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Imortant Publications in Enterprise Data Science / ML / AI

What follows is a manually curated list of papers in modern data science and operations research that are worth reading.

  • We mainly focus on industrial reports, papers, and case studies, not purely theoretical works. Many entries are explitly tagged with [CompanyYear] prefix to provide a clearer picture of industrial adoption or affiliation.
  • Theoretical Foundations section focuses on papers that are most relevant in the context of operations research. There is no goal to create a comprehensive list of deep learning or reinforcement learning papers in general.

Customer Intelligence and Personalization

Representation Learning and Semantic Spaces

  1. [ Microsoft2015 ] Barkan O., Koenigstein N. -- Item2Vec: Neural Item Embedding for Collaborative Filtering
  2. [ Myntra2016 ] Arora S., Warrier D. -- Decoding Fashion Contexts Using Word Embeddings, 2016
  3. [ Rakuten2016 ] Phi V., Chen L., Hirate Y. -- Distributed Representation-based Recommender Systems in E-commerce, 2016
  4. [ RTBHouse2016 ] Zolna K., Bartlomiej R. -- User2vec: user modeling using LSTM networks, 2016
  5. [ MediaGamma2017 ] Stiebellehner S., Wang J, Yuan S. -- Learning Continuous User Representations through Hybrid Filtering with doc2vec, 2017
  6. [ Yandex2018 ] Seleznev N., Irkhin I., Kantor V. -- Automated extraction of rider’s attributes based on taxi mobile application activity logs, 2018
  7. [ BBVA2018 ] Baldassini L., Serrano J. -- client2vec: Towards Systematic Baselines for Banking Applications
  8. [ Santander2018 ] Mancisidor R., Kampffmeyer M., Aas K., Jenssen R. -- Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder, 2018
  9. [ Zalando2017 ] Lang T., Rettenmeier M. -- Understanding Consumer Behavior with Recurrent Neural Networks, 2017
  10. [ Facebook2017 ] Wu L., Fisch A., Chopra S., Adams K., Bordes A. and Weston J. -- StarSpace: Embed All The Things!, 2017
  11. Netzer O., Lattin J., Srinivasan V. -- A Hidden Markov Model of Customer Relationship Dynamics, 2008

Personalized Recommendations, Ads, and Promotions

Basic Methods

  1. [ BookingCom2019 ] Bernardi L., Mavridis T., Estevez P. -- 150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com, 2019
  2. [ Amazon2003 ] Linden G., Smith B., and York J. -- Amazon.com Recommendations: Item-to-Item Collaborative Filtering, 2003
  3. [ Netflix2009 ] Koren Y. -- The BellKor Solution to the Netflix Grand Prize, 2009
  4. [ Netflix2009 ] Koren Y., Bell R., and Volinsky C. -- Matrix Factorization Techniques for Recommender Systems, 2009
  5. Pfeifer P., Carraway R. -- Modeling Customer Relationships as Markov Chains, 2000
  6. Rendle S. -- Factorization Machines, 2010

Reinforcement Learning Methods

  1. [ Facebook2019 ] Gauci J., et al -- Horizon: Facebook's Open Source Applied Reinforcement Learning Platform, 2019
  2. [ Adobe2015 ] G. Theocharous, P. Thomas, and M. Ghavamzadeh -- Personalized Ad Recommendation Systems for Life-Time Value Optimization with Guarantees, 2015
  3. [ Criteo2018 ] Rohde D., Bonner S., Dunlop T., Vasile F., Karatzoglou A. -- RecoGym: A Reinforcement Learning Environment for the Problem of Product Recommendation in Online Advertising, 2018
  4. [ Spotify2018 ] McInerney J., Lacker B., Hansen S., Higley K., Bouchard H., Gruson A., Mehrotra R. -- Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits, 2018
  5. [ Google2018 ] Chen M., Beutel A., Covington P., Jain S., Belletti F., Chi E. -- Top-K Off-Policy Correction for a REINFORCE Recommender System, 2018
  6. [ Yahoo2010 ] Li L., Chu W., Langford J., Schapire R. -- A Contextual-Bandit Approach to Personalized News Article Recommendation, 2010

Deep Learning Methods

  1. [ Airbnb2019 ] Du G. -- Discovering and Classifying In-app Message Intent at Airbnb, 2019
  2. [ Google2016 ] Covington P., Adams J., Sargin E. -- Deep Neural Networks for YouTube Recommendations, 2016
  3. [ Netflix2016 ] Hidasi B., Karatzoglou A., Baltrunas L., Tikk D. -- Session-based Recommendations with Recurrent Neural Networks, 2016
  4. [ Google2017 ] Wu C., Ahmed A., Beutel A., Smola A., Jing H. -- Recurrent Recommender Networks, 2017
  5. [ Snap2018 ] Yang C., Shi X., Luo J. and Han J. -- I Know You'll Be Back: Interpretable New User Clustering and Churn Prediction on a Mobile Social Application, 2018
  6. Zhang S., Yao L., Sun A., Tay Y. -- Deep Learning based Recommender System: A Survey and New Perspectives, 2019

