research-article

NEST: Simulating Pandemic-like Events for Collaborative Filtering by Modeling User Needs Evolution

Authors:

Pablo Castells,

Mark SandersonAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 1430 - 1440

https://doi.org/10.1145/3511808.3557407

Published: 17 October 2022 Publication History

Abstract

We outline a simulation-based study of the effect rapid population-scale concept drifts have on Collaborative Filtering (CF) models. We create a framework for analyzing the effects of macro-trends in population dynamics on the behavior of such models. Our framework characterizes population-scale concept drifts in item preferences and provides a lens to understand the influence events, such as a pandemic, have on CF models. Our experimental results show the initial impact on CF performance at the initial stage of such events, followed by an aggravated population herding effect during the event. The herding introduces a popularity bias that may benefit affected users, but which comes at the expense of a normal user experience. We propose an adaptive ensemble method that can effectively apply optimal algorithms to cope with the change brought about by different stages of the event.

Supplementary Material

MP4 File (CIKM22-fp0571.mp4)

The COVID-19 pandemic has profoundly impacted people's life and behavior. How do recommender systems behave given these changes in user behavior and preferences? We propose a Need Evolution Simulator to study this. This video briefly shows our motivation, research questions, methodology and experiment results.

Download
47.55 MB

References

[1]

Marie Al-Ghossein, Talel Abdessalem, and Anthony BARRÉ. 2021. A Survey on Stream-Based Recommender Systems. ACM Computing Surveys (CSUR), Vol. 54, 5 (2021), 1--36.

Digital Library

[2]

Clayton P. Alderfer. 1969. An Empirical Test of a New Theory of Human Needs. Organizational Behavior and Human Performance, Vol. 4, 2 (May 1969), 142--175. https://doi.org/10.1016/0030-5073(69)90004-X

[3]

Hamsa Bastani, Mohsen Bayati, and Khashayar Khosravi. 2021. Mostly exploration-free algorithms for contextual bandits. Management Science, Vol. 67, 3 (2021), 1329--1349.

Digital Library

[4]

Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. Openai gym. arXiv preprint arXiv:1606.01540 (2016).

[5]

R. Cañamares and P. Castells. 2018. Should I follow the crowd? A probabilistic analysis of the effectiveness of popularity in recommender systems. In Proceedings of the 41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018). ACM, New York, NY, USA, 415--424.

[6]

Roc'io Ca namares, Marcos Redondo, and Pablo Castells. 2019. Multi-armed recommender system bandit ensembles. In Proceedings of the 13th ACM Conference on Recommender Systems. 432--436.

[7]

P. Castells and R. Ca namares. 2018. Characterization of fair experiments for recommender system evaluation: A formal analysis. In Proceedings of the Workshop on Offline Evaluation for Recommender Systems (REVEAL 2018) at the 12th ACM Conference on Recommender Systems (RecSys 2018).

[8]

Pablo Castells, Neil Hurley, and Saul Vargas. 2022. Novelty and diversity in recommender systems. In Recommender systems handbook. Springer, 603--646.

[9]

Dong-Kyu Chae, Jin-Soo Kang, Sang-Wook Kim, and Jung-Tae Lee. 2018. CFGAN: A Generic Collaborative Filtering Framework Based on Generative Adversarial Networks. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (Torino, Italy) (CIKM '18). Association for Computing Machinery, New York, NY, USA, 137--146. https://doi.org/10.1145/3269206.3271743

Digital Library

[10]

Allison J. B. Chaney, Brandon M. Stewart, and Barbara E. Engelhardt. 2018. How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, Vancouver British Columbia Canada, 224--232. https://doi.org/10.1145/3240323.3240370

Digital Library

[11]

Kae H. Chung. 1969. A Markov Chain Model of Human Needs : An Extension of Maslow 's Need Theory. AMJ, Vol. 12, 2 (June 1969), 223--234. https://doi.org/10.5465/254818

[12]

G. L. Ciampaglia, A. Nematzadeh, F. Menczer, and A. Flammini. 2018. How algorithmic popularity bias hinders or promotes quality. Scientific Reports, Vol. 8 (2018).

[13]

Olivier Coibion, Yuriy Gorodnichenko, and Michael Weber. 2020. Labor Markets During the COVID -19 Crisis : A Preliminary View. Technical Report w27017. National Bureau of Economic Research. https://doi.org/10.3386/w27017

[14]

Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos. 2018. Implicit quantile networks for distributional reinforcement learning. In International conference on machine learning. PMLR, 1096--1105.

