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Regularizing Matrix Factorization with User and Item Embeddings for Recommendation

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Dongwon LeeAuthors Info & Claims

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Pages 687 - 696

https://doi.org/10.1145/3269206.3271730

Published: 17 October 2018 Publication History

Abstract

Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked. In experimental validation, the RME outperforms competing state-of-the-art models in both explicit and implicit feedback datasets, significantly improving Recall@5 by 5.9~7.0%, NDCG@20 by 4.3~5.6%, and MAP@10 by 7.9~8.9%. In addition, under the cold-start scenario for users with the lowest number of interactions, against the competing models, the RME outperforms NDCG@5 by 20.2% and 29.4% in MovieLens-10M and MovieLens-20M datasets, respectively. Our datasets and source code are available at: https://github.com/thanhdtran/RME.git.

References

[1]

Deepak Agarwal and Bee-Chung Chen. 2009. Regression-based latent factor models. In SIGKDD. 19--28.

Digital Library

[2]

Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, and Aaron Courville. 2015. Learning distributed representations from reviews for collaborative filtering. In RecSys . 147--154.

Digital Library

[3]

Oren Barkan and Noam Koenigstein. 2016. Item2vec: neural item embedding for collaborative filtering. In MLSP Workshop . 1--6.

[4]

Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 2017. A generic coordinate descent framework for learning from implicit feedback. In WWW . 1341--1350.

Digital Library

[5]

Marcel Blattner, Yi-Cheng Zhang, and Sergei Maslov. 2007. Exploring an opinion network for taste prediction: An empirical study. Physica A: Statistical Mechanics and its Applications (2007), 753--758.

[6]

Da Cao, Liqiang Nie, Xiangnan He, Xiaochi Wei, Shunzhi Zhu, and Tat-Seng Chua. 2017. Embedding Factorization Models for Jointly Recommending Items and User Generated Lists. In SIGIR. 585--594.

Digital Library

[7]

Mukund Deshpande and George Karypis. 2004. Item-based top-n recommendation algorithms. TOIS (2004), 143--177.

Digital Library

[8]

Robin Devooght, Nicolas Kourtellis, and Amin Mantrach. 2015. Dynamic matrix factorization with priors on unknown values. In SIGKDD. 189--198.

Digital Library

[9]

Nemanja Djuric, Hao Wu, Vladan Radosavljevic, Mihajlo Grbovic, and Narayan Bhamidipati. 2015. Hierarchical neural language models for joint representation of streaming documents and their content. In WWW. 248--255.

Digital Library

[10]

Elie Guàrdia-Sebaoun, Vincent Guigue, and Patrick Gallinari. 2015. Latent trajectory modeling: A light and efficient way to introduce time in recommender systems. In RecSys. 281--284.

Digital Library

[11]

Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW. 507--517.

Digital Library

[12]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW. 173--182.

Digital Library

[13]

Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. 2016. Fast matrix factorization for online recommendation with implicit feedback. In SIGIR . 549--558.

Digital Library

[14]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In ICDM. 263--272.

Digital Library

[15]

Yehuda Koren. 2008. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In SIGKDD. 426--434.

Digital Library

[16]

Yehuda Koren. 2009. Collaborative filtering with temporal dynamics. In SIGKDD. 447--456.

Digital Library

[17]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer (2009).

Digital Library

[18]

Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In ICML. 1188--1196.

Digital Library

[19]

Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. In NIPS . 2177--2185.

Digital Library

[20]

Dawen Liang, Jaan Altosaar, Laurent Charlin, and David M Blei. 2016a. Factorization meets the item embedding: Regularizing matrix factorization with item co-occurrence. In RecSys. 59--66.

Digital Library

[21]

Dawen Liang, Laurent Charlin, James McInerney, and David M Blei. 2016b. Modeling user exposure in recommendation. In WWW. 951--961.

Digital Library

[22]

Bing Liu, Wee Sun Lee, Philip S Yu, and Xiaoli Li. 2002. Partially supervised classification of text documents. In ICML. 387--394.

Digital Library

[23]

Julian McAuley and Jure Leskovec. 2013. Hidden factors and hidden topics: understanding rating dimensions with review text. In RecSys . 165--172.

Digital Library

[24]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS . 3111--3119.

Digital Library

[25]

Rong Pan, Yunhong Zhou, Bin Cao, Nathan N Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. 2008. One-class collaborative filtering. In ICDM. 502--511.

Digital Library

[26]

Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In EMNLP. 1532--1543.

[27]

István Pilászy, Dávid Zibriczky, and Domonkos Tikk. 2010. Fast als-based matrix factorization for explicit and implicit feedback datasets. In RecSys . 71--78.

Digital Library

[28]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI. 452--461.

Digital Library

[29]

Paul Resnick, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. 1994. GroupLens: an open architecture for collaborative filtering of netnews. In CSCW . 175--186.

Digital Library

[30]

Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007. Restricted Boltzmann machines for collaborative filtering. In ICML. 791--798.

Digital Library

[31]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In WWW. 285--295.

Digital Library

[32]

Harald Steck. 2010. Training and testing of recommender systems on data missing not at random. In SIGKDD . 713--722.

Digital Library

[33]

Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Adv. Artificial Intellegence (2009).

Digital Library

[34]

Maksims Volkovs and Guang Wei Yu. 2015. Effective latent models for binary feedback in recommender systems. In SIGIR . 313--322.

