In order to see my remarks and notes download pdf file instead of viewing it on GitHub.
I tried to put papers in each category in convenient to read order.
Papers
- Deep Character-Level Click-Through Rate Prediction for Sponsored Search, 2017
- Field-aware Factorization Machines for CTR Prediction
- FFM in a Real-world Online Advertising System
- Attentional Factorization Machines - Learning the Weight of Feature Interactions via Attention Networks
- Deep & Cross Network for Ad Click Predictions
- Deep Crossing - Web-Scale Modeling without Manually Crafted Combinatorial Features
- Neural Factorization Machines for Sparse Predictive Analytics, 2017
- DeepFM
- Deep Embedding Forest- Forest-based Serving with Deep Embedding Features
- AutoInt- Automatic Feature Interaction Learning via Self-Attentive Neural Networks, 2019
- Deep Interest Network for Click-Through Rate Prediction, 2018
- Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks, 2018
- Deep Interest Evolution Network for Click-Through Rate Prediction
- DeepGBM, 2019
- Model Ensemble for Click Prediction in Bing Search Ads
- Using boosted trees for Click-Through Rate Prediction
- Ad Click Prediction- a View from the Trenches
- Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising
- Improving Ad Click Prediction by Considering Non-displayed Events, 2019
- Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions
- Estimating CVR from Past Performance Data
- Offline Evaluation of Response Prediction in Online Advertising Auctions
- Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising
- Entire Space Multi-Task Model - An Effective Approach for Estimating Post-Click Conversion Rate
- A Survey on Transfer Learning
- Warm Up Cold-start Advertisements- Improving CTR Predictions via Learning to Learn ID Embeddings, 2019
- AiAds- Automated and Intelligent Advertising System for Sponsored Search, 2019
- RippleNet- Propagating User Preferences on the Knowledge Graph for Recommender Systems, 2018
- A Sparse Deep Factorization Machine for Efficient CTR prediction, 2020
- Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data, 2020
- Click-Through Rate Prediction with the User Memory Network, 2019
- Predicting conversions in display advertising based on URL embeddings, 2020
- On the Effectiveness of Self-supervised Pre-training for Modeling User Behavior Sequences, 2020
Papers
- A Structured Self-Attentive Sentence Embedding, 2017
- Exploiting Similarities among Languages for Machine Translation, 2013
- Word translation without parallel data, 2017
- Translating Embeddings for Modeling Multi-relational Data, 2013
- Knowledge graph embedding by translating on hyperplanes, 2014
- Translation-based Recommendation, 2017
- Item2Vec
- Learning Item-Interaction Embeddings for User Recommendations, 2018
- Neural Feature Embedding for User Response Prediction in Real-Time Bidding
- Search Retargeting using Directed Query Embeddings
- Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising, 2016
- Real-time Personalization using Embeddings for Search Ranking at Airbnb
- DeepWalk
- LINE- Large-scale Information Network Embedding, 2015
- node2vec
- entity2rec- Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation, 2018
- PTE Predictive Text Embedding through Large-scale Heterogeneous Text Networks
- Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba
- Personolized Entity Recommendation: A Heterogeneous Information Network Approach
- metapath2vec
- HIN2vec
- Are Meta-Paths Necessary?
- Is a Single Vector Enough? Exploring Node Polysemy for Network Embedding
- dynnode2vec Scalable Dynamic Network Embedding
- Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks, 2017
- Heterogeneous Neural Attentive Factorization Machine for Rating Prediction, 2018
- Should we Embed?, 2019
- Beyond Vector Spaces- Compact Data Representation as Differentiable Weighted Graphs, 2019
- A Comprehensive Survey on Graph Neural Networks, 2019
- Inductive Representation Learning on Large Graphs, 2017
- Graph Convolutional Neural Networks for Web-Scale Recommender Systems, 2018
- Keep It Simple- Graph Autoencoders Without Graph Convolutional Networks, 2019
- StarSpace- Embed All The Things, 2018
- PyTorch BigGraph, 2019
- DynGEM- Deep Embedding Method for Dynamic Graphs, 2018
- Embedding-based Retrieval in Facebook Search, 2020
- General-Purpose User Embeddings based on Mobile App Usage, 2020
- PinText- A Multitask Text Embedding System in Pinterest, 2019
- Graph-RISE- Graph-Regularized Image Semantic Embedding, 2019
Papers
- Amazon-Recommendations
- The YouTube Video Recommendation System
- E-commerce in Your Inbox- Product Recommendations at Scale
- Meta-Prod2Vec - Product Embeddings Using Side-Information for Recommendation
- Deep neural network marketplace recommenders in online experiments, 2018
- Word2vec applied to Recommendation- Hyperparameters Matter, 2018
- Session-based Recommendations with RNNs
- Improved Recurrent Neural Networks for Session-based Recommendations
- Neural Attentive Session-based Recommendation
- STAMP Short-Term AttentionMemory Priority Model for Sessionbased Recommendation
- Streaming Session-based Recommendation, 2019
- Session-Based Recommendation with Graph Neural Networks
- Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
- Learning from History and Present- Next-item Recommendation via Discriminatively Exploiting User Behaviors
- Performance Comparison of Neural and Non-Neural Approaches to Session-based Recommendation, 2019
