PyTorch(1.6+) implementation of https://github.com/kang205/SASRec
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Updated
Mar 19, 2026 - TeX
PyTorch(1.6+) implementation of https://github.com/kang205/SASRec
Several sequential recommended models implemented by tenosrflow1.x
⚡️ Implementation of TRON: Transformer Recommender using Optimized Negative-sampling, accepted at ACM RecSys 2023.
A toy large model for recommender system based on LLaMA2/SASRec/Meta's generative recommenders. Besides, note and experiments of official implementation for Meta's generative recommenders.
common used Recommend Baseline Model, including the traditional statistical model, and the Nerual Network Model. Focus on the SRS (Sequential Recommend System).
An extremely modular, easy-to-use, and research-oriented framework for Generative Recommendation.
Simple and fast project to learn baseline sequential recommenders.
An easy and efficient tool to build sequential recommendation system utilizing SASRec
AI Recommendation System — Real-time recommendation engine for movies, video, and digital media powered by SASRec Transformers, LightGCN Graphs, and PySpark Delta Lake.
Sequential recommender with an S4 state-space encoder; includes a preliminary Amazon Reviews comparison against a SASRec baseline
rusket 🦀🧺
Minor project
Sequential deep-learning recommender (SASRec, PyTorch) for movies & music — Flask REST API + React/TS UI. 98.47% AUC-ROC on MovieLens 20M.
Two-stage e-commerce recommender system (ALS retrieval + SASRec re-ranking) with Optuna tuning, evaluated on RetailRocket and Amazon Video Games datasets
Sequential recommendation: GRU4Rec & SASRec vs Popularity/Markov baselines on MovieLens-1M and RetailRocket; Recall/NDCG/MRR.
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.
A reproducible, resource-aware solution for the Kaggle OTTO multi-objective recommendation competition, featuring co-visitation retrieval, target-aware candidates, LambdaMART ranking, and neural retrieval experiments.
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