research-article

iRec: An Interactive Recommendation Framework

Authors:

Nícollas Silva,

Heitor Werneck,

Adriano C.M. Pereira,

Leonardo RochaAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 3165 - 3175

https://doi.org/10.1145/3477495.3531754

Published: 07 July 2022 Publication History

Abstract

Nowadays, most e-commerce and entertainment services have adopted interactive Recommender Systems (RS) to guide the entire journey of users into the system. This task has been addressed as a Multi-Armed Bandit problem where systems must continuously learn and recommend at each iteration. However, despite the recent advances, there is still a lack of consensus on the best practices to evaluate such bandit solutions. Several variables might affect the evaluation process, but most of the works have only been concerned about the accuracy of each method. Thus, this work proposes an interactive RS framework named iRec. It covers the whole experimentation process by following the main RS guidelines. The iRec provides three modules to prepare the dataset, create new recommendation agents, and simulate the interactive scenario. Moreover, it also contains several state-of-the-art algorithms, a hyperparameter tuning module, distinct evaluation metrics, different ways of visualizing the results, and statistical validation.

References

[1]

Marc Abeille and Alessandro Lazaric. 2017. Linear thompson sampling revisited. In Artificial Intelligence and Statistics. PMLR, 176--184.

[2]

Xavier Amatriain and Justin Basilico. 2015. Recommender systems in industry: A netflix case study. In Recommender systems handbook . Springer, 385--419.

[3]

Xavier Amatriain and Justin Basilico. 2016. Past, present, and future of recommender systems: An industry perspective. In Proceedings of the 10th ACM conference on recommender systems. 211--214.

Digital Library

[4]

Vito Walter Anelli, Alejandro Bellog'in, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, and Tommaso Di Noia. 2021. Elliot: a comprehensive and rigorous framework for reproducible recommender systems evaluation. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval .

Digital Library

[5]

Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone, and Joseph Trotta. 2019. Local popularity and time in top-n recommendation. In European Conference on Information Retrieval . Springer, 861--868.

Digital Library

[6]

Peter Auer. 2002. Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research, Vol. 3, Nov (2002), 397--422.

Digital Library

[7]

Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning, Vol. 47, 2 (2002), 235--256.

Digital Library

[8]

Andrea Barraza-Urbina, Georgia Koutrika, Mathieu d'Aquin, and Conor Hayes. 2018. BEARS: Towards an evaluation framework for bandit-based interactive recommender systems. REVEAL 18, October 6--7, 2018, Vancouver, Canada (2018).

[9]

Alejandro Bellog'in and Pablo Sánchez. 2017. Revisiting Neighbourhood-Based Recommenders For Temporal Scenarios. In RecTemp RecSys . 40--44.

[10]

Olivier Chapelle and Lihong Li. 2011. An empirical evaluation of thompson sampling. Advances in neural information processing systems, Vol. 24 (2011), 2249--2257.

[11]

Paolo Cremonesi, Yehuda Koren, and Roberto Turrin. 2010. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems. 39--46.

Digital Library

[12]

Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, and Dietmar Jannach. 2021. A troubling analysis of reproducibility and progress in recommender systems research. ACM Transactions on Information Systems (TOIS), Vol. 39, 2 (2021).

Digital Library

[13]

Mehdi Elahi, Francesco Ricci, and Neil Rubens. 2016. A survey of active learning in collaborative filtering recommender systems. Computer Science Review, Vol. 20 (2016), 29--50.

Digital Library

[14]

Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems. 257--260.

Digital Library

[15]

Jaya Kawale, Hung H Bui, Branislav Kveton, Long Tran-Thanh, and Sanjay Chawla. 2015. Efficient thompson sampling for online matrix-factorization recommendation. In Advances in neural information processing systems .

[16]

Yehuda Koren and Robert Bell. 2015. Advances in collaborative filtering. Recommender systems handbook (2015), 77--118.

[17]

Volodymyr Kuleshov and Doina Precup. 2014. Algorithms for multi-armed bandit problems. arXiv preprint arXiv:1402.6028 (2014).

[18]

Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of the 19th international conference on World wide web. 661--670.

Digital Library

[19]

Shuai Li, Alexandros Karatzoglou, and Claudio Gentile. 2016. Collaborative filtering bandits. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval . 539--548.

Digital Library

[20]

Steffen Rendle, Walid Krichene, Li Zhang, and John Anderson. 2020. Neural collaborative filtering vs. matrix factorization revisited. In Fourteenth ACM Conference on Recommender Systems. 240--248.

Digital Library

[21]

Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to recommender systems handbook. In Recommender systems handbook . Springer, 1--35.

