Source code for Twitter's Recommendation Algorithm

推荐/广告/搜索领域工业界经典以及最前沿论文集合。A collection of industry classics and cutting-edge papers in the field of recommendation/advertising/search.

Awesome Deep Learning papers for industrial Search, Recommendation and Advertisement. They focus on Embedding, Matching, Ranking (CTR/CVR prediction), Post Ranking, Large Model (Generative Recommen…

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Papers on Computational Advertising

Graph Convolutional Networks in PyTorch

《机器翻译：基础与模型》肖桐 朱靖波 著 - Machine Translation: Foundations and Models

基于Pytorch的，中文语义相似度匹配模型（ABCNN、Albert、Bert、BIMPM、DecomposableAttention、DistilBert、ESIM、RE2、Roberta、SiaGRU、XlNet）

Easy-to-use,Modular and Extendible package of deep-learning based CTR models .

ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled generation task: given a Wikipedia table and a set of highlighted table cells, p…

Models and examples built with TensorFlow

Longformer: The Long-Document Transformer

Easy Bootstrap Resampling and Approximate Randomization for BLEU, METEOR, and TER using Multiple Optimizer Runs. This implements "Better Hypothesis Testing for Statistical Machine Translation: Cont…

Implementation of the "Deep Matrix Factorization Models for Recommender Systems"

An elaborate and exhaustive paper list for Named Entity Recognition (NER)

Code for our paper: Controlling Text Complexity in Neural Machine Translation

Implementation of NeurIPS 19 paper: Paraphrase Generation with Latent Bag of Words

Code for "Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation" (NeurIPS 2019)

Chinese Language Generation Evaluation 中文生成任务基准测评

Must-read Papers on Textual Adversarial Attack and Defense

The collection of recent papers about variational inference

Must-read Papers on pre-trained language models.

Code for the paper "Language Models are Unsupervised Multitask Learners"

Repository for KPTimes corpus

Dataset for EMNLP-2019 (Attribute-aware Sequence Network for Review Summarization) paper