2020推薦系統(tǒng)算法一覽、實(shí)踐

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人工智能大數(shù)據(jù)與深度學(xué)習(xí)  公眾號(hào)：datayx

國外前沿

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】

經(jīng)典論文

篩選文章的標(biāo)準(zhǔn)：前沿或者經(jīng)典的，工程導(dǎo)向的，google、阿里、facebook等一線互聯(lián)網(wǎng)公司出品的：

Wide & Deep Learning for Recommender Systems

google 的 wide&deep，必看論文，經(jīng)典到難以附加

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

華為對(duì)wide&deep的改進(jìn)，加了wide層的交叉項(xiàng)。如今工業(yè)界的主流模型

Practical lessons from predicting clicks on ads at facebook

facebook GBDT+LR的經(jīng)典方案。雖然如今已不是主流方案，但論文中的思想很值得學(xué)習(xí)。

Deep Neural Networks for YouTube Recommendations

介紹了Youtube推薦系統(tǒng)工業(yè)界架構(gòu)與方案，經(jīng)典必看

Real-time Personalization using Embeddings for Search Ranking at Airbnb

KDD2018 best paper，Embedding 必看論文，非常經(jīng)典

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

阿里的多目標(biāo)學(xué)習(xí)經(jīng)典方案，同時(shí)優(yōu)化CTR & CVR

Real-time Personalization using Embeddings for Search Ranking at Airbnb

介紹了 airbnb 搜索排序模型的演進(jìn)，工業(yè)性質(zhì)很強(qiáng)，值得參考

搜索引擎點(diǎn)擊模型綜述

清華馬少平團(tuán)隊(duì)的文章點(diǎn)擊模型入門必看，搜索引擎點(diǎn)擊模型綜述

論文附帶的開源項(xiàng)目

重在對(duì)自己算法的實(shí)現(xiàn)

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

綜述性復(fù)現(xiàn)

1.NeuRec -2020

復(fù)現(xiàn)了2013-2019年多篇論文，當(dāng)然bug也多，有一定學(xué)習(xí)價(jià)值項(xiàng)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. 推薦動(dòng)手實(shí)現(xiàn)-facebookresearch/dlrm-2019

https://github.com/facebookresearch/dlrm

單肩包／雙肩包／斜挎包／手提包／胸包／旅行包／上課書包 ／個(gè)性布袋等各式包飾挑選

https://shop585613237.taobao.com/

KDD（https://www.kdd.org/kdd2020/）是推薦領(lǐng)域一個(gè)頂級(jí)的國際會(huì)議。本次接收的論文按照推薦系統(tǒng)應(yīng)用場(chǎng)景可以大致劃分為：CTR預(yù)估、TopN推薦、對(duì)話式推薦、序列推薦等。同時(shí)，GNN、強(qiáng)化學(xué)習(xí)、多任務(wù)學(xué)習(xí)、遷移學(xué)習(xí)、AutoML、元學(xué)習(xí)在推薦系統(tǒng)的落地應(yīng)用也成為當(dāng)下的主要研究點(diǎn)。此屆會(huì)議有很大一部分來自工業(yè)界的論文，包括Google、Microsoft、Criteo、Spotify以及國內(nèi)大廠阿里、百度、字節(jié)、華為、滴滴等。

CTR Prediction

1. AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction 【華為諾亞】

簡(jiǎn)介：本文采用AutoML的搜索方法選擇重要性高的二次特征交互項(xiàng)、去除干擾項(xiàng)，提升FM、DeepFM這類模型的準(zhǔn)確率。
論文：arxiv.org/abs/2003.1123

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

論文：arxiv.org/abs/2006.1033

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

論文：arxiv.org/abs/2007.0643

Graph-based Recommendation

1. A Framework for Recommending Accurate and Diverse Items Using Bayesian Graph Convolutional Neural Networks 【華為諾亞】

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

論文：arxiv.org/abs/2007.0021

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

簡(jiǎn)介：本文通過關(guān)聯(lián)多個(gè)視角的圖(item-item圖、item-shop圖、shop-shop圖等)增強(qiáng)item表征，用于item召回。
論文：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

論文：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

論文：arxiv.org/abs/2006.0873

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

論文：arxiv.org/abs/2007.0403

3. Interactive Path Reasoning on Graph for Conversational Recommendation

論文：arxiv.org/abs/2007.0019

CF and Top-N Recommendation

1. Dual Channel Hypergraph Collaborative Filtering 【百度】

筆記：blog.csdn.net/weixin_42

2. Probabilistic Metric Learning with Adaptive Margin for Top-K Recommendation 【華為諾亞】

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

論文：arxiv.org/abs/2005.0934

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

論文：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】

論文：arxiv.org/abs/1909.0210

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

論文：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】

論文：astro.temple.edu/~tuf28

Sequential Recommendation

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

論文：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】

論文：arxiv.org/pdf/2006.0452

RL for Recommendation

1. Jointly Learning to Recommend and Advertise 【字節(jié)跳動(dòng)】

論文：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】

論文：researchgate.net/public

Multi-Task Learning

1. Privileged Features Distillation at Taobao Recommendations 【阿里】

論文：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】

論文：arxiv.org/abs/2007.0708

AutoML for Recommendation

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

論文：arxiv.org/abs/1907.0447

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

論文：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】

論文：arxiv.org/abs/2007.1298

2. Evaluating Conversational Recommender Systems via User Simulation

論文：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】

論文：public.asu.edu/~rguo12/

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

論文：arxiv.org/abs/2007.1298

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

論文：research.google/pubs/pu

POI Recommendation

1. Geography-Aware Sequential Location Recommendation 【Microsoft】

論文：staff.ustc.edu.cn/~lian

Cold-Start Recommendation

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

論文：arxiv.org/abs/2007.0318

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

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

Others

1. Improving Recommendation Quality in Google Drive 【Google】

論文：research.google/pubs/pu

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

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

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不斷更新資源

深度學(xué)習(xí)、機(jī)器學(xué)習(xí)、數(shù)據(jù)分析、python

 搜索公眾號(hào)添加： datayx  

機(jī)大數(shù)據(jù)技術(shù)與機(jī)器學(xué)習(xí)工程

 搜索公眾號(hào)添加： datanlp

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