gru4rec 话题在 GitHub 上没有填写简介，这里按 Star 数汇总最相关的热门仓库。
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、AutoFIS 等，包含经典推荐系统数据集 criteo、movielens 等
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.
Full stack recommendation system with Machine learning models, FastAPI, ReactJS, Celery, Redis, Postgresql
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.
PyTorch implementation of GRU4Rec, SQN and SMORL with evaluation framework covering Hit-Ratio, NDCG, Novelty, Diversity and Repetitiveness metrics for top-k recommendations.
Real-time traveler representation learning for hotel recommendations, benchmarking recency-weighted baselines, GRUs, and Transformers with temporal evaluation and streaming inference.
Deep hybrid recommender system for cold-start mitigation and cross-/up-selling using behavioral and content-based item embeddings.
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.