skip to main content
10.1145/3298689.3347039acmotherconferencesArticle/Chapter ViewAbstractPublication PagesrecsysConference Proceedingsconference-collections
research-article

A recommender system for heterogeneous and time sensitive environment

Published: 10 September 2019 Publication History

Abstract

The digital game industry has recently adopted recommender systems to deliver the most relevant content and suggest the most suitable activities to players. Because of diverse game designs and dynamic experiences, recommender systems typically operate in highly heterogeneous and time-sensitive environments. In this paper, we describe a recommender system at a digital game company which aims to provide recommendations for a large variety of use-cases while being easy to integrate and operate. The system leverages a unified data platform, standardized context and tracking data pipelines, robust naive linear contextual multi-armed bandit algorithms, and experimentation platform for extensibility as well as flexibility. Several games and applications have successfully launched with the recommender system and have achieved significant improvements.

References

[1]
Ernest Adams. 2010. Fundamentals of Game Design. Pearson Education. https://books.google.com/books?id=-BCrex2U1XMC
[2]
Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17, 6 (2005), 734--749. arXiv:3
[3]
Charu C. Aggarwal. 2016. Recommender Systems. Springer International Publishing, Cham. 29--71 pages. arXiv:arXiv:1202.1112v1
[4]
Shipra Agrawal and Navin Goyal. 2012. Analysis of Thompson Sampling for the multi-armed bandit problem. JMLR Workshop and Conference Proceedings (COLT2012) 23 (2012), 39.1-39.26. https://doi.org/arXiv:1111.1797 arXiv:1111.1797
[5]
Shipra Agrawal and Navin Goyal. 2013. Thompson Sampling for Contextual Bandits with Linear Payoffs. In Proceedings of the 30 th International Conference on Machine Learning, Vol. 28. JMLR, Atlanta. arXiv:1209.3352 http://arxiv.org/abs/1209.3352
[6]
Aliyun. 2017. Aliyun Recommendation Engine. https://data.aliyun.com/product/re
[7]
Xavier Amatriain. 2012. Building Industrial-scale Real-world Recommender Systems. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys'12. ACM, New York, New York, USA, 7--8. https://xamat.github.io/pubs/recsys12-tutorial.pdf
[8]
Amazon. 2019. Amazon Personalize - Real-time personalization and recommendation, based on the same technology used at Amazon.com.
[9]
Syed Muhammad Anwar, Talha Shahzad, Zunaira Sattar, Rahma Khan, and Muhammad Majid. 2017. A game recommender system using collaborative filtering (GAMBIT). In 2017 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 328--332.
[10]
Peter Auer. 2002. Using Confidence Bounds for Exploitation-Exploration Trade-offs. Journal of Machine Learning Research 3 (2002), 397--422.
[11]
Robert M. Bell and Yehuda Koren. 2007. Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. In Seventh IEEE International Conference on Data Mining (ICDM 2007), Vol. 58. IEEE, 43--52.
[12]
Olivier Chapelle and Lihong Li. 2011. An Empirical Evaluation of Thompson Sampling. In Advances in Neural Information Processing Systems 24. Shawe-Taylor Weinberger, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. (Eds.). Curran Associates, Inc., 2249--2257.
[13]
Heng-tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, and Hemal Shah. 2016. Wide & Deep Learning for Recommender Systems. In Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. Cheng2016, 7--10. https://doi.org/2988450.2988454
[14]
Anthony Chow, Min Hui Nicole, and Giuseppe Manai. 2014. HybridRank : A hybrid content-based approach to mobile game recommendations. CEUR Workshop Proceedings 1245 (2014), 10--12.
[15]
Evangelia Christakopoulou and George Karypis. 2016. Local Item-Item Models For Top-N Recommendation. In Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16. ACM, New York, NY, USA, 67--74.
[16]
Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16, ACM (Ed.). 191--198.
[17]
James Davidson, Blake Livingston, Dasarathi Sampath, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, and Mike Lambert. 2010. The YouTube video recommendation system. Proceedings of the fourth ACM conference on Recommender systems - RecSys '10 (2010), 293.
[18]
Anders Drachen, James Green, Chester Gray, Elie Harik, Patty Lu, Rafet Sifa, and Diego Klabjan. 2016. Guns and guardians: Comparative cluster analysis and behavioral profiling in destiny. In 2016 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 1--8.
[19]
Anders Drachen, Nicholas Ross, Julian Runge, and Rafet Sifa. 2016. Stylized facts for mobile game analytics. In 2016 IEEE Conference on Computational Intelligence and Games (CIG). IEEE, 1--8.
[20]
Mihajlo Grbovic and Haibin Cheng. 2018. Real-time Personalization using Embeddings for Search Ranking at Airbnb. In Proceedings of The 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Association for Computing Machinery, London, United Kingdom, 311--320.
[21]
Frédéric Guillou, Romaric Gaudel, and Philippe Preux. 2015. Collaborative Filtering as a Multi-Armed Bandit. In NIPS'15 Workshop: Machine Learning for eCommerce. Montréal.
[22]
Negar Hariri, Bamshad Mobasher, and Robin Burke. 2014. Context adaptation in interactive recommender systems. In RecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems. 41--48.
[23]
Volodymyr Kuleshov and D Precup. 2010. Algorithms for the multi-armed bandit problem. Journal of Machine Learning 1 (2010), 1--32. arXiv:arXiv:1402.6028v1
[24]
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 - WWW '10. ACM Press, New York, New York, USA, 661. arXiv:1003.0146
[25]
Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing 7, 1 (2003), 76--80. arXiv:69
[26]
Michael Meidl, Steven Lytinen, and Kevin Raison. 2014. Using Game Reviews to Recommend Games. In Proceedings of the Third Workshop on Games and NLP. 24--29.
[27]
Charles O Nutter, Thomas Enebo, Nick Sieger, Ola Bini, and Ian Dees. 2011. Using JRuby: Bringing Ruby to Java (1st ed.). Pragmatic Bookshelf.
[28]
Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to Recommender Systems Handbook. Springer US, Boston, MA, 1--35.
[29]
James Owen Ryan, Eric Kaltman, Timothy Hong, Michael Mateas, and Noah Wardrip-Fruin. 2015. People Tend to Like Related Games. Proceedings of the 10th International Conference on the Foundations of Digital Games Fdg (2015). https://games.soe.ucsc.edu/sites/default/files/ryanEtAl{_}BottomUpGameStudiesUsingNLP.pdf
[30]
Rafet Sifa, Christian Bauckhage, and Anders Drachen. 2014. Archetypal game recommender systems. CEUR Workshop Proceedings 1226, January (2014), 45--56.
[31]
Brent Smith and Greg Linden. 2017. Two Decades of Recommender Systems at Amazon.com. IEEE Internet Computing 21, 3 (may 2017), 12--18.
[32]
George Trigeorgis, Konstantinos Bousmalis, Stefanos Zafeiriou, and Bjorn W. Schuller. 2017. A Deep Matrix Factorization Method for Learning Attribute Representations. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 3 (mar 2017), 417--429. arXiv:1509.03248v1
[33]
Qing Wang, Chunqiu Zeng, Wubai Zhou, Tao Li, S. Sitharama Iyengar, Larisa Shwartz, and Genady Grabarnik. 2018. Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms. IEEE Transactions on Knowledge and Data Engineering (2018). arXiv:1708.03058
[34]
Junyuan Xie, Ross Girshick, R B G F B Com, and Ali Farhadi. 2016. Unsupervised Deep Embedding for Clustering Analysis. In Proceedings of the 33 rd International Conference on Machine Learning, Vol. 48. JMLR, New York, New York, USA. arXiv:arXiv:1511.06335v2 https://arxiv.org/pdf/1511.06335.pdf
[35]
Hong-Jian Xue, Xinyu Dai, Jianbing Zhang, Shujian Huang, and Jiajun Chen. 2017. Deep Matrix Factorization Models for Recommender Systems. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, California, 3203--3209.
[36]
Xiwang Yang, Yang Guo, Yong Liu, and Harald Steck. 2014. A survey of collaborative filtering based social recommender systems. Computer Communications 41 (2014), 1--10.
[37]
Chunqiu Zeng, Qing Wang, Shekoofeh Mokhtari, and Tao Li. 2016. Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16. ACM Press, New York, New York, USA, 2025--2034.
[38]
Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Comput. Surv. 52, 1 (feb 2019), 5:1--5:38.
[39]
Guanjie Zheng, Fuzheng Zhang, Zihan Zheng, Yang Xiang, Nicholas Jing Yuan, Xing Xie, and Zhenhui Li. 2018. DRN: A Deep Reinforcement Learning Framework for News Recommendation. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 167--176.

