| HongZhaoHua/jstarcraft-rns |
368 |
|
0 |
0 |
about 3 years ago |
0 |
|
0 |
apache-2.0 |
Java |
| 专注于解决推荐领域与搜索领域的两个核心问题:排序预测(Ranking)和评分预测(Rating). 为相关领域的研发人员提供完整的通用设计与参考实现. 涵盖了70多种排序预测与评分预测算法,是最快最全的Java推荐与搜索引擎. |
| DataSystemsLab/recdb-postgresql |
262 |
|
0 |
0 |
almost 6 years ago |
0 |
|
10 |
|
C |
| RecDB is a recommendation engine built entirely inside PostgreSQL |
| GHamrouni/Recommender |
249 |
|
0 |
0 |
over 3 years ago |
0 |
|
1 |
bsd-2-clause |
C |
| A C library for product recommendations/suggestions using collaborative filtering (CF) |
| khanhnamle1994/movielens |
202 |
|
0 |
0 |
almost 7 years ago |
0 |
|
5 |
mit |
Jupyter Notebook |
| 4 different recommendation engines for the MovieLens dataset. |
| XuefengHuang/RecommendationSystem |
190 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
|
Python |
| Book recommender system using collaborative filtering based on Spark |
| fstrub95/Autoencoders_cf |
155 |
|
0 |
0 |
about 8 years ago |
0 |
|
4 |
|
Lua |
| blei-lab/ctr |
139 |
|
0 |
0 |
over 10 years ago |
0 |
|
2 |
gpl-2.0 |
C++ |
| Collaborative modeling for recommendation. Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users' ratings. |
| revantkumar/Collaborative-Filtering |
114 |
|
0 |
0 |
over 10 years ago |
0 |
|
3 |
|
Python |
| Implemented Item, User and Hybrid based Collaborative Filtering |
| artem-oppermann/Deep-Autoencoders-For-Collaborative-Filtering |
111 |
|
0 |
0 |
almost 3 years ago |
0 |
|
5 |
apache-2.0 |
Python |
| Using Deep Autoencoders for predictions of movie ratings. |
| asif536/Movie-Recommender-System |
86 |
|
0 |
0 |
about 5 years ago |
0 |
|
4 |
apache-2.0 |
HTML |
| Basic Movie Recommendation Web Application using user-item collaborative filtering. |
