zhangwudi0

zhangwudi0's Stars

• binary-husky/gpt_academic

  为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。

  Language:Python67.9k2791.7k8.3k

• tensorflow/tensor2tensor

  Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

  Language:Python16k4701.2k3.6k

• LibraHp/GetQzonehistory

  获取QQ空间发布的历史说说

  Language:Python7.5k20264528

• tkipf/gcn

  Implementation of Graph Convolutional Networks in TensorFlow

  Language:Python7.2k1551942k

• princewen/tensorflow_practice

  tensorflow实战练习，包括强化学习、推荐系统、nlp等

  Language:Python6.8k221673.3k

• Yelp/dataset-examples

  Samples for users of the Yelp Academic Dataset

  Language:Python1.3k9441614

• cyang-kth/fmm

  Fast map matching, an open source framework in C++

  Language:C++93317223215

• suragnair/seqGAN

  A simplified PyTorch implementation of "SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient." (Yu, Lantao, et al.)

  Language:Python6451324149

• jayparks/transformer

  A Pytorch Implementation of "Attention is All You Need" and "Weighted Transformer Network for Machine Translation"

  Language:Python553617124

• chuxuzhang/KDD2019_HetGNN

  code of HetGNN

  Language:Python398524101

• guoyang9/NCF

  A pytorch implementation of He et al. "Neural Collaborative Filtering" at WWW'17

  Language:Python34842364

• librahu/HIN-Datasets-for-Recommendation-and-Network-Embedding

  Heterogeneous Information Network Datasets for Recommendation and Network Embedding

  34091692

• hubojing/POI-Recommendation

  Papers and resources about POI recommendation. | 兴趣点推荐相关论文、模型和资源。

  2244236

• yd-kwon/POMO

  codes for the paper "POMO: Policy Optimization with Multiple Optima for Reinforcement Learning"

  Language:Python1792851

• xbresson/TSP_Transformer

  Code for TSP Transformer

  Language:Jupyter Notebook1757436

• librahu/HERec

  Source code for TKDE 2018 "Heterogeneous information network embedding for recommendation"

  Language:TeX16742461

• hegongshan/Recommender-Systems-Paper

  Must-read Papers for Recommender Systems (RS)

  1531018

• yongqyu/STRNN

  Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts

  Language:Python14121463

• slientGe/Sequential_Recommendation_Tensorflow

  Several sequential recommended models implemented by tenosrflow1.x

  Language:Python1182724

• drhuangliwei/An-Attention-based-Spatiotemporal-LSTM-Network-for-Next-POI-Recommendation

  A python vision code of An Attention-based Spatiotemporal LSTM Network for Next POI Recommendation

  Language:Python875535

• natnij/seqGAN_pytorch

  seqGAN in pytorch

  Language:Python382310

• pyy0715/Neural-Collaborative-Filtering

  pytorch version of NCF

  Language:Python361211

• MrLeeeee/SDNE-based-on-Pytorch

  The SDNE (Structural Deep Network Embedding) based on Pytorch

  Language:Python31209

• idea-iitd/NeuroMLR

  Robust & Reliable Route Recommendation on Road Networks

  Language:Python231111

• YuxiaWu/PLSPL

  [TKDE 2020] Code and data for "Personalized long-and short-term preference learning for next POI recommendation."

  Language:Python22236

• libaoquan95/flickrAnalyse

  分析Flickr数据集

  Language:JavaScript179

• guoyiguang/Recommend

  Language:Python12118

• bigscity/NASR

  Language:Python10106

• Youjiangbaba/Dijkstra_travel_path

  Language:Python10203

• sajalhalder/TLR-M

  In this research, we present a problem of queuing time aware next POI recommendation and demonstrate how it is non-trivial to both recommend a next POI and simultaneously predict its queuing time. To solve this problem, we propose a multi-task, multi head attention transformer model called TLR-M. The model recommends next POIs to the target users and predicts queuing time to access the POIs simultaneously. By utilizing multi-head attention, the TLR-M model can integrate long range dependencies between any two POI visit efficiently and evaluate their contribution to select next POIs and to predict queuing time. To use this code in your research work please cite the following paper. Sajal Halder, Kwan Hui Lim, Jeffrey Chan, and Xiuzhen Zhang. Transformer-based multi-task learning for queuing time aware next poi recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 510–523. Springer, 2021, DOI: https://doi.org/10.1007/978-3-030-75765-6_41

  Language:Python4203