research-article

Dual-Embedding based Deep Latent Factor Models for Recommendation

Authors:

Yanmin ZhuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 5

Article No.: 85, Pages 1 - 24

https://doi.org/10.1145/3447395

Published: 15 April 2021 Publication History

Abstract

Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to the recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this article proposes a dual-embedding based deep latent factor method for recommendation with implicit feedback. In addition to learning a primitive embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users) and propose attentive neural methods to discriminate the importance of interacted users/items for dual-embedding learning. We design two dual-embedding based deep latent factor models, DELF and DESEQ, for pure collaborative filtering and temporal collaborative filtering (i.e., sequential recommendation), respectively. The novel attempt of the proposed models is to capture each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on four real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of our methods on item recommendation.

References

[1]

Charu C. Aggarwal. 2016. Recommender Systems - The Textbook. Springer.

Digital Library

[2]

Charu C. Aggarwal and Srinivasan Parthasarathy. 2001. Mining massively incomplete data sets by conceptual reconstruction. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 227--232.

[3]

Georgios Alexandridis, Georgios Siolas, and Andreas Stafylopatis. 2017. ParVecMF: A paragraph vector-based matrix factorization recommender system. arXiv preprint arXiv:1706.07513 (2017).

[4]

Georgios Alexandridis, Thanos Tagaris, Giorgos Siolas, and Andreas Stafylopatis. 2019. From free-text user reviews to product recommendation using paragraph vectors and matrix factorization. In Proceedings of the Companion of the 2019 World Wide Web Conference. 335--343.

Digital Library

[5]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations (ICLR'15).

[6]

Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 2017. A generic coordinate descent framework for learning from implicit feedback. In Proceedings of the 26th International Conference on World Wide Web. 1341--1350.

Digital Library

[7]

Yoshua Bengio, Aaron C. Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35, 8 (2013), 1798--1828.

Digital Library

[8]

John S. Breese, David Heckerman, and Carl Myers Kadie. 1998. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence. 43--52.

[9]

John F. Canny. 2002. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 238--245.

Digital Library

[10]

Jingyuan Chen, Hanwang Zhang, Xiangnan He, Liqiang Nie, Wei Liu, and Tat-Seng Chua. 2017. Attentive collaborative filtering: Multimedia recommendation with item- and component-level attention. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 335--344.

Digital Library

[11]

Lei Chen, Le Wu, Richang Hong, Kun Zhang, and Meng Wang. 2020. Revisiting graph based collaborative filtering: A Linear residual graph convolutional network approach. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. 27--34.

[12]

Xu Chen, Hongteng Xu, Yongfeng Zhang, Jiaxi Tang, Yixin Cao, Zheng Qin, and Hongyuan Zha. 2018. Sequential recommendation with user memory networks. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. Yi Chang, Chengxiang Zhai, Yan Liu, and Yoelle Maarek (Eds.). ACM, 108--116.

Digital Library

[13]

Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence. 2605--2611.

Digital Library

[14]

Weiyu Cheng, Yanyan Shen, and Linpeng Huang. 2020. Adaptive factorization network: Learning adaptive-order feature interactions. In Proceedings of the 34th AAAI Conference on Artificial Intelligence. 3609--3616. Retrieved from https://aaai.org/ojs/index.php/AAAI/article/view/5768

[15]

Weiyu Cheng, Yanyan Shen, Yanmin Zhu, and Linpeng Huang. 2018. DELF: A dual-embedding based deep latent factor model for recommendation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3329--3335.

[16]

Qiang Cui, Shu Wu, Qiang Liu, Wen Zhong, and Liang Wang. 2020. MV-RNN: A multi-view recurrent neural network for sequential recommendation. IEEE Trans. Knowl. Data Eng. 32, 2 (2020), 317--331.

Digital Library

[17]

Robin Devooght, Nicolas Kourtellis, and Amin Mantrach. 2015. Dynamic matrix factorization with priors on unknown values. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 189--198.

Digital Library

[18]

Jingtao Ding, Yuhan Quan, Xiangnan He, Yong Li, and Depeng Jin. 2019. Reinforced negative sampling for recommendation with exposure data. In Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2230--2236.

[19]

Xue Geng, Hanwang Zhang, Jingwen Bian, and Tat-Seng Chua. 2015. Learning image and user features for recommendation in social networks. In Proceedings of the 2015 IEEE International Conference on Computer Vision. 4274--4282.

Digital Library

[20]

Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011. Deep sparse rectifier neural networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. 315--323.

[21]

Ruining He and Julian McAuley. 2016. Fusing similarity models with Markov chains for sparse sequential recommendation. In Proceedings of the IEEE 16th International Conference on Data Mining. 191--200.

