TensorHouse is a collection of reference machine learning and optimization models for enterprise operations: marketing, pricing, supply chain, and more. The goal of the project is to provide baseline implementations for industrial, research, and educational purposes.
This project contains the follwoing resources:
- a well-documented repository of reference model implementations,
- a manually curated list of important papers in modern operations research,
- a manually curated list of public datasets related to entrerpirse use cases.
Strategic price optimization using reinforcement learning
- Promotions and Advertisements
- Campaign/Channel Attribution using Adstock Model
- Customer Lifetime Value (LTV) Modeling using Markov Chain
- Next Best Action Model using Reinforcement Learning (Fitted Q Iteration)
- Multi-touch Multi-channel Attribution Model using Deep Learning (LSTM with Attention)
- Search
- Latent Semantic Analysis (LSA)
- Recommendations
- Nearest Neighbor User-based Collaborative Filtering
- Nearest Neighbor Item-based Collaborative Filtering
- Item2Vec Model using NLP Methods (word2vec)
- Customer2Vec Model using NLP Methods (doc2vec)
- Pricing and Assortment
- Markdown Price Optimization
- Dynamic Pricing using Thompson Sampling
- Dynamic Pricing with Limited Price Experimentation
- Price Optimization using Reinforcement Learning (DQN)
- Supply Chain
- Multi-echelon Inventory Optimization using Reinforcement Learning (DDPG, TD3)
- The most basic models come from Introduction to Algorithmic Marketing book. Book's website - https://algorithmicweb.wordpress.com/
- More advanced models use deep learning techniques to analyze event sequences (e.g. clickstream) and reinforcement learning for optimization (e.g. safety stock management policy)
- Almost all models are based on industrial reports and real-life case studies
- Ilya Katsov
- Dmytro Zikrach