demonstration

LensKit: a modular recommender framework

Authors:

Michael D. Ekstrand,

Michael Ludwig,

Jack Kolb,

John T. RiedlAuthors Info & Claims

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

Pages 349 - 350

https://doi.org/10.1145/2043932.2044001

Published: 23 October 2011 Publication History

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Abstract

LensKit is a new recommender systems toolkit aiming to be a platform for recommender research and education. It provides a common API for recommender systems, modular implementations of several collaborative filtering algorithms, and an evaluation framework for consistent, reproducible offline evaluation of recommender algorithms. In this demo, we will showcase the ease with which LensKit allows recommenders to be configured and evaluated.

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Coba LConfalonieri RZanker M(2022)RecoXplainer: A Library for Development and Offline Evaluation of Explainable Recommender SystemsIEEE Computational Intelligence Magazine10.1109/MCI.2021.312995817:1(46-58)Online publication date: Feb-2022
https://doi.org/10.1109/MCI.2021.3129958
El Handri KIdrissi A(2021)Parallelization of $Top_{k}$ Algorithm Through a New Hybrid Recommendation System for Big Data in Spark Cloud Computing FrameworkIEEE Systems Journal10.1109/JSYST.2020.301936815:4(4876-4886)Online publication date: Dec-2021
https://doi.org/10.1109/JSYST.2020.3019368
Polychronou IKatsivelis PPapakonstantinou MStoitsis GManouselis N(2020)Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate PredictionsEnvironmental Software Systems. Data Science in Action10.1007/978-3-030-39815-6_16(165-172)Online publication date: 29-Jan-2020
https://doi.org/10.1007/978-3-030-39815-6_16
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Index Terms

LensKit: a modular recommender framework
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

Information

Published In

RecSys '11: Proceedings of the fifth ACM conference on Recommender systems

October 2011

414 pages

ISBN:9781450306836

DOI:10.1145/2043932

General Chairs:
Bamshad Mobasher
DePaul University, USA
,
Robin Burke
DePaul University, USA
,
Program Chairs:
Dietmar Jannach
TU Dortmund, Germany
,
Gediminas Adomavicius
University of Minnesota, USA

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Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2011

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Author Tags

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Demonstration

Conference

RecSys '11

Sponsor:

SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Upcoming Conference

RecSys '24

Sponsor:
sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

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Citations

Cited By

View all

Coba LConfalonieri RZanker M(2022)RecoXplainer: A Library for Development and Offline Evaluation of Explainable Recommender SystemsIEEE Computational Intelligence Magazine10.1109/MCI.2021.312995817:1(46-58)Online publication date: Feb-2022
https://doi.org/10.1109/MCI.2021.3129958
El Handri KIdrissi A(2021)Parallelization of $Top_{k}$ Algorithm Through a New Hybrid Recommendation System for Big Data in Spark Cloud Computing FrameworkIEEE Systems Journal10.1109/JSYST.2020.301936815:4(4876-4886)Online publication date: Dec-2021
https://doi.org/10.1109/JSYST.2020.3019368
Polychronou IKatsivelis PPapakonstantinou MStoitsis GManouselis N(2020)Machine Learning Algorithms for Food Intelligence: Towards a Method for More Accurate PredictionsEnvironmental Software Systems. Data Science in Action10.1007/978-3-030-39815-6_16(165-172)Online publication date: 29-Jan-2020
https://doi.org/10.1007/978-3-030-39815-6_16
Schelter SCelebi UDunning TMaltzahn CMalik T(2019)Efficient Incremental Cooccurrence Analysis for Item-Based Collaborative FilteringProceedings of the 31st International Conference on Scientific and Statistical Database Management10.1145/3335783.3335784(61-72)Online publication date: 23-Jul-2019
https://dl.acm.org/doi/10.1145/3335783.3335784
Manouselis NStoitsis G(2018)Towards an e-Science Environment for Collaborative Filtering ResearchersInternational Journal of Digital Library Systems10.4018/ijdls.20140101044:1(41-72)Online publication date: 13-Dec-2018
https://dl.acm.org/doi/10.4018/ijdls.2014010104
Mansoury MBurke ROrdonez-Gauger ASepulveda XPera SEkstrand MAmatriain XO'Donovan J(2018)Automating recommender systems experimentation with librec-autoProceedings of the 12th ACM Conference on Recommender Systems10.1145/3240323.3241614(500-501)Online publication date: 27-Sep-2018
https://dl.acm.org/doi/10.1145/3240323.3241614
Coba LSymeonidis PZanker M(2018)Replicating and Improving Top-N Recommendations in Open Source PackagesProceedings of the 8th International Conference on Web Intelligence, Mining and Semantics10.1145/3227609.3227671(1-7)Online publication date: 25-Jun-2018
https://dl.acm.org/doi/10.1145/3227609.3227671
Erdt MFernandez ARensing C(2018)Evaluating Recommender Systems for Technology Enhanced LearningIEEE Transactions on Learning Technologies10.1109/TLT.2015.24388678:4(326-344)Online publication date: 12-Dec-2018
https://dl.acm.org/doi/10.1109/TLT.2015.2438867
Beel JBreitinger CLanger SLommatzsch AGipp B(2018)Towards reproducibility in recommender-systems researchUser Modeling and User-Adapted Interaction10.1007/s11257-016-9174-x26:1(69-101)Online publication date: 26-Dec-2018
https://dl.acm.org/doi/10.1007/s11257-016-9174-x
Pessemier TMartens L(2018)Heart rate monitoring, activity recognition, and recommendation for e-coachingMultimedia Tools and Applications10.1007/s11042-018-5640-277:18(23317-23334)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s11042-018-5640-2
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