ML meets Economics
- implicit: full data & model pipeline, article
- LightFM: article
- How to build Item2Vec (or W2V) for item recommendations in retail
- OK.ru: graph based recsys, article
- OK.ru: Neural item recommendations with cold start, article
- HH.ru: classic 2 level model of search at hh.ru, article
- Okko competition: classic 2 level model, article
- Yandex.Dzen: fit ALS -> fit Catboost on warm embeddings to predict warm&cold embeddings, 15-25min in video
- TikTok: No use of popularity features! post
- Instagram: Insights on candidate generation articles
- DoorDash: Store2Vec as a feature in recommendations
- Pinterest: Multi-taste user embeddings
- AirBnb: Hotel2Vec with novel positive samples approach
- HRNN, Temporal-Contextual Recommendation in Real-Time
- How to use W2V and FastText for search: Query2Vec
- Avito: FAISS for fast similar embedding search
- Similar vectors search with Nmslib (HNSW - hierarchical navigable small world), FAISS (embeddings space K-means clustering + Product quantizer) and Annoy (divides embeddings space with a binary tree)
- ElasticSearch basics
- DoorDash Elasticsearch meets logistic regression
- Avito: Recommending additional item - upsell with advanced W2V
- Directly optimizing Uplift in recsys by change in target
- Avito Multi-armed bandits for item2item recommendations
- RecSys 2018 paper on multi-armed badits for explainable recommendations
- Pinterest: Personal notification volume optimization
- Uber.Eats: Multi objective optimisation in recsys, article
- Avito: Multi-objective optimization in search
- VK: Directly optimizing business metrics
- GlowByte Consulting: Customer communication chains optimization with RL
- Measure user surprise by serendipidy metric
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent