- Version 0.9.36
- By Outpace Systems
A recommender model that learns a matrix factorization embedding based off minimizing the pairwise ranking loss described in the paper.
Algorithm - Fulfilled on Amazon SageMaker
Ranking (8 results) showing 1 - 8
- Version 1.0.0
- By ClosedLoop.ai
The CV19 Index (http://cv19index.com) is an open source, AI-based predictive model that identifies people likely to have heightened vulnerability to complications from COVID-19. The index is intended to help hospitals and government agencies respond to COVID-19. By targeting their outreach...
Model Package - Fulfilled on Amazon SageMaker
- Version 3.4
- By Mphasis
Key phrase extractor uses end-to-end text extraction pipeline, text analysis and natural language processing techniques to automate key phrases/words extraction from text documents. This solution is based on unsupervised graph-based, topic-based, statistics-based algorithms for the construction of...
Model Package - Fulfilled on Amazon SageMaker
- Version 0.8
- By Outpace Systems
A recommendation model using an alternating least squares factorization approach for implicit datasets.
Algorithm - Fulfilled on Amazon SageMaker
- Version 0.9
- By Outpace Systems
An explicit feedback matrix factorization model. Uses a classic matrix factorization approach, with latent vectors used to represent both users and items. Their dot product gives the predicted score for a user-item pair.
Algorithm - Fulfilled on Amazon SageMaker
- Version 0.9
- By Outpace Systems
A recommender model that learns a matrix factorization embedding based off minimizing the pairwise ranking loss described in the paper.
Algorithm - Fulfilled on Amazon SageMaker
- Version 0.9
- By Outpace Systems
Models for recommending items given a sequence of previous implicit user/item interactions. Solves user cold start problem.
Algorithm - Fulfilled on Amazon SageMaker
- Version 0.9
- By Outpace Systems
An implicit feedback matrix factorization model. Uses a classic matrix factorization approach, with latent vectors used to represent both users and items. Their dot product gives the predicted score for a user-item pair. The model is trained through negative sampling: for any known user-item pair,...
Algorithm - Fulfilled on Amazon SageMaker
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