gru4rec

Recommendation Algorithm大规模推荐算法库，包含推荐系统经典及最新算法LR、Wide&Deep、DSSM、TDM、MIND、Word2Vec、Bert4Rec、DeepWalk、SSR、AITM，DSIN，SIGN，IPREC、GRU4Rec、Youtube_dnn、NCF、GNN、FM、FFM、DeepFM、DCN、DIN、DIEN、DLRM、MMOE、PLE、ESMM、ESCMM, MAML、xDeepFM、DeepFEFM、NFM、AFM、RALM、DMR、GateNet、NAML、DIFM、Deep Crossing、PNN、BST、AutoInt、FGCNN、FLEN、Fibinet、ListWise、DeepRec、ENSFM，TiSAS，AutoFI…

Several sequential recommended models implemented by tenosrflow1.x

用pytorch 方法复现了二十多个经典的推荐算法论文，其中包含排序论文和推荐召回论文，并在demo里面选了一个召回模型和排序模型的运行示例。

⚡️ Implementation of TRON: Transformer Recommender using Optimized Negative-sampling, accepted at ACM RecSys 2023.

Jupyter тетрадка с решением Kaggle соревнования Alfa Campus

Keras implementation of the GRU4Rec recommendation algorithm.

PyTorch implementation of GRU4Rec, SQN and SMORL with evaluation framework covering Hit-Ratio, NDCG, Novelty, Diversity and Repetitiveness metrics for top-k recommendations.

Sequential Recommender (SASRec/GRU) benchmarking the T-ECD dataset (9.2M events). Features interactive testing dashboards, 2D/3D cross-domain embedding visualizations, and impact analysis.

Full stack recommendation system with Machine learning models, FastAPI, ReactJS, Celery, Redis, Postgresql

GRU4Rec and SASRec vs Popularity/Markov on MovieLens-1M and RetailRocket. Ranking metrics (Recall/NDCG/MRR) plus SNIPS off-policy evaluation - would deploying it actually lift conversion? - cross-checked against Open Bandit Pipeline. Neither sequence model beats the Markov baseline, which is the honest result.