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Модель Похожих товаров и её приложения

Модель Похожих товаров и её приложения

Айгуль Газимова (Joom.ru, Junior Machine Learning Developer) @ Moscow Python №78

"Похожие товары – это важная часть для любой e-commerce платформы, а особенно для маркетплейсов. Они помогают покупателям найти лучший товар за меньшие деньги. Joom – международная группа e-commerce и финтех компаний, которая работает как с B2B, так и с B2C сегментами с одной основной командой разработки поиска. Мы поговорим про:
- построение Модели Похожих Товаров для Joom Marketplace (B2C-продукт);
- разработку Модели Похожих Товаров для JoomPro (B2B-продукт), где у нас мало пользовательской истории;
- как с помощью такой модели мы улучшили алгоритмы рекомендаций".

Видео: https://moscowpython.ru/meetup/78/similar-products-search/

Moscow Python Meetup
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July 14, 2022
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  1. SIMILAR PRODUCTS MODEL AND ITS’ APPLICATIONS Aigul Gazimova

  2. Aliexpress SHEIN Similar Products Model Amazon

  3. Joom Search Team ML engineers, Software engineers, Analytics

  4. About me AIGUL GAZIMOVA Bachelor’s degree in applied mathematics and

    computer science Cloud storage → eCommerce [email protected]
  5. JoomPro platform for crossborder wholesale trade International group of e-commerce

    and fintech companies Joom Marketplace platform for shopping from all over the world Onfy pharmaceutical marketplace in Germany Joompay fintech service for daily financial transactions in Europe Joom Logistics business that provides logistics, technology and infrastructure services for crossborder eCommerce
  6. Similar Products Model

  7. Similar products: idea of using Word2Vec device_id product_id1 … product_idn

  8. . . . device_id1 product_id1 … product_idn1 device_id2 product_id1 …

    product_idn2 device_idm product_id1 … product_idnm Similar products: idea of using Word2Vec
  9. Word2Vec: recap

  10. Center Center embedding and context embedding

  11. V(KINGS) - V(KING) ≈ V(QUEENS) - V(QUEEN) Interpretation of embeddings

  12. Issue: new products

  13. Issue: new products

  14. Image embeddings

  15. Customer experience

  16. Customer experience

  17. Customer experience

  18. Customer experience

  19. How to build the Similar Products Model if we lack

    of user activity history?
  20. How to build the Similar Products Model if we lack

    of user activity history? - word2vec approach
  21. How to build the Similar Products Model if we lack

    of user activity history? - word2vec approach
  22. How to build the Similar Products Model if we lack

    of user activity history? - word2vec approach - image-based embeddings
  23. How to build the Similar Products Model if we lack

    of user activity history? - word2vec approach - image-based embeddings
  24. How to build the Similar Products Model if we lack

    of user activity history? - word2vec approach - image-based embeddings to use content information for word2vec approach and to train the model on joom users’ history
  25. category id product name from EUR 12 Content Similar Products

    Model: product features
  26. Optimus Prime!!!

  27. category_id product name BOW BPE category emb name emb Transformer

    encoder center embedding context embedding product_id category_id BOW BOW emb categories emb + Content Similar Products Model: architecture
  28. category_id product name BOW BPE category emb name emb Transformer

    encoder center embedding context embedding product_id category_id BOW BOW emb categories emb + Content Similar Products Model: applications
  29. Category embeddings: 2D representation with TSNE

  30. Clusterization

  31. • Similar Products Model for B2C • Content Similar Products

    Model for B2B • Why it’s useful in e-commerce platforms Conclusion
  32. AIGUL GAZIMOVA j[email protected]

  33. BTW, we are hiring!