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 easy and efficient tool to build sequential recommendation system utilizing SASRec
An extremely modular, easy-to-use, and research-oriented framework for Generative Recommendation.
Minor project
Pet projects involving recommender systems
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.
OTTO session-based recommendation — co-visitation baseline + SASRec multi-task transformer
Sequential recommendation on Amazon Video Games, built progressively from a bag-of-items baseline to a full self-attention transformer — measuring what attention and positional embeddings each contribute. Streamlit demo included.
A decoupled Two-Tower and SASRec architecture that isolates user data from item embeddings to enable "Right to be Forgotten" compliance via exact retraining.
State-space (S4) sequential recommender for next-item prediction: a from-scratch Structured State-Space kernel (DPLR/HiPPO, bilinear+ZOH, FFT long-conv) that beats a SASRec baseline (+11% Hit@10 / +14% NDCG@10) on Amazon Reviews under a leakage-free timestamp leave-one-out split.
LoRA-adapted LFM2.5 for sequential recommendation with SASRec attention
A fully implemented AI Template Kit for building, understanding, and deploying real-world recommendation systems. Covers collaborative filtering, matrix factorization, content-based models, deep learning approaches. Includes ready-to-use code, service deployment logic, evaluation workflows, and step-by-step explanations.
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