Transformers4Rec is a flexible and efficient library for sequential and session-based recommendation and works with PyTorch.
[AAAI 2019] Source code and datasets for "Session-based Recommendation with Graph Neural Networks"
Recommender Systems Paperlist that I am interested in
Session-based Recommendation
[SIGIR 2020] Python implementation for "TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation"
PyTorch Implementation of Introducing Self-Attention to Target Attentive Graph Neural Networks (AISP '22)
[ECIR 2024] Official repository for the paper titled "Self Contrastive Learning for Session-based Recommendation"
Comparative study of Graph Attention Networks for graph-based session recommendation.
A session-based recommendation system to recommend baby products on Amazon using 4 models namely ITEMKNN, POP, GRU4Rec, and STAMP, STAMP performs the best in all accuracy metrics followed by GRU4Rec. We also did result analysis, including ranking accuracy, coverage, popularity.
Code for 2022 Applied Science Special Issue "Logit Averaging: Capturing Global Relation for Session-based Recommendation"
Data and feedback-loop half of a session-based music recommender on Navidrome — live scrobbler events, versioned datasets for GRU4Rec/SessionKNN, and drift-triggered retraining.
Two-stage session-based recommender (LightGBM LambdaRank) for an Inditex hackathon. Cold-start focused, 93% of sessions have no user history. NDCG@5 = 0.377, Hit Rate@5 = 76%.
This repository contains my summaries of various academic papers I have read.
OTTO session-based recommendation — co-visitation baseline + SASRec multi-task transformer
Amazon KDD Cup '23: Multilingual Recommendation Challenge
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