hu739317870 / UPCF
CF 推荐系统的实现,以及我的改进。使用 MovieLens-1m 数据集,通过 MAE、Persicion、Recall 检验准确性。
☆11Updated 4 years ago
Alternatives and similar repositories for UPCF:
Users that are interested in UPCF are comparing it to the libraries listed below
- 实现了基于协同过滤(UserCF)的模型、基于隐语义(LFM)的模型、基于图(PersonalRank)的模型,并结合三种模型的结果给出最终结果的推荐算法☆24Updated 6 years ago
- 推荐系统---实验+复现+创新☆51Updated last year
- 推荐系统论文☆23Updated 5 years ago
- 使用MovieLens数据集实现了基于Auto Encoder(AE), Variational Auto Encoder(VAE), BERT的深度学习电影推荐系统☆72Updated 4 years ago
- 本项目使用两种算法来实现一个电影推荐系统,一个是CNN,另一个是矩阵分解的协同过滤。☆136Updated 6 years ago
- 一些传统推荐算法的实现,包括基于内容的推荐,协同过滤,矩阵分解☆296Updated 7 years ago
- 用来处理freebase, kb4rec, movielens它们数据集的项目☆35Updated 4 years ago
- 深度学习推荐算法☆23Updated 4 years ago
- 矩阵分解pytorch实现☆13Updated last year
- 基于知识图谱的推荐算法实现☆41Updated 2 years ago
- collaborative filtering methods for recommender systems☆62Updated 3 years ago
- A pure Python implement of Collaborative Filtering based on MovieLens' dataset.☆187Updated 5 years ago
- 基于netflix prize 和 H&M开源数据集,从零开始构建企业级推荐系统。☆86Updated 3 months ago
- 图书推荐系统☆11Updated 5 years ago
- UserCF和ItemCF协同过滤推荐算法的实现☆541Updated 3 years ago
- Pytorch for autorec with collaborative filtering☆36Updated 6 years ago
- 豆瓣电影推荐系统(Douban Movie Recommendation System)根据豆瓣电影数据以及豆瓣用户的观影和影评数据,使用基于物品的协同过滤算法对用户进行个性化推荐,并设计GUI进行用户交互。☆213Updated 3 years ago
- A network TV program recommendation system implemented by python is mainly based on the post-fusion of user collaborative filtering and c…☆29Updated 5 years ago
- 构建的简单电影推荐系统☆15Updated 6 years ago
- Flask+Spark+ALS+MovieLens(电影推荐系统)☆9Updated 6 years ago
- 电影推荐系统☆39Updated 5 years ago
- 推荐系统实践(基于近邻和LFM的推荐系统)☆101Updated 7 years ago
- re-implementation of ConvMF (RecSys'16) in TensorFlow with even better performance☆10Updated 4 years ago
- algorithms about recommender systems:probabilistic matrix factorization☆25Updated 7 years ago
- 对KGCN的代码做了一些注释,希望有需要的同学看了后有所帮助。☆37Updated 4 years ago
- It's a realization of the algorithm SoRec (Social Recommendation Using Probabilistic Matrix Factorization) which was published in 2008.☆16Updated 5 years ago
- 实现了一系列常见的推荐算法,如UserCF,ItemCF,SVD等,包含“切分训练集与测试集-训练模型-推荐-评估”一整套流程。☆20Updated 5 years ago
- TrustSVD: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings☆21Updated 6 years ago
- 项亮等《推荐系统实践》算法代码☆29Updated 5 years ago
- Summary of social recommendation papers and codes☆214Updated 3 years ago