Tags / Machine learning /
Recommender system
A library or system that recommends items to users from behaviour or content data.
Repositories
10
Net stars gained, last 7 days
+233
Top-level Tag
Net stars gained each day, last 90 days
Peak days
- 1
Aug 13, 2026 +1,126 net stars (median day +34)
- xai-org/x-algorithm+1,094 (97%)
- twitter/the-algorithm+29 (3%)
- NicolasHug/Surprise+2 (0%)
- 2
Aug 14, 2026 +1,581 net stars (median day +34)
- xai-org/x-algorithm+1,530 (97%)
- twitter/the-algorithm+46 (3%)
- twitter/the-algorithm-ml+4 (0%)
- 3
Aug 15, 2026 +1,499 net stars (median day +34)
- xai-org/x-algorithm+1,465 (98%)
- twitter/the-algorithm+31 (2%)
- twitter/the-algorithm-ml+2 (0%)
- 4
Aug 16, 2026 +515 net stars (median day +34)
- xai-org/x-algorithm+495 (96%)
- twitter/the-algorithm+17 (3%)
- twitter/the-algorithm-ml+3 (1%)
- 5
Aug 17, 2026 +282 net stars (median day +34)
- xai-org/x-algorithm+264 (94%)
- twitter/the-algorithm+15 (5%)
- twitter/the-algorithm-ml+2 (1%)
A peak day has at least 2 times the net stars of the median day of this chart, and at least 100 more. For each peak day, the list shows the repositories that gained the most net stars that day. It also shows their GitHub releases of that day or the day before (UTC), and their creation date when it was 14 days or less before. These events occur near the same day. They do not show why the stars came.
Largest
Most stars in total now.
readme:
X's Recommendation Algorithm is a set of services and jobs that are responsible for serving feeds of posts and other content across all X product surfaces
readme:
**Phoenix** reads the viewer's recent engagement history and predicts, for each post, how likely the viewer is to take each action on it.
description:
Source code for Twitter's Recommendation Algorithm
readme:
Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data.
- lyst/lightfm5.1K
description:
A Python implementation of LightFM, a hybrid recommendation algorithm.
- RUCAIBox/RecBole4.6K
readme:
Our library includes 94 recommendation algorithms, covering four major categories:
- amazon-archives/amazon-dsstnearchived · 4.4K
readme:
DSSTNE has been used at Amazon to generate personalized product recommendations for our customers at Amazon's scale.
- benfred/implicit3.8K
readme:
This project provides fast Python implementations of several different popular recommendation algorithms for implicit feedback datasets:
description:
A Macau food recommendation Agent built on Thompson Sampling + StackedPareto weight fusion
readme:
一个基于浙江历年录取数据的高考志愿推荐项目。输入位次、选科、专业和地区偏好后，可以生成冲稳保候选
Newest Admissions
Entered the index by the rising rule, newest first.
description:
A Macau food recommendation Agent built on Thompson Sampling + StackedPareto weight fusion
readme:
一个基于浙江历年录取数据的高考志愿推荐项目。输入位次、选科、专业和地区偏好后，可以生成冲稳保候选
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