lfywork

bilibili-api

哔哩哔哩的API调用模块

birthday

Characteristic-Based-Time-Series-Clustering

Time Series Clustering Based on Characteristic Based Feature Extraction inspired by the paper mentioned in the README

deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

DSSL

User activities in real systems are usually time-sensitive. But most of the existing sequential models in recommender systems neglect the time-related signals. In this paper, we find that users' temporal behaviours tend to be driven by their regularly-changing states, which provides a new perspective on learning users' dynamic preference. However, since the individual state is usually latent, the event space is high dimensional, and meanwhile temporal dependency of states is personalized and complex, it is challenging to represent, model and learn the time-evolving patterns of user's state. Focusing on these challenges, we propose a Deep Structured State Learning (DSSL) framework which is able to learn the representation of temporal states and the complex state dependency for time-sensitive recommendation. Extensive experiments demonstrate that DSSL achieves competitive results on four real-world recommendation datasets. Furthermore, experiments also show some interesting rules for designing the state dependency network.

edward

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Familia

A Toolkit for Industrial Topic Modeling

gdutthesis

广东工业大学 LaTeX 论文模板

gluon-ts

Probabilistic time series modeling in Python

SRGNN_PyG

A reimplementation of SRGNN.

lfywork/SRGNN_PyG

A reimplementation of SRGNN.

lfywork/DSSL

User activities in real systems are usually time-sensitive. But most of the existing sequential models in recommender systems neglect the time-related signals. In this paper, we find that users' temporal behaviours tend to be driven by their regularly-changing states, which provides a new perspective on learning users' dynamic preference. However, since the individual state is usually latent, the event space is high dimensional, and meanwhile temporal dependency of states is personalized and complex, it is challenging to represent, model and learn the time-evolving patterns of user's state. Focusing on these challenges, we propose a Deep Structured State Learning (DSSL) framework which is able to learn the representation of temporal states and the complex state dependency for time-sensitive recommendation. Extensive experiments demonstrate that DSSL achieves competitive results on four real-world recommendation datasets. Furthermore, experiments also show some interesting rules for designing the state dependency network.

lfywork/bilibili-api

哔哩哔哩的API调用模块

lfywork/birthday

lfywork/Characteristic-Based-Time-Series-Clustering

Time Series Clustering Based on Characteristic Based Feature Extraction inspired by the paper mentioned in the README

lfywork/deep-learning-time-series

List of papers, code and experiments using deep learning for time series forecasting

lfywork/edward

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

lfywork/Familia

A Toolkit for Industrial Topic Modeling

lfywork/gdutthesis

广东工业大学 LaTeX 论文模板

lfywork/gluon-ts

Probabilistic time series modeling in Python

lfywork/GraphEmbedding

Implementation and experiments of graph embedding algorithms.deep walk,LINE(Large-scale Information Network Embedding),node2vec,SDNE(Structural Deep Network Embedding),struc2vec

lfywork/ishop

My private homework about mobile web.

lfywork/ISSM

Innovation State Space Models

lfywork/jodie

A PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"

lfywork/lfywork.github.io

My personalized web page.

lfywork/NeuralPointProcess

lfywork/proximityhash

Geohashes in proximity

lfywork/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

lfywork/pytorch-ts

PyTorch based Probabilistic Time Series forecasting framework based on GluonTS backend

lfywork/smore

SMORe: Modularize Graph Embedding for Recommendation

lfywork/ssm-python

State Space Models for Python

lfywork/ssmdm

Recurrent state-space models for decision making

lfywork/svae

code for Structured Variational Autoencoders

lfywork/tf-gnn-samples

Tensorflow implementations of Graph Neural Networks

lfywork/vae

a simple vae and cvae from keras

lfywork/wmf

Weighted matrix factorization in Python