (2015), 782-792.
[18]
Guangneng Hu, Xinyu Dai, Fengyu Qiu, Rui Xia, Tao Li, Shujian Huang, and Jiajun Chen. 2018. Collaborative filtering with topic and social latent factors incorporating implicit feedback. ACM Transactions on Knowledge Discovery from Data 12, 2 (2018), 1-23.
[19]
Yehuda Koren, Robert M. Bell, and Chris Volinsky. 2009. Matrix Factorization Techniques for Recommender Systems. IEEE Computer 42, 8 (2009), 30-37.
[20]
Ye Yuan, Xin Luo, and Mingsheng Shang. 2018. Effects of preprocessing and training biases in latent factor models for recommender systems. Neurocomputing 275, 2019-2030.
[21]
Zhiyong Cheng, Ying Ding, Lei Zhu, and Mohan S. Kankanhalli. 2018. Aspect-aware latent factor model: Rating prediction with ratings and reviews. In Proceedings Of the 27th International Conference on World Wide Web (WWW). 639-648.
[22]
Guibing Guo, Jie Zhang, and Neil Yorke-Smith. 2015. TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI). 123-129.
[23]
Hao Li, Keqin Li, Jiyao An, Weihua Zheng, and Kenli Li. 2019. An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs. Information Sciences 496, 464-484.
[24]
Jing Lin, Weike Pan, and Zhong Ming. 2018. MF-DMPC: Matrix Factorization with Dual Multiclass Preference Context for Rating Prediction. In Proceedings of International Conference on Web Services (ICWS). 337-349.
[25]
Sheng Zhang, Weihong Wang, James Ford, and Fillia Makedon. 2006. Learning from Incomplete Ratings Using Non-negative Matrix Factorization. In Proceedings of the International Conference on Data Mining (ICDM). 549-553.
[26]
Daniel D. Lee and H. Sebastian Seung. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401, 788-791.
[27]
Yangyang Xu, Wotao Yin, Zaiwen Wen, and Yin Zhang. 2012. An alternating direction algorithm for matrix completion with nonnegative factors. Frontiers of Mathematics in China 7, 2 (2012), 365-384.
[28]
Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th International Conference on Data Mining (ICDM). 263-272.
[29]
Antonio Hernando, Jesús Bobadilla, and Fernando Ortega. 2016. A non negative matrix factorization for collaborative filtering recommender systems based on a Bayesian probabilistic model. Knowledge-Based Systems 97, 188-202.
[30]
Andrzej Cichocki, Sergio Cruces, and Shun-ichi Amari. 2011. Generalized alpha-beta divergences and their application to robust nonnegative matrix factorization. Entropy 13, 1 (2011), 134-170.
[31]
Jin Shang and Mingxuan Sun. 2018. Local Low-Rank Hawkes Processes for Temporal User-Item Interactions. In Proceedings of the International Conference on Data Mining (ICDM). 27-436.
[32]
Stephen Boyd and Lieven Vandenberghe. 2009. Convex Optimization. Cambridge University Press.
[33]
Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John Riedl. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22, 1 (2004), 5-53.
[34]
Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, and John Riedl. 1997. GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40, 3 (1997), 77-87.
[35]
Lukas Brozovsky and Vaclav Petricek. 2007. Recommender system for online dating service. eprint arXiv:cs/0703042.
[36]
Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. AutoRec: Autoencoders Meet Collaborative Filtering. In Proceedings of the 24th International Conference on World Wide Web. 111-112.
[37]
Qingxian Wang, Binbin Peng, Xiaoyu Shi, Tianqi Shang, and Mingsheng Shang. 2019. DCCR: Deep Collaborative Conjunctive Recommender for Rating Prediction. IEEE Access 7, 60186-60198.
[38]
Pablo Ribalta Lorenzo, Jakub Nalepa, Michal Kawulok, Luciano Sánchez Ramos, and José Ranilla Pastor. 2017. Particle swarm optimization for hyper-parameter selection in deep neural networks. In Proceedings of the ACM International Conference on Genetic and Evolutionary Computation (GECCO). 481-488.
[39]
Shuai Li, Alexandros Karatzoglou, and Claudio Gentile. 2016. Collaborative filtering bandits. In Proceedings of the 39th International conference on Research and Development in Information Retrieval (SIGIR). 539-548.
[40]
Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Giovanni Zappella, and Evans Etrue. 2017. On Context-Dependent Clustering of Bandits. In Proceedings of the 34th International Conference on Machine Learning (ICML). 1253-1262.
[41]
Antoine Bordes, Nicolas Usunier, Alberto García Durán, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Proceedings of advances in neural information processing systems (NIPS). 2787-2795.