Digital Library

[35]

Chong Wang and David M Blei. 2011. Collaborative topic modeling for recommending scientific articles. In SIGKDD . 448--456.

Digital Library

[36]

Can Wang, Qiudan Li, Lei Wang, and Daniel Dajun Zeng. 2017. Incorporating message embedding into co-factor matrix factorization for retweeting prediction. In IJCNN. 1265--1272.

[37]

Hsiang-Fu Yu, Cho-Jui Hsieh, Si Si, and Inderjit S Dhillon. 2014. Parallel matrix factorization for recommender systems. Knowledge and Information Systems (2014), 793--819.

Digital Library

[38]

Weinan Zhang, Tianqi Chen, Jun Wang, and Yong Yu. 2013. Optimizing top-n collaborative filtering via dynamic negative item sampling. In SIGIR . 785--788.

Digital Library

[39]

Yunhong Zhou, Dennis Wilkinson, Robert Schreiber, and Rong Pan. 2008. Large-scale parallel collaborative filtering for the netflix prize. In International Conference on Algorithmic Applications in Management. 337--348.

Digital Library

Cited By

Peng SSiet SIlkhomjon SKim DPark D(2024)Integration of Deep Reinforcement Learning with Collaborative Filtering for Movie Recommendation SystemsApplied Sciences10.3390/app1403115514:3(1155)Online publication date: 30-Jan-2024
https://doi.org/10.3390/app14031155
Elahi FFazlali MMalazi HElahi M(2024)Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.318521235:3(470-483)Online publication date: Mar-2024
https://doi.org/10.1109/TPDS.2022.3185212
Zheng XNi ZZhong XLuo Y(2024)Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit InformationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318294235:1(1205-1216)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3182942
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Index Terms

Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

CIKM '18: Proceedings of the 27th ACM International Conference on Information and Knowledge Management

October 2018

2362 pages

ISBN:9781450360142

DOI:10.1145/3269206

General Chair:
Alfredo Cuzzocrea
University of Trieste, Italy
,
Program Chairs:
James Allan
University of Massachusetts, USA
,
Norman Paton
University of Manchester, United Kingdom
,
Divesh Srivastava
AT&T Labs Research, USA
,
Rakesh Agrawal
Data Insights Lab, USA
,
Andrei Broder
Google Research, USA
,
Mohammed Zaki
Rensselaer Polytechnic Institute, USA
,
Selcuk Candan
Arizona State University, USA
,
Alexandros Labrinidis
University of Pittsburgh, USA
,
Assaf Schuster
Technion, Israel
,
Haixun Wang
Google Research, USA

Copyright © 2018 ACM.

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Published: 17 October 2018

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Microsoft Azure Research Award
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CIKM '18: The 27th ACM International Conference on Information and Knowledge Management

October 22 - 26, 2018

Torino, Italy

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Peng SSiet SIlkhomjon SKim DPark D(2024)Integration of Deep Reinforcement Learning with Collaborative Filtering for Movie Recommendation SystemsApplied Sciences10.3390/app1403115514:3(1155)Online publication date: 30-Jan-2024
https://doi.org/10.3390/app14031155
Elahi FFazlali MMalazi HElahi M(2024)Parallel Fractional Stochastic Gradient Descent With Adaptive Learning for Recommender SystemsIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2022.318521235:3(470-483)Online publication date: Mar-2024
https://doi.org/10.1109/TPDS.2022.3185212
Zheng XNi ZZhong XLuo Y(2024)Kernelized Deep Learning for Matrix Factorization Recommendation System Using Explicit and Implicit InformationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318294235:1(1205-1216)Online publication date: Jan-2024
https://doi.org/10.1109/TNNLS.2022.3182942
Chen YLee W(2024)An Evolving Preference-Based Recommendation SystemIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.33439988:2(1118-1124)Online publication date: Apr-2024
https://doi.org/10.1109/TETCI.2023.3343998
Chen YChen YHsu C(2024)G-TransRec: A Transformer-Based Next-Item Recommendation With Time PredictionIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.335431511:3(4175-4188)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2024.3354315
Li XMa CLi GXu PLiu CYuan YWang G(2023)Meta Auxiliary Learning for Top-K RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.322315535:10(10857-10870)Online publication date: 1-Oct-2023
https://doi.org/10.1109/TKDE.2022.3223155
Han LChen LShi X(2022)Recommendation Model Based on Probabilistic Matrix Factorization and Rated Item RelevanceElectronics10.3390/electronics1124416011:24(4160)Online publication date: 13-Dec-2022
https://doi.org/10.3390/electronics11244160
George GLal A(2022)Hy-MOMCybernetics and Information Technologies10.2478/cait-2022-000922:1(134-150)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.2478/cait-2022-0009
Wu HMa CZhang YLiu XTang RCoates MAl Hasan MXiong L(2022)Adapting Triplet Importance of Implicit Feedback for Personalized RecommendationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557229(2148-2157)Online publication date: 17-Oct-2022
https://dl.acm.org/doi/10.1145/3511808.3557229
Sun ZFang HYang JQu XLiu HYu DOng YZhang J(2022)DaisyRec 2.0: Benchmarking Recommendation for Rigorous EvaluationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3231891(1-20)Online publication date: 2022
https://doi.org/10.1109/TPAMI.2022.3231891
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