- Predictability Limits in Session-based Next Item Recommendation, 2019
- BERT4Rec- Sequential Recommendation with Bidirectional Encoder Representations from Transformer, 2019
- Controllable Multi-Interest Framework for Recommendation, 2020
Papers
- Collaborative Filtering for Implicit Feedback Datasets
- Probabilistic Matrix Factorization, 2008
- Fast Matrix Factorization for Online Recommendation with Implicit Feedback
- Incremental Learning for Matrix Factorization in Recommender Systems
- Large-Scale Matrix Factorization with Distributed Stochastic Gradient Descent
- A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems, 2015
- Collaborative Multi-Level Embedding Learning from Reviews for Rating Prediction, 2016
- Factorization Meets the Item Embedding- Regularizing Matrix Factorization with Item Co-occurrence, 2016
- Regularizing Matrix Factorization with User and Item Embeddings for Recommendation, 2018
- Deep Content-based Music Recommendation, 2013
- CB2CF- A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations, 2019
- AutoRec- Autoencoders Meet Collaborative Filtering, 2015
- Hidden Factors and Hidden Topics- Understanding Rating Dimensions with Review Text, 2013
- Collaborative Deep Learning for Recommender Systems, 2015
- Convolutional Matrix Factorization for Document Context-Aware Recommendation, 2016
- Joint Deep Modeling of Users and Items Using Reviews for Recommendation, 2017
- Neural Collaborative Filtering, 2017
- Deep Matrix Factorization Models for Recommender Systems, 2017
- Collaborative Knowledge Base Embedding for Recommender Systems, 2016
- Joint Representation Learning for Top N Recommendation with Heterogeneous Information Sources, 2017
- Deep Neural Networks for YouTube Recommendations, 2016
- TEM- Tree-enhanced Embedding Model for Explainable Recommendation, 2018
- MF/ATRank- An Attention-Based User Behavior Modeling Framework for Recommendation, 2017
- DKN- Deep Knowledge-Aware Network for News Recommendation, 2018
- Graph Convolutional Matrix Completion, 2018
- Variational Autoencoders for Collaborative Filtering
- PinnerSage- Multi-Modal User Embedding Framework for Recommendations at Pinterest, 2020
- SimClusters- Community-Based Representations for Heterogeneous Recommendations at Twitter, 2020
Papers
- Evaluating the Replicability of Significance Tests for Comparing Learning Algorithms, 2004
- Statistical Comparisons of Classifiers over Multiple Data Sets, 2006
- Power and Minumal Detectable Effect Notes
- Controlled experiments on the web- survey and practical guide, 2009
- Overlapping Experiment Infrastructure- More, Better, Faster Experimentation, 2010
- Online Controlled Experiments at Large Scale, 2013
- Improving the Sensitivity of Online Controlled Experiments by Utilizing Pre-Experiment Data, 2013
- Practical Aspects of Sensitivity in Online Experimentation with User Engagement Metrics, 2015
- Boosted Decision Tree Regression Adjustment for Variance Reduction in Online Controlled Experiments, 2016
- Applying the Delta Method in Metric Analytics- A Practical Guide with Novel Ideas, 2018, Notes
- Consistent Transformation of Ratio Metrics for Efficient Online Controlled Experiments, 2018
- Sequential Testing for Early Stopping of Online Experiments, 2015
- Diagnosing Sample Ratio Mismatch in Online Controlled Experiments- A Taxonomy and Rules of Thumb for Practitioners, 2019
- Machine Learning Methods for Estimating Heterogeneous Causal Effects, 2015
- Recursive partitioning for heterogeneous causal effects, 2016
- Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning, 2019
- Three Key Checklists and Remedies for Trustworthy Analysis of Online Controlled Experiments at Scale, 2019
Papers
Papers
- Do Deep Nets Really Need to be Deep?, 2014
- Distilling the Knowledge in a Neural Network, 2015
- Learning both Weights and Connections for Efficient Neural Networks, 2015
- Deep Compression, 2016
- A Survey of Model Compression and Acceleration for Deep Neural Networks, 2019
- The Lottery Ticket Hypothesis, 2019
Papers
- Approximate nearest neighbor algorithm based on navigable, 2014
- Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs
Papers
Papers
- AutoCross- Automatic Feature Crossing for Tabular Data in Real-World Applications
- Saliency Detection A Spectral Residual Approach
- Time-Series Anomaly Detection Service at Microsoft
- Attention Is All You Need, 2017
- MonoForest framework for tree ensemble analysis, 2019
- Intent-Based Browse Activity Segmentation, 2013
- Robust De-anonymization of Large Sparse Datasets, 2008
- Siamese Neural Networks for One-shot Image Recognition, 2015
- Deep Metric Learning Using Triplet Network, 2015
- Ten Simple Rules for Reproducible Research in Jupyter Notebooks, 2018
- Finding Users Who Act Alike- Transfer Learning for Expanding Advertiser Audiences, 2019
- Applying Deep Learning To Airbnb Search, 2018
- Improving Deep Learning For Airbnb Search, 2020
- Improving Recommendation Quality in Google Drive, 2020
- Initializing Bayesian Hyperparameter Optimization via Meta-Learning, 2015
- Auto-Sklearn 2.0- The Next Generation, 2020
- Optuna- A Next-generation Hyperparameter Optimization Framework, 2019
- MLOps: Continuous delivery and automation pipelines in machine learning, 2020
- COLD- Towards the Next Generation of Pre-Ranking System, 2020
- Learning to Create Better Ads- Generation and Ranking Approaches for Ad Creative Refinement, 2020