| Title | Year | Why read? | Russian notes |
|---|---|---|---|
| Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting | 2016 | computational advertising starter guide/survey, covers many topics, great starting point | vk |
- 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
- Item2Vec
- Neural Feature Embedding for User Response Prediction in Real-Time Bidding
- Search Retargeting using Directed Query Embeddings
- Real-time Personalization using Embeddings for Search Ranking at Airbnb
- DeepWalk
- 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
- 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
- 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
- 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
- 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
- Power and Minumal Detectable Effect Notes
- Controlled experiments on the web- survey and practical guide, 2009
- 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
- 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