Deep Graph Learning Methods

  1. [ Pinterest2018 ] Ying R., He R., Chen K., Eksombatchai P., Hamilton W., Leskovec J. -- Graph Convolutional Neural Networks for Web-Scale Recommender Systems, 2018
  2. [ Pinterest2017 ] Eksombatchai C., Jindal P., Liu J., Liu Y., Sharma R., Sugnet C., Ulrich M., Leskovec J. -- Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time, 2017
  3. [ Uber2019 ] Jain A., Liu I., Sarda A., and Molino P. -- Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations, 2019

Evaluation and Measurement

  1. [ Netflix2018 ] Steck H. -- Calibrated Recommendations, 2018
  2. Chaney A., Stewart B., Engelhardt B. -- How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility, 2017

Channel Attribution, Marketing Spend Optimization, and Ad Bidding

  1. [ Adobe2018 ] N. Li, S. K. Arava, C. Dong, Z. Yan, and A. Pani -- Deep Neural Net with Attention for Multi-channel Multi-touch Attribution, 2018
  2. [ Miaozhen2018 ] Ren K., Fang Y., Zhang W., Liu S., Li J., Zhang Y., Yu Y., and Wang J. -- Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising, 2018
  3. [ Alibaba2018 ] D. Wu, X. Chen, X. Yang, H. Wang, Q. Tan, X. Zhang, J. Xu, and K. Gai -- Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising, 2018
  4. [ Alibaba2018] Zhao J., Qiu G., Guan Z., Zhao W. and He X. -- Deep Reinforcement Learning for Sponsored Search Real-time Bidding, 2018
  5. [ iProspect2004 ] Kitts B., Leblanc B. -- Optimial Bidding on Keyword Auctions, 2004
  6. [ Dstillery2012 ] Dalessandro B., Perlich C., Stitelman O., Provost F. -- Causally motivated attribution for online advertising, 2012
  7. [ TurnInc2011 ] Shao X., Li L. -- Data-driven Multi-touch Attribution Models, 2011
  8. [ IntegralAds2015 ] Hill D., Moakler R., Hubbard A., Tsemekhman V., Provost F., Tsemekhman K. -- Measuring Causal Impact of Online Actions via Natural Experiments: Application to Display Advertising, 2015
  9. [ Google2017 ] Jin Y., Wang Y., Sun Y., Chan D., Koehler J. -- Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects, 2017

Identity Resolution and Cross-Device Tracking

  1. Phan M., Tay Y., Pham T. -- Cross Device Matching for Online Advertising with Neural Feature Ensembles: First Place Solution at CIKM Cup 2016, 2017
  2. Karakaya C., Toguc H., Kuzu R., Buyuklu A. -- Survey of Cross Device Matching Approaches with a Case Study on a Novel Database, 2018

Price Management and Optimization

Demand Analysis and Forecasting

  1. [ CVS2007 ] Ailawadi K., Harlam B., César J., Trounce D. -- Quantifying and Improving Promotion Effectiveness at CVS, 2007
  2. [ Lexus2010 ] van Heerde H., Srinivasan S., Dekimpe M. -- Estimating Cannibalization Rates for Pioneering Innovations, 2010
  3. [ AlbertHeijn2006 ] Kök A., Fisher M. -- Demand Estimation and Assortment Optimization Under Substitution: Methodology and Application, 2006
  4. [ Uber2017 ] Zhu L., Laptev N. -- Deep and Confident Prediction for Time Series at Uber, 2017
  5. [ Uber2017 ] Laptev N., Yosinski J., Li L., Smyl S. -- Time-series Extreme Event Forecasting with Neural Networks at Uber, 2017
  6. [ Google2013 ] Scott S., Varian H. -- Predicting the Present with Bayesian Structural Time Series, 2013
  7. Rodrigues F., Markou I., Pereira F. -- Combining Time-Series and Textual Data for Taxi Demand Prediction in Event Areas: A Deep Learning Approach, 2018
  8. Ghobbar A., Friend C. -- Evaluation of Forecasting Methods for Intermittent Parts Demand in the Field of Aviation: A Predictive Model, 2002

Dynamic Pricing

  1. [ Groupon2017 ] Cheung W., Simchi-Levi D., and Wang H. -- Dynamic Pricing and Demand Learning with Limited Price Experimentation, 2017
  2. [ Harward2017 ] Ferreira K., Simchi-Levi D., and Wang H. -- Online Network Revenue Management Using Thompson Sampling, November 2017
  3. [ Walmart2018 ] Ganti R., Sustik M., Quoc T., Seaman B. -- Thompson Sampling for Dynamic Pricing, February 2018
  4. [ RueLaLa2015 ] Ferreira K. J., Lee B., and Simchi-Levi D. -- Analytics for an Online Retailer: Demand Forecasting and Price Optimization, November 2015
  5. [ Airbnb2018 ] Srinivasan S. -- Learning Market Dynamics for Optimal Pricing, 2018
  6. [ Uber2017 ] Chen L. -- Measuring Algorithms in Online Marketplaces, 2017
  7. [ Amazon2016 ] Chen L., Mislove A., Wilson C. -- An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace, 2016

Macroeconomic Impact of Algorithmic Pricing

  1. Cavallo A. -- More Amazon Effects: Online Competition and Pricing Behaviors, 2018

Inventory and Supply Chain Management

  1. Oroojlooyjadid A., Snyder L., Takáč M. -- Applying Deep Learning to the Newsvendor Problem, 2018
  2. Kemmer L., et al. -- Reinforcement learning for supply chain optimization, 2018
  3. [ Microsoft2023 ] Beibin L., et al. -- Large Language Models for Supply Chain Optimization, 2023

Search

  1. Radford A., et al. -- Learning Transferable Visual Models From Natural Language Supervision, 2021

Theoretical Foundations

Foundations of Reinforcement Learning

  1. Russo D., Roy B., Kazerouni A., Osband I., and Wen Z. -- A Tutorial on Thompson Sampling, November 2017
  2. Riedmiller M. -- Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method, 2005
  3. Mnih V., et al. -- Human-level Control Through Deep Reinforcement Learning, 2015
  4. Silver D., Lever G., Heess N., Degris T., Wierstra D., Riedmiller M. -- Deterministic Policy Gradient Algorithms, 2014
  5. Lillicrap T., Hunt J., Pritzel A., Heess N., Erez T., Tassa Y., Silver D., Wierstra D. -- Continuous Control with Deep Reinforcement Learning, 2015
  6. Hessel M, et al. -- Rainbow: Combining Improvements in Deep Reinforcement Learning, 2017

Reinforcement Learning in Operations

  1. Bello I., Pham H., Le Q., Norouzi M., Bengio S. -- Neural Combinatorial Optimization with Reinforcement Learning, 2017

Foundation of Deep Learning

  1. Hochreiter S., Schmidhuber J. -- Long short-term memory, 1997
  2. Mikolov T., Chen K., Corrado G., Dean J. -- Efficient Estimation of Word Representations in Vector Space, 2013
  3. Le Q., Mikolov T. -- Distributed Representations of Sentences and Documents, 2014
  4. Sutskever I., Vinyals O., Le Q. -- Sequence to Sequence Learning with Neural Networks, 2014
  5. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A., Kaiser L., Polosukhin I. -- Attention Is All You Need, 2017

Books

Customer Intelligence

  1. Winston W. -- Marketing Analytics: Data-Driven Techniques with Microsoft Excel, Wiley, 2014
  2. Grigsby M. -- Advanced Customer Analytics: Targeting, Valuing, Segmenting and Loyalty Techniques, Kogan Page, 2016
  3. Katsov I. -- Introduction to Algorithmic Marketing, 2017
  4. Falk K. -- Practical Recommender Systems, Manning, 2019

Price Management

  1. Simon H., FassnachtM. -- Price Management: Strategy, Analysis, Decision, Implementation, Springer, 2018
  2. Talluri K., van Ryzin G. -- The Theory and Practice of Revenue Management, Springer, 2004
  3. Smith T. -- Pricing Strategy: Setting Price Levels, Managing Price Discounts and Establishing Price Structures, Cengage Learning, 2011
  4. Phillips R. -- Pricing and Revenue Optimization, Stanford Business Books, 2005

Supply Chain

  1. Fisher M., Raman A. -- The New Science of Retailing: How Analytics are Transforming the Supply Chain and Improving Performance, Harvard Business Review Press, 2010
  2. Jacobs R., Berry W., Whybark D., Vollmann T. -- Manufacturing Planning and Control for Supply Chain Management, McGraw-Hill Education, 2018
  3. Vandeput N. -- Data Science for Supply Chain Forecast, 2018

Econometrics

  1. Shumway R., Stoffer D. -- Time Series Analysis and Its Applications: With R Examples, Springer, 2017
  2. Mills T. -- Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting, Academic Press, 2019

Data Science and Machine Learning for Enterprise Use Cases

  1. Provost F., Fawcett T. -- Data Science for Business, O'Reilly Media, 2013
  2. Osinga D. -- Deep Learning Cookbook, O'Reilly Media, 2018
  3. Molnar C. -- A Guide for Making Black Box Models Explainable, 2020
  4. Katsov I. -- The Theory and Practice of Enterprise AI, 2022