| XuefengHuang/RecommendationSystem |
190 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
|
Python |
| Book recommender system using collaborative filtering based on Spark |
| antonmedv/spark |
119 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
|
JavaScript |
| GitHub Stars Sparklines ⚡️ |
| brkyvz/streaming-matrix-factorization |
100 |
|
0 |
0 |
about 10 years ago |
0 |
|
4 |
apache-2.0 |
Scala |
| Distributed Streaming Matrix Factorization implemented on Spark for Recommendation Systems |
| huangyueranbbc/Spark_ALS |
68 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
mit |
Java |
| 基于spark-ml,spark-mllib,spark-streaming的推荐算法实现 |
| wangj1106/recommendMoteur |
57 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
|
Scala |
| 电影推荐系统、电影推荐引擎、使用Spark完成的电影推荐引擎 |
| snowch/movie-recommender-demo |
50 |
|
0 |
0 |
almost 4 years ago |
0 |
|
1 |
apache-2.0 |
Jupyter Notebook |
| This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop). |
| seejohnrun/php-statsd |
30 |
|
0 |
0 |
over 12 years ago |
0 |
|
0 |
|
PHP |
| simple statsd library and CI spark |
| Thomas-George-T/Movies-Analytics-in-Spark-and-Scala |
24 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
apache-2.0 |
|
| Data cleaning, pre-processing, and Analytics on a million movies using Spark and Scala. |
| cfregly/spark-after-dark |
24 |
|
0 |
0 |
almost 11 years ago |
0 |
|
0 |
|
Scala |
| yu-iskw/click-through-rate-prediction |
21 |
|
0 |
0 |
about 10 years ago |
0 |
|
1 |
apache-2.0 |
Scala |
| Kaggle's click through rate prediction with Spark Pipeline API |
