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ADPR: An Attention-based Deep Learning Point-of-Interest Recommendation Framework
A python vision code of An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation
Adversarial Point-of-Interest Recommendation
In this repository, We're going to implement the paper, which is "Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation", (B. Chang et al, IJCAI-ECAI'18), using a PyTorch library.
Official Matplotlib cheat sheets
Collaborative Deep Learning (CDL)
This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
Deep learning for recommender systems
Code for my PAKDD-2019, Distance2Pre: Personalized Spatial Preference for Next Point-of-Interest Prediction
Implementation of Graph Auto-Encoders in TensorFlow
The implementation of "Gated Attentive-Autoencoder for Content-Aware Recommendation"
A Graph Convolution Network based approach to Recommender Systems
Geom-GCN: Geometric Graph Convolutional Networks
Framework for evaluating Graph Neural Network models on semi-supervised node classification task
Gated Orthogonal Recurrent Unit implementation in tensorflow
Graph convolutional matrix completion
In this project, we will revisit the problem central to recommender systems: predicting a userβs preference for some item they have not yet rated. Like the Spark recommender from the first project, we will use a collaborative filtering model to explore this problem. Recall that in this model, the goal is to find the sentiment of a user about a particular item Unlike the the first project that used the ALS method, however, we will perform this task using a graphbased technique called DeepWalk.
PyTorch Implementation and Explanation of Graph Representation Learning papers involving DeepWalk, GCN, GraphSAGE, ChebNet & GAT.
Graph Neural Networks for Social Recommendation, WWW'19
Representation learning on large graphs using stochastic graph convolutions.
GRU4Rec is the original Theano implementation of the algorithm in "Session-based Recommendations with Recurrent Neural Networks" paper, published at ICLR 2016 and its follow-up "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations". The code is optimized for execution on the GPU.
Hierarchical Gating Networks for Sequential Recommendation
Inductive graph-based matrix completion (IGMC) from "M. Zhang and Y. Chen, Inductive Matrix Completion Based on Graph Neural Networks, ICLR 2020 spotlight".
KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019
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