Download as PDF, PPTX
Uploaded byDataconomy Media
DN 2017 | Boosting Product Categorization with Machine Learning | Amadeus Magrabi | commercetools
The document outlines a machine learning approach to automatically recommend product categories for online shops, addressing challenges like varying category structures across stores. It employs a convolutional neural network and various preprocessing techniques for product data, including text normalization and vectorization methods like TF-IDF and word2vec. The goal is to improve categorization accuracy and flexibility, enabling better category recommendations even for stores with limited data.
More Related Content
Similar to DN 2017 | Boosting Product Categorization with Machine Learning | Amadeus Magrabi | commercetools
![[DSC Europe 22] Developing Visual AI Solutions for Online Marketplaces - Mlad...](https://cdn.slidesharecdn.com/ss_thumbnails/mladenfernezir-developingvisualaisolutionsforonlinemarketplaces-221129232505-8166f0c6-thumbnail.jpg?width=640&height=640&fit=bounds)
DN 2017 | Boosting Product Categorization with Machine Learning | Amadeus Magrabi | commercetools
- 1. Amadeus Magrabi @amadeusmagrabi BOOSTING PRODUCTCATEGORIZATION WITH MACHINE LEARNING
- 2. … 11/2017 2 Company: Customers: Peoplewho want to sell something online … Main product: REST API to manage online shops
- 3.
- 4. 11/2017 4 Goal: Usemachine learning to automatically recommend categories for products Machine Learning for Category Recommendations Fashion Men Women Sports Shoes Pants Business
- 5. 11/2017 5 Challenge: Every onlinestore has a different category structure. Challenges Fashion Men Women Jeans Clothing Pants Shirts Shoes Store 1 Store 2
- 6. 11/2017 6 Challenge: Every onlinestore has a different category structure. Challenges Fashion Men Women Jeans Clothing Pants Shirts Shoes Store 1 Store 2 Store 3 Model 1 Model 2 Model 3 predictpredictpredict Option 1: multiple store-specific models Store 1 Store 2
- 7. 11/2017 7 Challenge: Every onlinestore has a different category structure. Challenges Fashion Men Women Jeans Clothing Pants Shirts Shoes Store 1 Store 2 Store 3 Model 1 Model 2 Model 3 predictpredictpredict General Categories Model 1 predict match Store 3Store 2Store 1 match match Option 1: multiple store-specific models Option 2: one general model Store 1 Store 2
- 8. 11/2017 8 Challenge: Every onlinestore has a different category structure. Challenges Fashion Men Women Jeans Clothing Pants Shirts Shoes Store 1 Store 2 Store 3 Model 1 Model 2 Model 3 predictpredictpredict General Categories Model 1 predict match Store 3Store 2Store 1 match match Option 1: multiple store-specific models Option 2: one general model • Better accuracies for stores with very specific categories • No category matching necessary • More data-per-model • More flexible • Easier to deploy • Also works for stores with little data • Can also recommend categories that are not yet defined in the store Store 1 Store 2
- 9. 11/2017 9 Challenge: Productdata is diverse and unbalanced, which complicates feature selection. Challenges Approach: → Focus on features names, images and descriptions • carry most information • available for most products • Product names • Images • Prices • Descriptions • Sizes • Brands • Colors • Expiration Dates • …
- 10. 11/2017 10 Challenge: Verylarge class set • Amazon/Ebay have listed 50000+ categories • Tradeoff: Coverage vs. Accuracy Challenges Approach: → select broad model categories to cover main use cases → rely on category matching procedure to catch more specialized categories → current version has a selection of 723 model categories
- 11. 11/2017 11 Overview ofApproach 723 General Categories Name Model Description Model predict match predict Store 3Store 2Store 1 match match Image Model predict