[15]

Laura Di Renzo, Paola Gualtieri, Francesca Pivari, Laura Soldati, Alda Attinà, Giulia Cinelli, Claudia Leggeri, Giovanna Caparello, Luigi Barrea, Francesco Scerbo, et al. 2020. Eating habits and lifestyle changes during COVID-19 lockdown: an Italian survey. Journal of translational medicine, Vol. 18 (2020), 1--15.

[16]

Lauren K Fink, Lindsay A Warrenburg, Claire Howlin, William M Randall, Niels Chr Hansen, and Melanie Wald-Fuhrmann. 2021. Viral tunes: changes in musical behaviours and interest in coronamusic predict socio-emotional coping during COVID-19 lockdown. Humanities and Social Sciences Communications, Vol. 8, 1 (2021), 1--11.

[17]

Jo ao Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A survey on concept drift adaptation. ACM computing surveys (CSUR), Vol. 46, 4 (2014), 1--37.

[18]

Honghao Gao, Li Kuang, Yuyu Yin, Bin Guo, and Kai Dou. 2020. Mining consuming behaviors with temporal evolution for personalized recommendation in mobile marketing apps. Mobile Networks and Applications, Vol. 25 (2020), 1233--1248.

Digital Library

[19]

Tom Griffiths. 2002. Gibbs sampling in the generative model of latent dirichlet allocation. (2002).

[20]

Shengyu Gu, Beata Ślusarczyk, Sevda Hajizada, Irina Kovalyova, and Amina Sakhbieva. 2021. Impact of the covid-19 pandemic on online consumer purchasing behavior. Journal of Theoretical and Applied Electronic Commerce Research, Vol. 16, 6 (2021), 2263--2281.

[21]

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Trans. Interact. Intell. Syst., Vol. 5, 4, Article 19 (Dec. 2015), 19 pages. https://doi.org/10.1145/2827872

Digital Library

[22]

Ruining He and Julian McAuley. 2016. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In Proceedings of the 25th International Conference on World Wide Web (Montréal, Québec, Canada) (WWW '16). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 507--517. https://doi.org/10.1145/2872427.2883037

Digital Library

[23]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, YongDong 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 (Virtual Event, China) (SIGIR '20). Association for Computing Machinery, New York, NY, USA, 639--648. https://doi.org/10.1145/3397271.3401063

Digital Library

[24]

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 (Perth, Australia) (WWW '17). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 173--182. https://doi.org/10.1145/3038912.3052569

Digital Library

[25]

Harry Helson. 1964. Adaptation-level theory: an experimental and systematic approach to behavior. (1964).

[26]

Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015).

[27]

Thomas Hofmann. 2004. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems (TOIS), Vol. 22, 1 (2004), 89--115.

Digital Library

[28]

Mark Honigsbaum. 2013. Regulating the 1918--19 pandemic: Flu, stoicism and the Northcliffe press. Medical history, Vol. 57, 2 (2013), 165--185.

[29]

Jen-Hung Huang and Yi-Fen Chen. 2006. Herding in online product choice. Psychology & Marketing, Vol. 23, 5 (2006), 413--428.

[30]

Eugene Ie, Chih-wei Hsu, Martin Mladenov, Vihan Jain, Sanmit Narvekar, Jing Wang, Rui Wu, and Craig Boutilier. 2019. Recsim: A configurable simulation platform for recommender systems. arXiv preprint arXiv:1909.04847 (2019).

[31]

Bar Ifrach, Costis Maglaras, and Marco Scarsini. 2014. Bayesian social learning with consumer reviews. ACM SIGMETRICS Performance Evaluation Review, Vol. 41, 4 (2014), 28--28.

Digital Library

[32]

Dmitry Ivanov. 2020. Viable supply chain model: integrating agility, resilience and sustainability perspectives-lessons from and thinking beyond the COVID-19 pandemic. Annals of operations research (2020), 1--21.

