https://github.com/takuti/stream-recommender
Experiments of item recommendation in a streaming environment
https://github.com/takuti/stream-recommender
factorization machine-learning python recommender-system research
Last synced: 11 months ago
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Experiments of item recommendation in a streaming environment
- Host: GitHub
- URL: https://github.com/takuti/stream-recommender
- Owner: takuti
- Created: 2015-12-26T07:45:01.000Z (about 10 years ago)
- Default Branch: master
- Last Pushed: 2018-02-22T19:45:52.000Z (about 8 years ago)
- Last Synced: 2025-04-15T04:17:33.306Z (11 months ago)
- Topics: factorization, machine-learning, python, recommender-system, research
- Language: Jupyter Notebook
- Homepage:
- Size: 6.71 MB
- Stars: 15
- Watchers: 4
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
Item Recommendation in a Streaming Environment
===
This repository discloses implementation used in the following research papers:
- T. Kitazawa. **[Incremental Factorization Machines for Persistently Cold-Starting Online Item Recommendation](https://arxiv.org/abs/1607.02858)**. arXiv:1607.02858 [cs.LG], July 2016.
- T. Kitazawa. **[Sketching Dynamic User-Item Interactions for Online Item Recommendation](http://dl.acm.org/citation.cfm?id=3022152)**. In *Proc. of CHIIR 2017*, March 2017.
Recommendation algorithms are implemented in [FluRS](https://github.com/takuti/flurs/tree/0.0.1), a Python library for online item recommendation tasks.
## Usage
$ python experiment.py -f path/to/config/file.ini
Examples of config files are available at: [config/](config/)
The results will be written text files under *results/*.