AutoX_Recommend, 负样本降采样
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AutoX_Recommend
call-for-contributions
- Dominant language
- Jupyter Notebook
- Stars
- 551
- Forks
- 143
- PR merge metrics
- No merged PRs in 30d
Description
This issue has no description.
Contributor guide
No contributing guide indexed for this repository
Research direction
The issue title suggests implementing negative sample downsampling for a recommendation system. Start by examining the AutoX codebase for recommendation modules or data preprocessing utilities. Look for existing sampling strategies or model training pipelines to understand the current data flow. Determine how negative samples are currently handled and where downsampling logic should be integrated.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning, python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100