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Conference proceeding
LensKit for Python Next-Generation Software for Recommender Systems Experiments
CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, pp 2999-3006
01 Jan 2020
Abstract
LensKit is an open-source toolkit for building, researching, and learning about recommender systems. First released in 2010 as a Java framework, it has supported diverse published research, small-scale production deployments, and education in both MOOC and traditional classroom settings. In this paper, I present the next generation of the LensKit project, re-envisioning the original tool's objectives as flexible Python package for supporting recommender systems research and development. LensKit for Python (LKPY) enables researchers and students to build robust, flexible, and reproducible experiments that make use of the large and growing PyData and Scientific Python ecosystem, including scikit-learn, and TensorFlow. To that end, it provides classical collaborative filtering implementations, recommender system evaluation metrics, data preparation routines, and tools for efficiently batch running recommendation algorithms, all usable in any combination with each other or with other Python software.
This paper describes the design goals, use cases, and capabilities of LKPY, contextualized in a reflection on the successes and failures of the original LensKit for Java software.
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Details
- Title
- LensKit for Python Next-Generation Software for Recommender Systems Experiments
- Creators
- Michael D. Ekstrand - Boise State UniversityASSOC COMP MACHINERY
- Publication Details
- CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, pp 2999-3006
- Publisher
- Assoc Computing Machinery
- Number of pages
- 8
- Grant note
- IIS 17-51278 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000749561302166
- Scopus ID
- 2-s2.0-85095863738
- Other Identifier
- 991021818386904721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Information Systems
- Computer Science, Theory & Methods