[33]

Mohsen Jamali and Martin Ester. 2010. A Matrix Factorization Technique with Trust Propagation for Recommendation in Social Networks. In Proceedings of the Fourth ACM Conference on Recommender Systems (Barcelona, Spain) (RecSys '10). Association for Computing Machinery, New York, NY, USA, 135--142. https://doi.org/10.1145/1864708.1864736

Digital Library

[34]

Wang-Cheng Kang and Julian McAuley. 2018. Self-Attentive Sequential Recommendation. In 2018 IEEE International Conference on Data Mining (ICDM). 197--206. https://doi.org/10.1109/ICDM.2018.00035

[35]

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 (Las Vegas, Nevada, USA) (KDD '08). Association for Computing Machinery, New York, NY, USA, 426--434. https://doi.org/10.1145/1401890.1401944

Digital Library

[36]

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

[37]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009), 30--37.

Digital Library

[38]

Daniel Lemire and Anna Maclachlan. 2005. Slope one predictors for online rating-based collaborative filtering. In Proceedings of the 2005 SIAM International Conference on Data Mining. SIAM, 471--475.

[39]

Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time Interval Aware Self-Attention for Sequential Recommendation. In Proceedings of the 13th International Conference on Web Search and Data Mining (Houston, TX, USA) (WSDM '20). Association for Computing Machinery, New York, NY, USA, 322--330. https://doi.org/10.1145/3336191.3371786

Digital Library

[40]

Jordan J Louviere, David A Hensher, and Joffre D Swait. 2000. Stated choice methods: analysis and applications. Cambridge university press.

Digital Library

[41]

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. 203--210.

Digital Library

[42]

Wei Ji Ma, Jeffrey M Beck, Peter E Latham, and Alexandre Pouget. 2006. Bayesian inference with probabilistic population codes. Nature neuroscience, Vol. 9, 11 (2006), 1432--1438.

[43]

A. H. Maslow. 1943. A Theory of Human Motivation. Psychological Review, Vol. 50, 4 (1943), 370--396. https://doi.org/10.1037/h0054346

[44]

Abraham H. Maslow. 1970. New Introduction: Religions, Values, and Peak-Experiences. Journal of Transpersonal Psychology, Vol. 2, 2 (1970), 83--90.

[45]

Mieszko Mazur, Man Dang, and Miguel Vega. 2021. COVID-19 and the march 2020 stock market crash. Evidence from S&P1500. Finance research letters, Vol. 38 (2021), 101690.

[46]

E. Mena-Maldonado, R. Cañamares, P. Castells, Y. Ren, and M. Sanderson. 2021. Popularity Bias in False-Positive Metrics for Recommender Systems Evaluation. ACM Transactions on Information Systems, Vol. 39, 3 (2021).

Digital Library

[47]

Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. 2015. Human-level control through deep reinforcement learning. nature, Vol. 518, 7540 (2015), 529--533.

[48]

Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 188--197.

[49]

Ravi Philip Rajkumar and SM Arafat. 2021. Model Driven Causal Factors of Panic Buying and Their Implications for Prevention: A Systematic Review. Psychiatry International, Vol. 2, 3 (2021), 325--343.

[50]

Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian Personalized Ranking from Implicit Feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (Montreal, Quebec, Canada) (UAI '09). AUAI Press, Arlington, Virginia, USA, 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 - WWW '10. ACM Press, Raleigh, North Carolina, USA, 811. https://doi.org/10.1145/1772690.1772773

Digital Library

[52]

David Rohde, Stephen Bonner, Travis Dunlop, Flavian Vasile, and Alexandros Karatzoglou. 2018. RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. In Proceedings of the Workshop on Offline Evaluation for Recommender Systems (REVEAL 2018) at the 12th ACM Conference on Recommender Systems (RecSys 2018).

[53]

Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton. 2007. Restricted Boltzmann machines for collaborative filtering. In Proceedings of the 24th international conference on Machine learning. 791--798.

Digital Library

[54]

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

Digital Library

[55]

Sven Schmit and Carlos Riquelme. 2018. Human interaction with recommendation systems. In International Conference on Artificial Intelligence and Statistics. PMLR, 862--870.

[56]

Guy Shani and Asela Gunawardana. 2011. Evaluating Recommendation Systems. Springer US, Boston, MA, 257--297. https://doi.org/10.1007/978-0-387-85820-3_8

[57]

Herbert A Simon. 1956. Rational choice and the structure of the environment. Psychological review, Vol. 63, 2 (1956), 129.

