Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

Generative model for sequential recommendation based on Convolution Neural Networks (CNN))

A PyTorch implementation of Neural Attentive Session Based Recommendation (NARM)

Session-based Recommendation

The 4th Place Solution to the 2019 ACM Recsys Challenge by Team RosettaAI

source code of paper "Positive, Negative and Neutral: Modeling Implicit Feedback in Session-based News Recommendation", which is accepted at SIGIR 2022.

[ECIR 2024] Official repository for the paper titled "Self Contrastive Learning for Session-based Recommendation"

This is the code for the Paper: Disentangled Graph Neural Networks for Session-based Recommendation

Sequence-to-Sequence Generative Model for Sequential Recommender System

This is the code for the Paper: Transition Information Enhanced Disentangled Graph Neural Networks for Session-based Recommendation

[Tutorial] - Applying Word2Vec technique to Recommendation System a.k.a Item2Vec a.k.a Prod2Vec

Code for the IJCAI'19 paper "Deep Session Interest Network for Click-Through Rate Prediction"

Implementation of Basic Recommendation System Models with PyTorch

A Tensorflow implementation of Session-based Recommendation with Simplified Graph Neural Networks

PyTorch implementation of GRU4Rec, SQN and SMORL with evaluation framework covering Hit-Ratio, NDCG, Novelty, Diversity and Repetitiveness metrics for top-k recommendations.