[22]

Yuta Saito, Aihara Shunsuke, Matsutani Megumi, and Narita Yusuke. 2020. Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation. arXiv preprint arXiv:2008.07146 (2020).

[23]

Aghiles Salah, Quoc-Tuan Truong, and Hady W Lauw. 2020. Cornac: A Comparative Framework for Multimodal Recommender Systems. J. Mach. Learn. Res., Vol. 21 (2020), 95--1.

[24]

Javier Sanz-Cruzado, Pablo Castells, and Esther López. 2019. A simple multi-armed nearest-neighbor bandit for interactive recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems. 358--362.

Digital Library

[25]

Gunnar Schröder, Maik Thiele, and Wolfgang Lehner. 2011. Setting goals and choosing metrics for recommender system evaluations. In UCERSTI2 workshop at the 5th ACM conference on recommender systems, Chicago, USA, Vol. 23. 53.

[26]

Sulthana Shams, Daron Anderson, and Douglas Leith. 2021. Cluster-Based Bandits: Fast Cold-Start for Recommender System New Users. (2021).

[27]

Nicollas Silva, Heitor Werneck, Thiago Silva, Adriano Pereira, and Leonardo Rocha. 2022. Multi-Armed Bandits in Recommendation Systems: A survey of the state-of-the-art and future directions. Expert Systems With Applications, Vol. 196, - (2022), --. https://doi.org/10.1016/j.eswa.2022.116669

Digital Library

[28]

Thiago Silveira, Min Zhang, Xiao Lin, Yiqun Liu, and Shaoping Ma. 2019. How good your recommender system is? A survey on evaluations in recommendation. International Journal of Machine Learning and Cybernetics, Vol. 10, 5 (2019), 813--831.

[29]

Emily Strong, Bernard Kleynhans, and Serdar Kadioglu. 2019. MABWiser: A Parallelizable Contextual Multi-Armed Bandit Library for Python. In 31st IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2019, Portland, OR, USA, November 4--6, 2019. IEEE, 909--914. https://doi.org/10.1109/ICTAI.2019.00129

[30]

Emily Strong, Bernard Kleynhans, and Serdar Kadioglu. 2021. MABWiser: Parallelizable Contextual Multi-armed Bandits. Int. J. Artif. Intell. Tools, Vol. 30, 4 (2021). https://doi.org/10.1142/S0218213021500214

[31]

Zhu Sun, Di Yu, Hui Fang, Jie Yang, Xinghua Qu, Jie Zhang, and Cong Geng. 2020. Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison. In Fourteenth ACM conference on recommender systems . 23--32.

Digital Library

[32]

Liang Tang, Yexi Jiang, Lei Li, and Tao Li. 2014. Ensemble contextual bandits for personalized recommendation. In Proceedings of the 8th ACM Conference on Recommender Systems. 73--80.

Digital Library

[33]

Saúl Vargas and Pablo Castells. 2011. Rank and relevance in novelty and diversity metrics for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems. 109--116.

Digital Library

[34]

Huazheng Wang, Qingyun Wu, and Hongning Wang. 2017. Factorization bandits for interactive recommendation. In Thirty-First AAAI Conference on Artificial Intelligence .

Digital Library

[35]

Qing Wang, Chunqiu Zeng, Wubai Zhou, Tao Li, S Sitharama Iyengar, Larisa Shwartz, and Genady Ya Grabarnik. 2018. Online interactive collaborative filtering using multi-armed bandit with dependent arms. IEEE Transactions on Knowledge and Data Engineering, Vol. 31, 8 (2018), 1569--1580.

Digital Library

[36]

Yu-Xiong Wang and Martial Hebert. 2016. Learning to learn: Model regression networks for easy small sample learning. In European Conference on Computer Vision. Springer, 616--634.

[37]

Robert F Woolson. 2007. Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials (2007), 1--3.

[38]

Qingyun Wu, Naveen Iyer, and Hongning Wang. 2018. Learning contextual bandits in a non-stationary environment. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval . 495--504.

Digital Library

[39]

Qingyun Wu, Huazheng Wang, Quanquan Gu, and Hongning Wang. 2016b. Contextual bandits in a collaborative environment. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval .

Digital Library

[40]

Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Peng He, Paul Weng, Han Gao, and Guihai Chen. 2019. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In The World Wide Web Conference . 2091--2102. https://dl.acm.org/doi/abs/10.1145/3308558.3313442

Digital Library

[41]

Yao Wu, Christopher DuBois, Alice X Zheng, and Martin Ester. 2016a. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the ninth ACM international conference on web search and data mining . 153--162.

Digital Library

[42]

ChengXiang Zhai, William W Cohen, and John Lafferty. 2015. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval. In ACM SIGIR Forum, Vol. 49. ACM New York, NY, USA, 2--9.