[58]

Marinko Škare, Domingo Riberio Soriano, and Małgorzata Porada-Rochoń. 2021. Impact of COVID -19 on the Travel and Tourism Industry. Technological Forecasting and Social Change, Vol. 163 (Feb. 2021), 120469. https://doi.org/10.1016/j.techfore.2020.120469

[59]

Lones Smith and Peter Sørensen. 2000. Pathological outcomes of observational learning. Econometrica, Vol. 68, 2 (2000), 371--398.

[60]

Xiaoyuan Su and Taghi M. Khoshgoftaar. 2009. A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, Vol. 2009 (2009), 1--19. https://doi.org/10.1155/2009/421425

[61]

Jina Suh, Eric Horvitz, Ryen W. White, and Tim Althoff. 2021. Population-Scale Study of Human Needs During the COVID-19 Pandemic: Analysis and Implications (WSDM '21). Association for Computing Machinery, New York, NY, USA, 4--12. https://doi.org/10.1145/3437963.3441788

Digital Library

[62]

Henri Tajfel, John C Turner, William G Austin, and Stephen Worchel. 1979. An integrative theory of intergroup conflict. Organizational identity: A reader, Vol. 56, 65 (1979), 9780203505984-16.

[63]

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

Digital Library

[64]

Louis Tay and Ed Diener. 2011. Needs and Subjective Well-Being around the World. Journal of Personality and Social Psychology, Vol. 101, 2 (Aug. 2011), 354--365. https://doi.org/10.1037/a0023779

[65]

John B Taylor and John C Williams. 2009. A black swan in the money market. American Economic Journal: Macroeconomics, Vol. 1, 1 (2009), 58--83.

[66]

Alexey Tsymbal. 2004. The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin, Vol. 106, 2 (2004), 58.

[67]

Gerhard Widmer and Miroslav Kubat. 1996. Learning in the presence of concept drift and hidden contexts. Machine learning, Vol. 23, 1 (1996), 69--101.

[68]

Eugene Koh Boon Yau, Nicholas Pang Tze Ping, Wendy Diana Shoesmith, Sandi James, Noor Melissa Nor Hadi, and Jiann Lin Loo. 2020. The behaviour changes in response to COVID-19 pandemic within Malaysia. The Malaysian journal of medical sciences: MJMS, Vol. 27, 2 (2020), 45.

[69]

Kum Fai Yuen, Xueqin Wang, Fei Ma, and Kevin X Li. 2020. The psychological causes of panic buying following a health crisis. International journal of environmental research and public health, Vol. 17, 10 (2020), 3513.

Cited By

Ma CRen YCastells PSanderson MChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Temporal Conformity-aware Hawkes Graph Network for RecommendationsProceedings of the ACM Web Conference 202410.1145/3589334.3645354(3185-3194)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645354

Index Terms

NEST: Simulating Pandemic-like Events for Collaborative Filtering by Modeling User Needs Evolution
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Evaluation of Herd Behavior Caused by Population-scale Concept Drift in Collaborative Filtering
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Concept drift in stream data has been well studied in machine learning applications. In the field of recommender systems, this issue is also widely observed, as known as temporal dynamics in user behavior. Furthermore, in the context of COVID-19 ...
Unifying Explicit and Implicit Feedback for Rating Prediction and Ranking Recommendation Tasks
ICTIR '19: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval

The two main tasks addressed by collaborative filtering approaches are rating prediction and ranking. Rating prediction models leverage explicit feedback (e.g. ratings), and aim to estimate the rating a user would assign to an unseen item. In contrast, ...
Escaping your comfort zone

A recommender system based on a positively-related item-graph targeted for novel and relevant recommendations is proposed.A live test was performed comparing the proposed system with a state-of-the-art matrix factorization algorithm.The proposed system ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

ARC Centre of Excellence for Automated Decision-Making and Society
Australian Research Council Discovery Grant

Conference

CIKM '22

Sponsor:

CIKM '22: The 31st ACM International Conference on Information and Knowledge Management

October 17 - 21, 2022

GA, Atlanta, USA

Acceptance Rates

CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;

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

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
168
Total Downloads

Downloads (Last 12 months)38
Downloads (Last 6 weeks)6

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Ma CRen YCastells PSanderson MChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Temporal Conformity-aware Hawkes Graph Network for RecommendationsProceedings of the ACM Web Conference 202410.1145/3589334.3645354(3185-3194)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645354

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents