The Bitfari Foundation is a research-based organization that actively promotes the advancement of marketing and advertising with new research and tools. This list, while not a comprehensive representation of the science behind Bitfari, can give you a taste of the kind of analysis we base our design decisions on. The Bitfari network employs a number of “computational social oracles” which are computer programs derived from marketing and advertising research in order to improve sales, reduce advertising costs, prevent fraud and improve customer service. The number of social oracles is intended to grow according to the BitfariDAO governance schedule. Meaning, that as AdTech science improves, so will Bitfari.
Please refer to the following list for papers on computational advertising. The full list of papers regularly cited by the foundation will also appear in Bitfari’s Github.
From Members of the Bitfari Foundation
- About Contextual Sales, Best Buy: Investing in Language Learning (2012)
- Bitfari: A Secure and Decentralized Network for Peer-to-Peer Advertising, (2021)
Whitepaper References
- Eades, Kenneth M.; Dickey, Keith; Ledesma, Jordhy; Frazier, Jennifer; Sider, Duane; Johnson, Melissa – Best Buy: Investing in Language Learning
- Mark Bonchek, Harvard Business Review – How Top Brands Pull Customers into Orbit
- Jamie Parfitt, 2012 – Citizen Advertising and the Transition Towards Consumer Collaboration
- Brand, J. (2013, March 5). Clarification of citizen advertising. (M. T. Choy, Interviewer)
- W. Feller, “An introduction to probability theory and its applications,” 1957.
- Satoshi Nakamoto, 2008 – Bitcoin: A Peer-to-peer Electronic Cash System
- Nick Szabo – Formalizing and Securing Relationships on Public Networks
- Gonçalo Pestana and others – THEMIS: Towards a Decentralized Ad Platform with Reporting Integrity (Part 1
- Thorsten Holz and others – The Dark Alleys of Madison Avenue: Understanding Malicious Advertisements
- Brave Software – Basic Attention Token (BAT) Whitepaper – Blockchain-Based Digital Advertising
Optimization Methods
- Google Vizier A Service for Black-Box Optimization
- Hogwild A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
- Parallelized Stochastic Gradient Descent
- A Review of Bayesian Optimization
- Taking the Human Out of the Loop- A Review of Bayesian Optimization
Topic Model
- Parameter estimation for text analysis
- Distributed Representations of Words and Phrases and their Compositionality
- Dirichlet Distribution, Dirichlet Process and Dirichlet Process Mixture(PPT)
Key Papers From Google
Factorization Machines
Embedding
- Word2vec Explained Negative-Sampling Word-Embedding Method (2014)
- Item2Vec-Neural Item Embedding for Collaborative Filtering (Microsoft 2016)
- Distributed Representations of Words and Phrases and their Compositionality (Google 2013)
- Word2vec Parameter Learning Explained (UMich 2016)
- Node2vec – Scalable Feature Learning for Networks (Stanford 2016)
- DeepWalk- Online Learning of Social Representations (SBU 2014)
- Real-time Personalization using Embeddings for Search Ranking at Airbnb (Airbnb 2018)
- Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba (Alibaba 2018)
- Efficient Estimation of Word Representations in Vector Space (Google 2013)
- LINE – Large-scale Information Network Embedding (MSRA 2015)
Budget Control
- Budget Pacing for Targeted Online Advertisements at LinkedIn
- Predicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-Side Platforms
- Real-Time Bid Optimization with Smooth Budget Delivery in Online Advertising
- Smart Pacing for Effective Online Ad Campaign Optimization
Tree Models
- Introduction to Boosted Trees
- Classification and Regression Trees
- Greedy Function Approximation A Gradient Boosting Machine
- Classification and Regression Trees
Guaranteed Contracts Ads
- A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising
- Pricing Guaranteed Contracts in Online Display Advertising
- Risk-Aware Dynamic Reserve Prices of Programmatic Guarantee in Display Advertising
- Pricing Guidance in Ad Sale Negotiations The PrintAds Example
- Risk-Aware Revenue Maximization in Display Advertising
Classic CTR Prediction
- Predicting Clicks – Estimating the Click-Through Rate for New Ads (Microsoft 2007)
- Field-aware Factorization Machines for CTR Prediction (Criteo 2016)
- Practical Lessons from Predicting Clicks on Ads at Facebook (Facebook 2014)
- Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (Alibaba 2017)
- Ad Click Prediction a View from the Trenches (Google 2013)
- Fast Context-aware Recommendations with Factorization Machines (UKON 2011)
Bidding Strategy
- Research Frontier of Real-Time Bidding based Display Advertising
- Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
- Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising
- Real-Time Bidding by Reinforcement Learning in Display Advertising
- Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget
- Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising
- Optimized Cost per Click in Taobao Display Advertising
- Real-Time Bidding Algorithms for Performance-Based Display Ad Allocation
- Deep Reinforcement Learning for Sponsored Search Real-time Bidding
Computational Advertising Architect
- Scaling Distributed Machine Learning with the Parameter Server
- Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
- A Comparison of Distributed Machine Learning Platforms
- Efficient Query Evaluation using a Two-Level Retrieval Process
- TensorFlow- A System for Large-Scale Machine Learning
- Parameter Server for Distributed Machine Learning
- Overlapping Experiment Infrastructure More, Better, Faster Experimentation
Machine Learning Tutorials
- Efficient Estimation of Word Representations in Vector Space
- Rules of Machine Learning- Best Practices for ML Engineering
- An introduction to ROC analysis
- Deep Learning Tutorial
Transfer Learning
- An Overview of Multi-Task Learning in Deep Neural Networks
- Scalable Hands-Free Transfer Learning for Online Advertising
- A Survey on Transfer Learning
Deep Learning CTR Prediction
- Deep & Cross Network for Ad Click Predictions (Stanford 2017)
- Deep Crossing – Web-Scale Modeling without Manually Crafted Combinatorial Features (Microsoft 2016)
- Product-based Neural Networks for User Response Prediction (SJTU 2016)
- Deep Interest Network for Click-Through Rate Prediction (Alibaba 2018)
- Entire Space Multi-Task Model – An Effective Approach for Estimating Post-Click Conversion Rate (Alibaba 2018)
- Wide & Deep Learning for Recommender Systems (Google 2016)
- Deep Learning over Multi-field Categorical Data (UCL 2016)
- A Factorization-Machine based Neural Network for CTR Prediction (HIT-Huawei 2017)
- Neural Factorization Machines for Sparse Predictive Analytics (NUS 2017)
Exploration and Exploitation
- Dynamic Online Pricing with Incomplete Information Using Multi-Armed Bandit Experiments
- Finite-time analysis of the Multiarmed Bandit Problem
- A Fast and Simple Algorithm for Contextual Bandits
- Customer Acquisition via Display Advertising Using MultiArmed Bandit Experiments
- Mastering the game of Go with deep neural networks and tree search
- Exploring compact reinforcement-learning representations with linear regression
- Incentivizing Exploration in Reinforcement Learning with Deep Predictive Models
- Bandit Algorithms Continued- UCB1
- A Contextual-Bandit Approach to Personalized News Article Recommendation(LinUCB)
- Exploitation and Exploration in a Performance-based Contextual Advertising System
- Thompson Sampling PPT
- Hierarchical Deep Reinforcement Learning- Integrating Temporal Abstraction and Intrinsic Motivation
- Exploration and Exploitation Problem by Wang Zhe
- Exploration exploitation in Go UCT for Monte-Carlo Go
- Using Confidence Bounds for Exploitation-Exploration Trade-offs