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人工智能大数据与深度学习  公众号：datayx

国外前沿

以下引用都高

Fairness-Aware Ranking in Search & Recommendation Systems with Application to LinkedIn Talent Search 【2019】

Learning binary codes with neural collaborative filtering for efficient recommendation systems【2019】

经典论文

筛选文章的标准：前沿或者经典的，工程导向的，google、阿里、facebook等一线互联网公司出品的：

Wide & Deep Learning for Recommender Systems

google 的 wide&deep，必看论文，经典到难以附加

DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

华为对wide&deep的改进，加了wide层的交叉项。如今工业界的主流模型

Practical lessons from predicting clicks on ads at facebook

facebook GBDT+LR的经典方案。虽然如今已不是主流方案，但论文中的思想很值得学习。

Deep Neural Networks for YouTube Recommendations

介绍了Youtube推荐系统工业界架构与方案，经典必看

Real-time Personalization using Embeddings for Search Ranking at Airbnb

KDD2018 best paper，Embedding 必看论文，非常经典

Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate

阿里的多目标学习经典方案，同时优化CTR & CVR

Real-time Personalization using Embeddings for Search Ranking at Airbnb

介绍了 airbnb 搜索排序模型的演进，工业性质很强，值得参考

搜索引擎点击模型综述

清华马少平团队的文章点击模型入门必看，搜索引擎点击模型综述

论文附带的开源项目

重在对自己算法的实现

Hyperbolic (ordinary and variational) autoencoders for recommender systems-2020

https://github.com/evfro/HyperbolicRecommenders

Hieararchical RNN recommender with temporal modeling-2017

fashion-recommendation-2018

In this project, I created an end-to-end solution for large-scale image classification and visual recommendation on fashion images. More specifically, my model can learn the important regions in an image and generate diverse recommendations based on such semantic similarity.

https://github.com/khanhnamle1994/fashion-recommendation

综述性复现

1.NeuRec -2020

复现了2013-2019年多篇论文，当然bug也多，有一定学习价值项目地址,目前700星星左右2020/11/27

https://github.com/wubinzzu/NeuRec

2.RecSys2019_DeepLearning_Evaluation-2019

https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation

3. 推荐动手实现-facebookresearch/dlrm-2019

https://github.com/facebookresearch/dlrm

单肩包／双肩包／斜挎包／手提包／胸包／旅行包／上课书包 ／个性布袋等各式包饰挑选

https://shop585613237.taobao.com/

↓

KDD（https://www.kdd.org/kdd2020/）是推荐领域一个顶级的国际会议。本次接收的论文按照推荐系统应用场景可以大致划分为：CTR预估、TopN推荐、对话式推荐、序列推荐等。同时，GNN、强化学习、多任务学习、迁移学习、AutoML、元学习在推荐系统的落地应用也成为当下的主要研究点。此届会议有很大一部分来自工业界的论文，包括Google、Microsoft、Criteo、Spotify以及国内大厂阿里、百度、字节、华为、滴滴等。

CTR Prediction

1. AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction 【华为诺亚】

简介：本文采用AutoML的搜索方法选择重要性高的二次特征交互项、去除干扰项，提升FM、DeepFM这类模型的准确率。
论文：arxiv.org/abs/2003.1123

2. Category-Specific CNN for Visual-aware CTR Prediction at JD.com 【京东】

论文：arxiv.org/abs/2006.1033

3. Towards Automated Neural Interaction Discovering for Click-Through Rate Prediction 【Facebook】

论文：arxiv.org/abs/2007.0643

Graph-based Recommendation

1. A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks 【华为诺亚】

2. An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph 【Amazon】

论文：arxiv.org/abs/2007.0021

3. M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems 【阿里】

简介：本文通过关联多个视角的图(item-item图、item-shop图、shop-shop图等)增强item表征，用于item召回。
论文：arxiv.org/abs/2005.1011

