Boosting Product Categorization with Machine Learning Models
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Product categories are the structural backbone of every online shop. To attract customers and facilitate navigation, categories need to be easily understandable, logical and consistent. With the explosive growth of data, it is becoming more and more difficult for retailers to match products to appropriate categories, and large product catalogs as well as the need to quickly adapt to changes often lead to costly misclassifications. In this talk, Amadeus presents the approach at commercetools to build a category recommendation system using methods from machine learning. He talks about the use of deep neural nets and transfer learning to build an image classifier, word2vec and tf-idf to build a text classifier, and how to integrate these models in a REST API.
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Boosting Product Categorization with Machine Learning Models
- 1. Amadeus Magrabi @amadeusmagrabi BOOSTING PRODUCT CATEGORIZATION WITH MACHINE LEARNING
- 2. … 11/2017 2 Company: Customers: People who want to sell something online … Main product: REST API to manage online shops
- 4. 11/2017 4 Goal: Use machine 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 online store has a different category structure. Challenges Fashion Men Women Jeans Clothing Pants Shirts Shoes Store 1 Store 2
- 6. 11/2017 6 Challenge: Every online store 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 online store 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 online store 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: Product data 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: Very large 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 of Approach 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 for Product Names Model for Product Descriptions Model for Product Descriptions
- 20. 11/2017 20 Category Matching Model categories 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. 11/2017 21 REST API General API
- 24. 11/2017 24 Thank you! Amadeus Magrabi @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