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recsys-tutorial

ML meets Economics

Tutorials:

  • implicit: full data & model pipeline, article
  • LightFM: article
  • How to build Item2Vec (or W2V) for item recommendations in retail

Classic RecSys:

  • 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

SOTA

  • HRNN, Temporal-Contextual Recommendation in Real-Time

Search

  • 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

Upsell

  • Avito: Recommending additional item - upsell with advanced W2V

Uplift

Optimization

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