Best Practices on Recommendation Systems
azure
microsoft
machine-learning
python
deep-learning
kubernetes
data-science
artificial-intelligence
jupyter-notebook
tutorial
operationalization
ranking
rating
recommendation
recommendation-algorithm
recommendation-engine
recommendation-system
recommender
Обновлено 2024-11-01 16:20:38 +03:00
A fast numpy-based implementation of ranking metrics for information retrieval and recommendation.
Обновлено 2022-08-22 22:05:09 +03:00
Implementation of "Debiasing Item-to-Item Recommendations With Small Annotated Datasets" (RecSys '20)
Обновлено 2020-10-13 21:31:30 +03:00
Peregrine is a workload optimization platform for cloud query engines. The goal of Peregrine is three-fold: 1. make it easier to ingest and analyze query workload telemetry into a common engine-agnostic representation, 2. help developers to quickly build workload optimization applications to reduce overall costs and improve operational efficiency, and 3. providing better experience to the customers in the form of workload insights, actionable recommendations, and self-tuning capabilities.
Обновлено 2020-08-31 07:53:14 +03:00
Recommendation server for Universal Search.
Обновлено 2016-11-30 20:00:05 +03:00