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- Description:
Alpenglow is an open-source recommender systems research framework, aimed at providing tools for rapid prototyping and evaluation of algorithms for time-aware and streaming recommendation tasks. It supports modeling non-stationary environments using prequential evaluation and incremental updating of models. The framework is implemented in C++, and also provides an easy-to-use python API.
Features:
- various tools for evaluation
- preconfigured experiments
- option for embedding traditional periodic retraining in the prequential framework
Implemented models:
- matrix factorization (SGD, ALS, iALS)
- asymmetric matrix factorization
- SVD++
- factorization machines
- nearest neighbor
- time-aware popularity
- transition probability
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Supported Operating Systems: Linux, Windows, Osx
- Data Formats: Pandas
- Tags: Recommender System, Online Leaning
- Archive: download here
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