More Web Proxy on the site http://driver.im/

research-article

Open access

OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms

Authors:

Eugene Bagdasaryan,

Joshua Gruenstein,

Cheng-Kang Hsieh,

Deborah EstrinAuthors Info & Claims

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

Pages 664 - 672

https://doi.org/10.1145/3159652.3159681

Published: 02 February 2018 Publication History

Abstract

With the increasing demand for deeper understanding of users» preferences, recommender systems have gone beyond simple user-item filtering and are increasingly sophisticated, comprised of multiple components for analyzing and fusing diverse information. Unfortunately, existing frameworks do not adequately support extensibility and adaptability and consequently pose significant challenges to rapid, iterative, and systematic, experimentation. In this work, we propose OpenRec, an open and modular Python framework that supports extensible and adaptable research in recommender systems. Each recommender is modeled as a computational graph that consists of a structured ensemble of reusable modules connected through a set of well-defined interfaces. We present the architecture of OpenRec and demonstrate that OpenRec provides adaptability, modularity and reusability while maintaining training efficiency and recommendation accuracy. Our case study illustrates how OpenRec can support an efficient design process to prototype and benchmark alternative approaches with inter-changeable modules and enable development and evaluation of new algorithms.

References

[1]

Mart´?n Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, and others. 2016. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467 (2016).

[2]

Linas Baltrunas, Bernd Ludwig, and Francesco Ricci. 2011. Matrix factorization techniques for context aware recommendation. In Proceedings of the fifth ACM conference on Recommender systems. ACM, 301--304.

Digital Library

[3]

James Bennett, Stan Lanning, and others. 2007. The netflix prize. In Proceedings of KDD cup and workshop, Vol. 2007. New York, NY, USA, 35.

[4]

Lee Breslau, Deborah Estrin, Kevin Fall, Sally Floyd, John Heidemann, Ahmed Helmy, Polly Huang, Steven McCanne, Kannan Varadhan, Ya Xu, and others. 2000. Advances in network simulation. Computer 33, 5 (2000), 59--67.

Digital Library

[5]

Denny Britz, Anna Goldie, Thang Luong, and quoc Le. 2017. Massive Exploration of Neural Machine Translation Architectures. ArXiv e-prints (March 2017). arXiv:cs.CL/1703.03906

[6]

Simon Chan, Thomas Stone, Kit Pang Szeto, and Ka Hou Chan. 2013. PredictionIO: a distributed machine learning server for practical software development. In Proceedings of the 22nd ACM international conference on Information & Knowledge Management. ACM, 2493--2496.

Digital Library

[7]

Franc¸ois Chollet and others. 2015. Keras. https://github.com/fchollet/keras. (2015).

[8]

Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems. ACM, 191--198.

Digital Library

[9]

Michael D Ekstrand, Michael Ludwig, Joseph A Konstan, and John T Riedl. 2011. Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit. In Proceedings of the fifth ACM conference on Recommender systems. ACM, 133--140.

Digital Library

[10]

Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-thieme. 2011. MyMediaLite: A free recommender system library. In Proceedings of the fifth ACM conference on Recommender systems. ACM, 305--308.

Digital Library

[11]

Guibing Guo, Jie Zhang, Zhu Sun, and Neil Yorke-Smith. 2015. LibRec: A Java Library for Recommender Systems. In UMAP Workshops.

[12]

Ido Guy, Naama Zwerdling, Inbal Ronen, David Carmel, and Erel Uziel. 2010. Social media recommendation based on people and tags. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. ACM, 194--201.

Digital Library

[13]

Ruining He and Julian McAuley. 2015. VBPR: visual bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1510.01784 (2015).

Digital Library

[14]

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. International World Wide Web Conferences Steering Committee.

Digital Library

[15]

Cheng-Kang Hsieh, Longqi Yang, Yin Cui, Tsung-Yi Lin, Serge Belongie, and Deborah Estrin. 2017. Collaborative Metric Learning. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee.