Cited By

View all
  • (2024)MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete DataIEEE Transactions on Services Computing10.1109/TSC.2023.333157017:2(575-588)Online publication date: Mar-2024
  • (2024)A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor AnalysisIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.338973332:7(4049-4061)Online publication date: Jul-2024
  • (2024)Diverse Transformation-Augmented Graph Tensor Convolutional Network for Dynamic Graph Representation Learning2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831069(3384-3389)Online publication date: 6-Oct-2024
  • Show More Cited By

Index Terms

  1. A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems
    Index terms have been assigned to the content through auto-classification.

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WWW '20: Proceedings of The Web Conference 2020
    April 2020
    3143 pages
    ISBN:9781450370233
    DOI:10.1145/3366423
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 April 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. α-β-divergence
    2. High-dimensional and sparse
    3. Momentum
    4. Non-negative latent factor
    5. Recommender system
    6. User preference

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    WWW '20
    Sponsor:
    WWW '20: The Web Conference 2020
    April 20 - 24, 2020
    Taipei, Taiwan

    Acceptance Rates

    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)20
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 12 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete DataIEEE Transactions on Services Computing10.1109/TSC.2023.333157017:2(575-588)Online publication date: Mar-2024
    • (2024)A Fuzzy PID-Incorporated Stochastic Gradient Descent Algorithm for Fast and Accurate Latent Factor AnalysisIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.338973332:7(4049-4061)Online publication date: Jul-2024
    • (2024)Diverse Transformation-Augmented Graph Tensor Convolutional Network for Dynamic Graph Representation Learning2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC54092.2024.10831069(3384-3389)Online publication date: 6-Oct-2024
    • (2024)An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis2024 7th International Symposium on Autonomous Systems (ISAS)10.1109/ISAS61044.2024.10552611(1-6)Online publication date: 7-May-2024
    • (2024)Tensor Graph Convolutional Network for Dynamic Graph Representation Learning2024 7th International Symposium on Autonomous Systems (ISAS)10.1109/ISAS61044.2024.10552463(1-5)Online publication date: 7-May-2024
    • (2023)A Hybrid Recommender System Based on Autoencoder and Latent Feature AnalysisEntropy10.3390/e2507106225:7(1062)Online publication date: 14-Jul-2023
    • (2023)A Graph-Incorporated Latent Factor Analysis Model for High-Dimensional and Sparse DataIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.329286611:4(907-917)Online publication date: Oct-2023
    • (2023)Diverse Biases Nonnegative Latent Factorization of TensorsDynamic Network Representation Based on Latent Factorization of Tensors10.1007/978-981-19-8934-6_4(43-56)Online publication date: 8-Mar-2023
    • (2023)PID-Incorporated Latent Factorization of TensorsDynamic Network Representation Based on Latent Factorization of Tensors10.1007/978-981-19-8934-6_3(27-42)Online publication date: 8-Mar-2023
    • (2023)Multiple Biases-Incorporated Latent Factorization of TensorsDynamic Network Representation Based on Latent Factorization of Tensors10.1007/978-981-19-8934-6_2(11-26)Online publication date: 8-Mar-2023
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media