4. Handling Information Loss of Graph Neural Networks for Session-based Recommendation

5. Interactive Path Reasoning on Graph for Conversational Recommendation

论文：arxiv.org/abs/2007.0019

6. A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce 【阿里】

7. Gemini: A Novel and Universal Heterogeneous Graph Information Fusing Framework for Online Recommendations 【滴滴】

Conversational Recommendation

1. Evaluating Conversational Recommender Systems via User Simulation

论文：arxiv.org/abs/2006.0873

2. Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion

论文：arxiv.org/abs/2007.0403

3. Interactive Path Reasoning on Graph for Conversational Recommendation

论文：arxiv.org/abs/2007.0019

CF and Top-N Recommendation

1. Dual Channel Hypergraph Collaborative Filtering 【百度】

笔记：blog.csdn.net/weixin_42

2. Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation 【华为诺亚】

3. Controllable Multi-Interest Framework for Recommendation 【阿里】

论文：arxiv.org/abs/2005.0934

4. Embedding-based Retrieval in Facebook Search 【Facebook】

论文：arxiv.org/abs/2006.1163

5. On Sampling Top-K Recommendation Evaluation

Embedding and Representation

1. Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems 【Facebook】

论文：arxiv.org/abs/1909.0210

2. PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest 【Pinterest】

论文：arxiv.org/abs/2007.0363

3. SimClusters: Community-Based Representations for Heterogeneous Recommendations at Twitter 【Twitter】

4. Time-Aware User Embeddings as a Service 【Yahoo】

论文：astro.temple.edu/~tuf28

Sequential Recommendation

1. Disentangled Self-Supervision in Sequential Recommenders 【阿里】

论文：http://pengcui.thumedialab.com/papers/Disen...

2. Handling Information Loss of Graph Neural Networks for Session-based Recommendation

3. Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective 【阿里】

论文：arxiv.org/pdf/2006.0452

RL for Recommendation

1. Jointly Learning to Recommend and Advertise 【字节跳动】

论文：arxiv.org/abs/2003.0009

2. BLOB: A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals 【Criteo】

3. Joint Policy-Value Learning for Recommendation 【Criteo】

论文：researchgate.net/public

Multi-Task Learning

1. Privileged Features Distillation at Taobao Recommendations 【阿里】

论文：arxiv.org/abs/1907.0517

Transfer Learning

1. Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling 【Salesforce】

2. Semi-supervised Collaborative Filtering by Text-enhanced Domain Adaptation 【阿里】

论文：arxiv.org/abs/2007.0708

AutoML for Recommendation

1. Neural Input Search for Large Scale Recommendation Models 【Google】

论文：arxiv.org/abs/1907.0447

2. Towards Automated Neural Interaction Discovering for Click-Through Rate Prediction 【Facebook】

论文：arxiv.org/abs/2007.0643

Federated Learning

1. FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems

Evaluation

1. Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions 【Netflix, Spotify】

论文：arxiv.org/abs/2007.1298

2. Evaluating Conversational Recommender Systems via User Simulation

论文：arxiv.org/abs/2006.0873

3. 【Best Paper Award】On Sampled Metrics for Item Recommendation 【Google】

4. On Sampling Top-K Recommendation Evaluation

Debiasing

1. Debiasing Grid-based Product Search in E-commerce 【Etsy】

论文：public.asu.edu/~rguo12/

2. Counterfactual Evaluation of Slate Recommendations with Sequential Reward Interactions 【Netflix, Spotify】

论文：arxiv.org/abs/2007.1298

3. Attribute-based Propensity for Unbiased Learning in Recommender Systems: Algorithm and Case Studies 【Google】

论文：research.google/pubs/pu

POI Recommendation

1. Geography-Aware Sequential Location Recommendation 【Microsoft】

论文：staff.ustc.edu.cn/~lian

Cold-Start Recommendation

1. MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

论文：arxiv.org/abs/2007.0318

2. Meta-learning on Heterogeneous Information Networks for Cold-start Recommendation

论文：https://ink.library.smu.edu.sg/cgi/...

Others

1. Improving Recommendation Quality in Google Drive 【Google】

论文：research.google/pubs/pu

2. Temporal-Contextual Recommendation in Real-Time 【Amazon】

论文：https://assets.amazon.science/96/71/d1f25754497681133c7aa2b7eb05/temporal-contextual-recommendation-in-real-time.pdf

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特征工程(一)

特征工程(二) :文本数据的展开、过滤和分块

特征工程(三):特征缩放,从词袋到 TF-IDF

特征工程(四): 类别特征

特征工程(五): PCA 降维

特征工程(六): 非线性特征提取和模型堆叠

特征工程(七)：图像特征提取和深度学习

如何利用全新的决策树集成级联结构gcForest做特征工程并打分？

Machine Learning Yearning 中文翻译稿

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不断更新资源

深度学习、机器学习、数据分析、python

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