poster

MyMediaLite: a free recommender system library

Authors:

Steffen Rendle,

Christoph Freudenthaler,

Lars Schmidt-ThiemeAuthors Info & Claims

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

Pages 305 - 308

https://doi.org/10.1145/2043932.2043989

Published: 23 October 2011 Publication History

Abstract

MyMediaLite is a fast and scalable, multi-purpose library of recommender system algorithms, aimed both at recommender system researchers and practitioners. It addresses two common scenarios in collaborative filtering: rating prediction (e.g. on a scale of 1 to 5 stars) and item prediction from positive-only implicit feedback (e.g. from clicks or purchase actions). The library offers state-of-the-art algorithms for those two tasks. Programs that expose most of the library's functionality, plus a GUI demo, are included in the package. Efficient data structures and a common API are used by the implemented algorithms, and may be used to implement further algorithms. The API also contains methods for real-time updates and loading/storing of already trained recommender models.

MyMediaLite is free/open source software, distributed under the terms of the GNU General Public License (GPL). Its methods have been used in four different industrial field trials of the MyMedia project, including one trial involving over 50,000 households.

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Cited By

Muttillo VDi Sipio CRubei RBerardinelli LDehghani MFilkov VRay BZhou M(2024)Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning ApproachProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695058(619-630)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695058
Vente TWegmeth LSaid ABeel J(2024)From Clicks to Carbon: The Environmental Toll of Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688074(580-590)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688074
Milogradskii ALashinin OP AAnanyeva MKolesnikov S(2024)Revisiting BPR: A Replicability Study of a Common Recommender System BaselineProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688073(267-277)Online publication date: 8-Oct-2024
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Index Terms

MyMediaLite: a free recommender system library
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information & Contributors

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Published In

cover image ACM Conferences

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

Copyright © 2011 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]

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Publication History

Published: 23 October 2011

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RecSys '11

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SIGCHI

RecSys '11: Fifth ACM Conference on Recommender Systems

October 23 - 27, 2011

Illinois, Chicago, USA

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

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Cited By

Muttillo VDi Sipio CRubei RBerardinelli LDehghani MFilkov VRay BZhou M(2024)Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning ApproachProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695058(619-630)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695058
Vente TWegmeth LSaid ABeel J(2024)From Clicks to Carbon: The Environmental Toll of Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688074(580-590)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688074
Milogradskii ALashinin OP AAnanyeva MKolesnikov S(2024)Revisiting BPR: A Replicability Study of a Common Recommender System BaselineProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688073(267-277)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688073
Spillo GMusto Cde Gemmis MLops PSemeraro G(2024)Recommender systems based on neuro-symbolic knowledge graph embeddings encoding first-order logic rulesUser Modeling and User-Adapted Interaction10.1007/s11257-024-09417-xOnline publication date: 26-Sep-2024
https://doi.org/10.1007/s11257-024-09417-x
Bellini VDi Sciascio EDonini FPomo CRagone ASchiavone A(2024)A qualitative analysis of knowledge graphs in recommendation scenarios through semantics-aware autoencodersJournal of Intelligent Information Systems10.1007/s10844-023-00830-z62:3(787-807)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10844-023-00830-z
Sanner SBalog KRadlinski FWedin BDixon L(2023)Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based PreferencesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608845(890-896)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608845
Spillo GMusto CPolignano MLops Pde Gemmis MSemeraro G(2023)Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware RecommendationsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592965(1-12)Online publication date: 18-Jun-2023
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Lian DHuang XChen XChen JWang XWang YJin HFan RLiu ZWu LChen EChen HDuh WHuang HKato MMothe JPoblete B(2023)RecStudio: Towards a Highly-Modularized Recommender SystemProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591894(2890-2900)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591894
Le NAbel MGouspillou P(2023)Combining Embedding-Based and Semantic-Based Models for Post-Hoc Explanations in Recommender Systems2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC53992.2023.10394410(4619-4624)Online publication date: 1-Oct-2023
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Bae HLee YHan KKim S(2023)A Competition-Aware Approach to Accurate TV Show Recommendation2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00216(2822-2834)Online publication date: Apr-2023
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