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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.

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Cited By

Akhadam AKbibchi OMekouar LIraqi Y(2025)A Comparative Evaluation of Recommender Systems ToolsIEEE Access10.1109/ACCESS.2025.354101413(29493-29522)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3541014
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
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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.

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Association for Computing Machinery

New York, NY, United States

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Published: 02 February 2018

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

February 5 - 9, 2018

CA, Marina Del Rey, USA

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

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

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Cited By

Akhadam AKbibchi OMekouar LIraqi Y(2025)A Comparative Evaluation of Recommender Systems ToolsIEEE Access10.1109/ACCESS.2025.354101413(29493-29522)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3541014
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
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