- 12. 11/2017 12 • Model:Convolutional Neural Network (Deep Learning) • Similar to mechanisms in the brain: Idea of building complex representations by combining simple representations Model for Product Images
- 13. 11/2017 13 • Model:Convolutional Neural Network (Deep Learning) • Similar to mechanisms in the brain: Idea of building complex representations by combining simple representations • Trained via transfer learning on famous image recognition network Inception v3 (TensorFlow, Google Cloud ML Engine) Model for Product Images
- 14. 11/2017 14 Preprocessing: (spacy,re, gensim, Google Translate, pyenchant) • spellchecker • translation • tokenization • normalization • lemmatization • phrasing • word removal Model for Product Names Examples: “Mens Heavyweight 6.1-ounce, 100% cotton T-Shirts in Regular, Big and Tall Sizes” “Gala Apples Fresh Fruit, 3 LB Bag” “Carhartt Men's Maddock Pocket T-Shirt Size M” “Samsung SM-G900V - Galaxy S5 - 16GB Android Smartphone Verizon + GSM - Black” (smartwathc → smartwatch) (German → English) (complete names → words) (lowercasing, deleting special characters) (apples → apple) (louis vuitton → louis_vuitton) (stop words, blacklist)
- 15. 11/2017 15 Preprocessing: (spacy,re, gensim, Google Translate, pyenchant) • spellchecker • translation • tokenization • normalization • lemmatization • phrasing • word removal Model for Product Names Examples: “Mens Heavyweight 6.1-ounce, 100% cotton T-Shirts in Regular, Big and Tall Sizes” “Gala Apples Fresh Fruit, 3 LB Bag” “Carhartt Men's Maddock Pocket T-Shirt Size M” “Samsung SM-G900V - Galaxy S5 - 16GB Android Smartphone Verizon + GSM - Black” (smartwathc → smartwatch) (German → English) (complete names → words) (lowercasing, deleting special characters) (apples → apple) (louis vuitton → louis_vuitton) (stop words, blacklist) Models: (scikit-learn) • Logistic Regression • Naive Bayes • Random Forest • XGBoost • Support Vector Machine
- 16. 11/2017 16 Vectorization methods:(text → numbers) bag-of-words: • Simple approach, but sparse representation and blind to context Model for Product Names
- 17. 11/2017 17 Vectorization methods:(text → numbers) bag-of-words: • Simple approach, but sparse representation and blind to context Model for Product Names tf-idf: • Similar to bag-of-words, but weighs words higher when they do not occur frequently in dataset • Intuition: “the” has less predictive value than “iPhone” • TF(w) = (number of times word appears in name) / (total number of words in name) • IDF(w) = log_e(total number of names / number of names with word w in it)
- 18. 11/2017 18 Vectorization methods:(text → numbers) bag-of-words: • Simple approach, but sparse representation and blind to context Model for Product Names word2vec: • Trains two-layer neural network that predicts context words of a word • Results in a dense and context-sensitive representation tf-idf: • Similar to bag-of-words, but weighs words higher when they do not occur frequently in dataset • Intuition: “the” has less predictive value than “iPhone” • TF(w) = (number of times word appears in name) / (total number of words in name) • IDF(w) = log_e(total number of names / number of names with word w in it)
- 19. 11/2017 19 Model forProduct Names Model for Product Descriptions Model for Product Descriptions
- 20. 11/2017 20 Category Matching Modelcategories are matched to store-specific categories via a word2vec model trained on a news dataset word2vec similarity 723 General Categories Name Model Description Model predict match predict Store 3Store 2Store 1 match match Image Model predictaveraging class probabilities
- 21.
- 22.
- 23.
- 24. 11/2017 24 Thank you! AmadeusMagrabi @amadeusmagrabi amadeus.magrabi@commercetools.com word2vec similarity 723 General Categories Name Model Description Model predict match predict Store 3Store 2Store 1 match match Image Model predictaveraging class probabilities Tensorflow, Inception tf-idf, LogReg tf-idf, LogReg