PyTorch(1.6+) implementation of https://github.com/kang205/SASRec
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Updated
Sep 7, 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).
AI Recommendation System & Data Engineering Lakehouse processing 21M+ records. Bridges distributed Databricks PySpark Medallion pipelines (Delta Lake SCD-2) with a 6-model PyTorch deep learning recommendation ensemble, 10-shard Neon serverless vector search (pgvector), sub-10ms serving, and an interactive React 19 cinema portal.
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
Sequential recommender with an S4 state-space encoder; includes a preliminary Amazon Reviews comparison against a SASRec baseline
rusket 🦀🧺
Minor project
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.
Sequential deep-learning recommender (SASRec, PyTorch) for movies & music — Flask REST API + React/TS UI. 98.47% AUC-ROC on MovieLens 20M.
A sequential recommendation system that predicts what users will watch next and how they'll rate it using SASRec and multi-task learning.
Two-stage e-commerce recommender system (ALS retrieval + SASRec re-ranking) with Optuna tuning, evaluated on RetailRocket and Amazon Video Games datasets
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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