[22]

Xiangnan He, Tao Chen, Min-Yen Kan, and Xiao Chen. 2015. TriRank: Review-aware explainable recommendation by modeling aspects. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 1661--1670.

Digital Library

[23]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 639--648.

Digital Library

[24]

Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, and Kazunari Sugiyama. 2014. Predicting the popularity of web 2.0 items based on user comments. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. 233--242.

Digital Library

[25]

Xiangnan He, Zhankui He, Jingkuan Song, Zhenguang Liu, Yu-Gang Jiang, and Tat-Seng Chua. 2018. NAIS: Neural attentive item similarity model for recommendation. IEEE Trans. Knowl. Data Eng. 30, 12 (2018), 2354--2366.

Digital Library

[26]

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. 173--182.

Digital Library

[27]

Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval. 549--558.

Digital Library

[28]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR'16).

[29]

Kurt Hornik, Maxwell B. Stinchcombe, and Halbert White. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 5 (1989), 359--366.

[30]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). 263--272.

Digital Library

[31]

Dietmar Jannach and Malte Ludewig. 2017. When recurrent neural networks meet the neighborhood for session-based recommendation. In Proceedings of the 11th ACM Conference on Recommender Systems. 306--310.

Digital Library

[32]

Santosh Kabbur, Xia Ning, and George Karypis. 2013. FISM: Factored item similarity models for top-N recommender systems. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 659--667.

Digital Library

[33]

Wang-Cheng Kang and Julian J. McAuley. 2018. Self-attentive sequential recommendation. In Proceedings of the IEEE International Conference on Data Mining. IEEE Computer Society, 197--206.

[34]

Eunhui Kim and Munchurl Kim. 2016. Topic-tracking-based dynamic user modeling with TV recommendation applications. Appl. Intell. 44, 4 (2016), 771--792.

Digital Library

[35]

Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference on Learning Representations (ICLR'15).

[36]

Yehuda Koren. 2008. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 426--434.

Digital Library

[37]

Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 447--456.

Digital Library

[38]

Walid Krichene and Steffen Rendle. 2020. On sampled metrics for item recommendation. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 1748--1757.

Digital Library

[39]

Ranzhen Li, Yanyan Shen, and Yanmin Zhu. 2018. Next point-of-interest recommendation with temporal and multi-level context attention. In Proceedings of the IEEE International Conference on Data Mining. 1110--1115.

[40]

Sheng Li, Jaya Kawale, and Yun Fu. 2015. Deep collaborative filtering via marginalized denoising auto-encoder. In Proceedings of the 24th ACM International Conference on Information and Knowledge Management. 811--820.

Digital Library

[41]

Roi Livni, Shai Shalev-Shwartz, and Ohad Shamir. 2014. On the computational efficiency of training neural networks. In Proceedings of the Annual Conference on Neural Information Processing Systems. 855--863.

[42]

Thomas Nedelec, Elena Smirnova, and Flavian Vasile. 2016. Content2vec: Specializing joint representations of product images and text for the task of product recommendation. (2016).

[43]

Makbule Gulcin Ozsoy. 2016. From word embeddings to item recommendation. arXiv preprint arXiv:1601.01356 (2016).

[44]

Makbule Gulcin Ozsoy. 2020. Utilizing fasttext for venue recommendation. arXiv preprint arXiv:2005.12982 (2020).

[45]

Rong Pan and Martin Scholz. 2009. Mind the gaps: Weighting the unknown in large-scale one-class collaborative filtering. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 667--676.

Digital Library

[46]

Rong Pan, Yunhong Zhou, Bin Cao, Nathan Nan Liu, Rajan M. Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM’08). 502--511.

Digital Library

[47]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the Advances in Neural Information Processing Systems. H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Eds.). Curran Associates, Inc., 8024--8035.

[48]

Arkadiusz Paterek. 2007. Improving regularized singular value decomposition for collaborative filtering. In Proceedings of KDD Cup and Workshop. Vol. 2007. 5--8.

[49]

Lutz Prechelt. 1998. Early stopping-but when? In Neural Networks: Tricks of the Trade. Springer, 55--69.

Digital Library

[50]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 452--461.

Digital Library

[51]

Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized Markov chains for next-basket recommendation. In Proceedings of the 19th International Conference on World Wide Web. 811--820.

Digital Library

[52]

Steffen Rendle and Lars Schmidt-Thieme. 2008. Online-updating regularized kernel matrix factorization models for large-scale recommender systems. In Proceedings of the 2008 ACM Conference on Recommender Systems. 251--258.

Digital Library

[53]

Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference. 285--295.

Digital Library

[54]

Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International World Wide Web Conference. 285--295.