Cited By

View all
  • (2024)Online Nonstationary and Nonlinear Bandits with Recursive Weighted Gaussian Process2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00012(11-20)Online publication date: 2-Jul-2024
  • (2022)Positive, Negative and NeutralProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532040(1185-1195)Online publication date: 6-Jul-2022
  • (2022)Multi-Armed Bandits in Recommendation SystemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116669197:COnline publication date: 18-May-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems
September 2019
635 pages
ISBN:9781450362436
DOI:10.1145/3298689
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 September 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. digital game
  2. feature embedding
  3. multi-armed bandits
  4. recommender system

Qualifiers

  • Research-article

Conference

RecSys '19
RecSys '19: Thirteenth ACM Conference on Recommender Systems
September 16 - 20, 2019
Copenhagen, Denmark

Acceptance Rates

RecSys '19 Paper Acceptance Rate 36 of 189 submissions, 19%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)16
  • Downloads (Last 6 weeks)1
Reflects downloads up to 11 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Online Nonstationary and Nonlinear Bandits with Recursive Weighted Gaussian Process2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC61105.2024.00012(11-20)Online publication date: 2-Jul-2024
  • (2022)Positive, Negative and NeutralProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3532040(1185-1195)Online publication date: 6-Jul-2022
  • (2022)Multi-Armed Bandits in Recommendation SystemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116669197:COnline publication date: 18-May-2022
  • (2022)Considering temporal aspects in recommender systems: a surveyUser Modeling and User-Adapted Interaction10.1007/s11257-022-09335-w33:1(81-119)Online publication date: 4-Jul-2022
  • (2021)Diversity-aware Recommendations for Social Justice? Exploring User Diversity and Fairness in Recommender SystemsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3463293(404-410)Online publication date: 21-Jun-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media