Digital Library

[16]

Cheng-Kang Hsieh, Longqi Yang, Honghao Wei, Mor Naaman, and Deborah Estrin. 2016. Immersive Recommendation: News and Event Recommendations Using Personal Digital Traces. In Proceedings of the 25th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 51--62.

Digital Library

[17]

Yifan Hu, Yehuda Koren, and Chris Volinsky. 2008. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. Ieee, 263--272.

Digital Library

[18]

Nicolas Hug. 2017. Surprise, a Python library for recommender systems. http://surpriselib.com. (2017).

[19]

Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor Darrell. 2014. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia. ACM, 675--678.

Digital Library

[20]

Yehuda Koren. 2010. Collaborative filtering with temporal dynamics. Commun. ACM 53, 4 (2010), 89--97.

Digital Library

[21]

Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7, 1 (2003), 76--80.

Digital Library

[22]

Julian McAuley, Rahul Pandey, and Jure Leskovec. 2015. Inferring networks of substitutable and complementary products. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 785--794.

Digital Library

[23]

Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 43--52.

Digital Library

[24]

Slav Petrov and others. 2016. SyntaxNet. https://github.com/tensorflow/models/ tree/master/syntaxnet. (2016).

[25]

Istvan Pil ´ aszy and Domonkos Tikk. 2009. Recommending new movies: even a ´ few ratings are more valuable than metadata. In Proceedings of the third ACM conference on Recommender systems. ACM, 93--100.

Digital Library

[26]

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. AUAI Press, 452--461.

Digital Library

[27]

Ruslan Salakhutdinov and Andriy Mnih. 2007. Probabilistic Matrix Factorization. In NIPS, Vol. 1. 2--1.

Digital Library

[28]

Yue Shi, Martha Larson, and Alan Hanjalic. 2014. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR) 47, 1 (2014), 3.

Digital Library

[29]

Apache Spark. 2017. MLlib. https://spark.apache.org/mllib/. (2017).

[30]

Aaron Van den Oord, Sander Dieleman, and Benjamin Schrauwen. 2013. Deep content-based music recommendation. In Advances in neural information processing systems. 2643--2651.

Digital Library

[31]

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. ACM, 1235--1244.

Digital Library

[32]

Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J Smola, and How Jing. 2017. Recurrent recommender networks. In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. ACM, 495--503.

Digital Library

[33]

Longqi Yang, Chen Fang, Hailin Jin, Matt Hoffman, and Deborah Estrin. 2017. Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces. In Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee.

Digital Library

[34]

Longqi Yang, Cheng-Kang Hsieh, Hongjian Yang, John P Pollak, Nicola Dell, Serge Belongie, Curtis Cole, and Deborah Estrin. 2017. Yum-me: a personalized nutrient-based meal recommender system. ACM Transactions on Information Systems (TOIS) 36, 1 (2017), 7.

Digital Library

Cited By

Li DJin RLiu ZRen BGao JLiu Z(2023)On Item-Sampling Evaluation for Recommender SystemACM Transactions on Recommender Systems10.1145/36291712:1(1-36)Online publication date: 27-Oct-2023
https://dl.acm.org/doi/10.1145/3629171
Paun IMoshfeghi YNtarmos N(2023)White Box: On the Prediction of Collaborative Filtering Recommendation Systems’ PerformanceACM Transactions on Internet Technology10.1145/355497923:1(1-29)Online publication date: 23-Feb-2023
https://dl.acm.org/doi/10.1145/3554979
Lops PPolignano MMusto CSilletti ASemeraro G(2023) ClayRSInformation Systems10.1016/j.is.2023.102273119:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102273
Show More Cited By

Index Terms

OpenRec: A Modular Framework for Extensible and Adaptable Recommendation Algorithms
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

Recommendations

Extensible middleware framework for multimodal interfaces in distributed environments
ICMI '07: Proceedings of the 9th international conference on Multimodal interfaces