Digital Library

[55]

Jiaxi Tang and Ke Wang. 2018. Personalized Top-N sequential recommendation via convolutional sequence embedding. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 565--573.

Digital Library

[56]

Flavian Vasile, Elena Smirnova, and Alexis Conneau. 2016. Meta-Prod2Vec: Product embeddings using side-information for recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems. 225--232.

Digital Library

[57]

Maksims Volkovs and Guang Wei Yu. 2015. Effective latent models for binary feedback in recommender systems. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 313--322.

Digital Library

[58]

Hao Wang, Naiyan Wang, and Dit-Yan Yeung. 2015. Collaborative deep learning for recommender systems. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1235--1244.

Digital Library

[59]

Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 2015. Learning hierarchical representation model for nextbasket recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 403--412.

Digital Library

[60]

Suhang Wang, Yilin Wang, Jiliang Tang, Kai Shu, Suhas Ranganath, and Huan Liu. 2017. What your images reveal: Exploiting visual contents for point-of-interest recommendation. In Proceedings of the 26th International Conference on World Wide Web. 391--400.

Digital Library

[61]

Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. KGAT: Knowledge graph attention network for recommendation. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 950--958.

Digital Library

[62]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 165--174.

Digital Library

[63]

Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for Top-N recommender systems. In Proceedings of the 9th ACM International Conference on Web Search and Data Mining. 153--162.

Digital Library

[64]

Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep matrix factorization models for recommender systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 3203--3209.

[65]

Fuzheng Zhang, Nicholas Jing Yuan, Defu Lian, Xing Xie, and Wei-Ying Ma. 2016. Collaborative knowledge base embedding for recommender systems. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 353--362.

Digital Library

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Guo LZhu YGao MTao YYu JChen CBaeza-Yates RBonchi F(2024)Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672056(944-955)Online publication date: 25-Aug-2024
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Chen JLiu KLuo XYuan YSedraoui KAl-Turki YZhou M(2024)A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete DataIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2024.12457511:11(2220-2235)Online publication date: Nov-2024
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Index Terms

Dual-Embedding based Deep Latent Factor Models for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 5

October 2021

508 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3461317

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 15 April 2021

Accepted: 01 January 2021

Revised: 01 December 2020

Received: 01 July 2020

Published in TKDD Volume 15, Issue 5

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Cited By

Guo LZhu YGao MTao YYu JChen CBaeza-Yates RBonchi F(2024)Consistency and Discrepancy-Based Contrastive Tripartite Graph Learning for RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672056(944-955)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3672056
Chen JLiu KLuo XYuan YSedraoui KAl-Turki YZhou M(2024)A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete DataIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2024.12457511:11(2220-2235)Online publication date: Nov-2024
https://doi.org/10.1109/JAS.2024.124575
Al-Ameen SSudharsan BAl-Taie RShah TRanjan R(2024)LEAP: Lifelong Learning Edge-Cloud Adaptive Fused Framework for Mobility Prediction2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825926(6707-6716)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825926
Ou WHuynh V(2024)Aspect-level item recommendation based on user reviews with variational autoencodersInformation Sciences10.1016/j.ins.2024.120655671(120655)Online publication date: Jun-2024
https://doi.org/10.1016/j.ins.2024.120655
Kim JLim C(2023)Feature Extracted Deep Neural Collaborative Filtering for E-Book Service RecommendationsApplied Sciences10.3390/app1311683313:11(6833)Online publication date: 5-Jun-2023
https://doi.org/10.3390/app13116833
Li HYu LNiu XLeng YDu Q(2023)Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer GateACM Transactions on Knowledge Discovery from Data10.1145/360461518:1(1-28)Online publication date: 10-Aug-2023
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Ihemelandu NEkstrand M(2023)Candidate Set Sampling for Evaluating Top-N Recommendation2023 IEEE International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT59888.2023.00018(88-94)Online publication date: 26-Oct-2023
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Li CIshak IIbrahim HZolkepli MSidi FLi C(2023)Deep Learning-Based Recommendation System: Systematic Review and ClassificationIEEE Access10.1109/ACCESS.2023.332335311(113790-113835)Online publication date: 2023
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Pan YXu LDash Wu DOlson D(2023)An online-to-offline service recommendation method based on two-layer knowledge networksInformation Sciences: an International Journal10.1016/j.ins.2023.119574648:COnline publication date: 1-Nov-2023
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Torkashvand AJameii SReza A(2023)Deep learning-based collaborative filtering recommender systems: a comprehensive and systematic reviewNeural Computing and Applications10.1007/s00521-023-08958-335:35(24783-24827)Online publication date: 5-Oct-2023
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