Digital Library

[43]

Wayne Xin Zhao, Shanlei Mu, Yupeng Hou, Zihan Lin, Yushuo Chen, Xingyu Pan, Kaiyuan Li, Yujie Lu, Hui Wang, Changxin Tian, et almbox. 2021. Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management . 4653--4664.

Digital Library

[44]

Xiaoxue Zhao, Weinan Zhang, and Jun Wang. 2013. Interactive collaborative filtering. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management . 1411--1420.

Digital Library

[45]

Guorui Zhou, Xiaoqiang Zhu, Chenru Song, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, and Kun Gai. 2018. Deep interest network for click-through rate prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 1059--1068.

Digital Library

[46]

Sijin Zhou, Xinyi Dai, Haokun Chen, Weinan Zhang, Kan Ren, Ruiming Tang, Xiuqiang He, and Yong Yu. 2020. Interactive recommender system via knowledge graph-enhanced reinforcement learning. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval .

Digital Library

[47]

Lixin Zou, Long Xia, Yulong Gu, Xiangyu Zhao, Weidong Liu, Jimmy Xiangji Huang, and Dawei Yin. 2020. Neural interactive collaborative filtering. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval . 749--758.

Digital Library

Cited By

Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1145/3687470
Bauer CZangerle ESaid A(2024)Exploring the Landscape of Recommender Systems Evaluation: Practices and PerspectivesACM Transactions on Recommender Systems10.1145/36291702:1(1-31)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3629170
Yu YGao CChen JTang HSun YChen QMa WZhang MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657868(977-987)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657868
Show More Cited By

Index Terms

iRec: An Interactive Recommendation Framework
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

Recommendations

Using a trust network to improve top-N recommendation
RecSys '09: Proceedings of the third ACM conference on Recommender systems

Top-N item recommendation is one of the important tasks of recommenders. Collaborative filtering is the most popular approach to building recommender systems which can predict ratings for a given user and item. Collaborative filtering can be extended ...
Typicality-Based Collaborative Filtering Recommendation

Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...
Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenario
WebMedia '22: Proceedings of the Brazilian Symposium on Multimedia and the Web

Nowadays, instead of the traditional batch paradigm where the system trains and predicts a model at scheduled times, new Recommender Systems (RSs) have become interactive models. In this case, the RS should continually recommend the most relevant item(...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

CNPq
CAPES
Fapemig

Conference

SIGIR '22

Sponsor:

SIGIR

SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
352
Total Downloads

Downloads (Last 12 months)78
Downloads (Last 6 weeks)7

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 27-Aug-2024
https://dl.acm.org/doi/10.1145/3687470
Bauer CZangerle ESaid A(2024)Exploring the Landscape of Recommender Systems Evaluation: Practices and PerspectivesACM Transactions on Recommender Systems10.1145/36291702:1(1-31)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3629170
Yu YGao CChen JTang HSun YChen QMa WZhang MHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657868(977-987)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657868
Zhang MQu YLi YWen XZhou Y(2024)RLISR: A Deep Reinforcement Learning Based Interactive Service Recommendation ModelIEEE Access10.1109/ACCESS.2024.342039512(90204-90217)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3420395
Andrade YSilva NSilva TPereira ADias DAlbergaria ERocha L(2023)A Complete Framework for Offline and Counterfactual Evaluations of Interactive Recommendation SystemsProceedings of the 29th Brazilian Symposium on Multimedia and the Web10.1145/3617023.3617049(193-197)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617023.3617049
Silva NSilva TWerneck HRocha LPereira A(2023)User Cold-start Problem in Multi-armed Bandits: When the First Recommendations Guide the User’s ExperienceACM Transactions on Recommender Systems10.1145/35548191:1(1-24)Online publication date: 27-Jan-2023
https://dl.acm.org/doi/10.1145/3554819
Silva NSilva THott HRibeiro YPereira ARocha LChen HDuh WHuang HKato MMothe JPoblete B(2023)Exploring Scenarios of Uncertainty about the Users' Preferences in Interactive Recommendation SystemsProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591684(1178-1187)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591684
Andrade YSilva NPereira ATuler EVieira CGuimarães MDias DRocha L(2023)Integrating Counterfactual Evaluations into Traditional Interactive Recommendation FrameworksComputational Science and Its Applications – ICCSA 202310.1007/978-3-031-36805-9_41(635-647)Online publication date: 3-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-36805-9_41
Silva TSilva NMito CPereira ARocha L(2022)Interactive POI Recommendation: applying a Multi-Armed Bandit framework to characterise and create new models for this scenarioProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3539637.3557060(211-221)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3539637.3557060

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