Recommender Systems and Product Semantics

1. Recommender Systems and Product Semantics Rayid Ghani & Andy FanoAccenture Technology Labs Workshop on Recommendation & Personalization in E-CommerceMay 28, 2002

2. Who we are?Accenture Technology Labs • R&D Group for Accenture • ~ 40 researchers in Chicago, Palo Alto (California) and Sophia Antipolis (France) • Research in Data Mining, Machine Learning, Ubiquitous Computing, Wearable Computing, Language Technologies, Virtual & Augmented Reality, Collaborative Workspaces…

3. What Does a Transaction Mean? Terabytes of transaction data. But what does any one transaction mean? What does it tell us about the customer?

4. Example: Apparel Transactional information captured by retailers: • Date of Purchase • SKU • Price • Size • Brand But what does this tell me about the customer who bought it?

5. Product Semantics:What does a product mean? What does this shirt say about her? Is it conservative or flashy? Trendy or classic? Formal or casual? Where would we get this information?

6. Where do people get this information?Marketing Product Companies and Retailers spend fortunes telling customers what their products mean. Our idea: Build a system that analyzes marketing texts to infer these attributes.

7. Example From the Macy’s web site: DKNY Jeans Ruched Side-Tie Tee Get back tobasicswith afreshnew lookthis season. The Ruched Side-Tie Tee has adrawstringtie at lefthipwithshirreddetail down the side.Stretchprovides aflattering, shapelyfit. V-neck.

8. Product Descriptions Domain Experts SupervisedLearning Algorithm Learned Statistical Models Product descriptionsmarked up with attribute values Training the System

9. Inferring Attributes via Text Classification • Build one classifier per attribute type • Simple statistical classifier – Naïve Bayes Multinomial model (McCallum & Nigam 1998) • For all words (description) and attribute values: • calculate P(word | attribute value) using the manually rated items • Given a new item description: • Calculate P(attribute value | item description) for all attribute values • Use Maximum Likelihood

10. Semi-supervised Learning • Lot of product descriptions available for minimal cost • Labeling them is expensive • Apply magical algorithms that combine labeled and unlabeled data for classification • EM (Nigam et al. 1999), Co-Training (Blum & Mitchell 1999), Co-EM (Nigam & Ghani), ECo-Train (Ghani, 2002)

11. Probabilistically add to labeled data The EM Algorithm Estimate labels Learn from labeled data Naïve Bayes

12. A Peek at the Learned Models Lauren Single-Breasted BlazerSporty elegance and classic Gatsby-esque styling are captured in this impeccably designed single-breasted, three-button blazer from Lauren by Ralph Lauren. With traditional notch collar, signature button hardware, front flap pockets, and signature crest on left breast pocket. Bias Slip DressThe perfect black dress gets flirty and feminine in the bias-cut slip dress with sheer ruffled cap sleeves. A low, scoop neck and back is ultra-flattering while a draped, romantic fit reveals total elegance.

13. A Peek at the Learned Models Polo Jeans Co. Muscle Logo TeeStrut your stuff in the Muscle Logo Tee. Flattering on the arms with a close-to-the-body fit, classic crewneck and shimmery logo print with stars. A sporty new basic for your tee collection.

14. A Peek at the Learned Models ABS by Allen Schwartz Asymmetrical Dress Just for the party girl with a big feminine streak. A ruffled one-shoulder cuts diagonally across the front and back. Accented with a rhinestone detail on the shoulder.

15. A Peek at the Learned Models DKNY Jeans Jrs. Mesh Jersey SweaterAn innovative take on the football jersey, the see-through mesh sweater is a fashion favorite among the sporty set. Denim appliqué

16. Product descriptionsautomatically marked up with attribute values NewProduct Descriptions Learned Statistical Models Product Semantics Knowledge Base Populating the Knowledge Base

17. Recommender System Retailer’sWeb Site Learned Statistical Models ExtractedDescriptions of Products Browsed EvolvingUser Profile Query the Knowledge Base forMatching Products Recommend Matching Products to User Product Semantics Knowledge Base

23. Advantages over Traditional Recommendation Systems This approach provides us some of the underlying attributes that characterize a customer’s preference. We can therefore begin to explain the preference rather than simply rely on the co-occurrence of purchases (e.g. people who bought x also bought y). This helps with: • Handling new products/rapidly changing products • Low Frequency Products • Cross Category Recommendations

24. Cross-Category Recommendations • Difficult for collaborative filtering and content-based systems • Build a model of the user - personality, stylistic attributes • Taste in clothing might also be suggestive of taste in other products, say furniture and home decoration • Create models for different product classes and create mappings among these models

25. Summary • “Understand” a product and hence the customer • Use Text Learning (supervised and semi-supervised) to abstract from product (description) to subjective, domain-specific features • Effective for new (and low frequency) products and for cross-category recommendations