We present a framework to manage multimodal applications and interfaces in a reusable and extensible manner. We achieve this by focusing the architecture both on applications' needs and devices' capabilities. One particular domain we want to approach is ...
Towards a dynamic and adaptable application server
Internetware '09: Proceedings of the First Asia-Pacific Symposium on Internetware

Internetware is proposed as a new software paradigm to cope with the open, dynamic and ever-changing Internet environment for applications. The incarnated characteristics of Internetware promote the operating platform to be more dynamic and adaptable. ...
Hydra: an open framework for virtual-fusion of recommendation filters
RecSys '10: Proceedings of the fourth ACM conference on Recommender systems

Today's web commercial applications demand more powerful recommendation systems due to the rapid increase in the number of both consumers and available products. Searching for the best algorithm with the highest accuracy and realistic complexity is, ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

WSDM '18: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining

February 2018

821 pages

ISBN:9781450355810

DOI:10.1145/3159652

General Chairs:
Yi Chang
Jilin University, Huawei Inc.
,
Chengxiang Zhai
University of Illinois Urbana-Champaign
,
Program Chairs:
Yan Liu
University of Southern California
,
Yoelle Maarek
Amazon

Copyright © 2018 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 February 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Conference

WSDM 2018

Sponsor:

WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining

February 5 - 9, 2018

CA, Marina Del Rey, USA

Acceptance Rates

WSDM '18 Paper Acceptance Rate 81 of 514 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

37
Total Citations
View Citations
1,255
Total Downloads

Downloads (Last 12 months)125
Downloads (Last 6 weeks)36

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li DJin RLiu ZRen BGao JLiu Z(2023)On Item-Sampling Evaluation for Recommender SystemACM Transactions on Recommender Systems10.1145/36291712:1(1-36)Online publication date: 27-Oct-2023
https://dl.acm.org/doi/10.1145/3629171
Paun IMoshfeghi YNtarmos N(2023)White Box: On the Prediction of Collaborative Filtering Recommendation Systems’ PerformanceACM Transactions on Internet Technology10.1145/355497923:1(1-29)Online publication date: 23-Feb-2023
https://dl.acm.org/doi/10.1145/3554979
Lops PPolignano MMusto CSilletti ASemeraro G(2023) ClayRSInformation Systems10.1016/j.is.2023.102273119:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102273
Krichene WRendle S(2022)On sampled metrics for item recommendationCommunications of the ACM10.1145/353533565:7(75-83)Online publication date: 21-Jun-2022
https://dl.acm.org/doi/10.1145/3535335
Sanz-Cruzado JCastells PAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)RELISON: A Framework for Link Recommendation in Social NetworksProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531730(2992-3002)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531730
Jiang ZZheng ZChen KLuo XTang XLi Y(2022)Exploring Smart Contract Recommendation: Towards Efficient Blockchain DevelopmentIEEE Transactions on Services Computing10.1109/TSC.2022.3202081(1-12)Online publication date: 2022
https://doi.org/10.1109/TSC.2022.3202081
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
Naghiaei MRahmani HDeldjoo Y(2022)PyCPFair: A framework for consumer and producer fairness in recommender systemsSoftware Impacts10.1016/j.simpa.2022.10038213(100382)Online publication date: Aug-2022
https://doi.org/10.1016/j.simpa.2022.100382
Rahmani HDeldjoo Ydi Noia T(2022)The role of context fusion on accuracy, beyond-accuracy, and fairness of point-of-interest recommendation systemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117700205:COnline publication date: 22-Jun-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117700
Tao HNiu XFu LYuan SWang XZhang JHu Y(2022)DeepRS: A Library of Recommendation Algorithms Based on Deep LearningInternational Journal of Computational Intelligence Systems10.1007/s44196-022-00102-815:1Online publication date: 14-Jul-2022
https://doi.org/10.1007/s44196-022-00102-8
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

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

Media

Figures

Other

Tables

View Table of Contents