Inferring networks of substitute and complementary products
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1. Jure Leskovec presented on using product graphs to model relationships between products like substitutes and complements. The graphs are built by analyzing product descriptions, reviews, and other data to discover topics and infer relationships. 2. The product graphs understand substitute and complement goods and can generate explanations for why people prefer certain products. They also discover micro-categories of tightly related products. 3. Jure discussed how Pinterest is applying similar techniques to discover relationships between pins/objects in order to improve recommendations.
![Approach
Products are described by their properties:
Review text, Product description,
Brand, Price, …
[0.3, 0, 0, 0.3, 0, 0, 0.2, 0, 0, 0.1]
[0.1, 0, 0, 0, 0.2, 0, 0, 0.1, 0, 0.2]
Challenges:
How do we discover right features?
How do we explain relationships?
How do we identify micro-categories?
16
Shoes Female
Jure Leskove, Pinterest & Stanford University](https://image.slidesharecdn.com/prodgraph-dato-jul15-150726182753-lva1-app6892/85/Inferring-networks-of-substitute-and-complementary-products-16-320.jpg)
Inferring networks of substitute and complementary products
- 1. Jure Leskovec (@jure) Pinterest and Stanford 1Jure Leskove, Pinterest & Stanford University
- 2. 2 Recommendations drive whole businesses! Jure Leskove, Pinterest & Stanford University
- 3. People and Items 3 + – + + + – People Items Fundamental problem: Making items discoverable!Jure Leskove, Pinterest & Stanford University
- 4. Understanding Products To make relevant recommendations we need to understand the products and how they fit together Discovering relationships between products 4Jure Leskove, Pinterest & Stanford University
- 5. Product Graph Ingest product catalogs: 10s of millions of products 100s of millions of descriptions, reviews Infer product networks with multiple types of directed relationships: Input: Data about items (products) Output: Network with multiple types of relationships 5Jure Leskove, Pinterest & Stanford University
- 6. Product Graph: Relations 6 Substitutes: Purchase instead Complements: Purchase in addition Jure Leskove, Pinterest & Stanford University
- 7. Product Graph: Description 7 : cleaner; quieter : cheaper; high power : well made, easy to install : fits perfectly, great value Jure Leskove, Pinterest & Stanford University
- 8. Product Graph: Overview 8 substitute complement Jure Leskove, Pinterest & Stanford University
- 9. Product Graph:What it does? 1. Understands the notions of substitute and complement goods is substitutable for complements 9Jure Leskove, Pinterest & Stanford University
- 10. Product Graph:What it does? 2. Generates explanations of why certain products are preferred “Good quality, soft, light weight, the colors are beautiful and exactly like the picture!” People prefer this because: 10Jure Leskove, Pinterest & Stanford University
- 11. Product Graph:What it does? 3. Discovers micro-categories of products Small clusters of tightly related products: 11Jure Leskove, Pinterest & Stanford University
- 12. Product Graph:What it does? 4. Recommends baskets of related products Query: Suggested outfit: Query: Suggested outfit: 12Jure Leskove, Pinterest & Stanford University
- 13. Product Graph: Overview Building networks from products Modeling: Can we use product data to model product relationships? Understanding: Can we explain why people prefer certain products over others? 13Jure Leskove, Pinterest & Stanford University
- 14. Problem Setting Binary prediction task: Given a pair of products, x and y, predict whether they are related (substitute/complementary) Goal: Build a probabilistic model that encodes 14Jure Leskove, Pinterest & Stanford University
- 15. Problem Setting How to learn from data Train by maximum likelihood: 15 XComplementary Not Complementary Jure Leskove, Pinterest & Stanford University
- 16. Approach Products are described by their properties: Review text, Product description, Brand, Price, … [0.3, 0, 0, 0.3, 0, 0, 0.2, 0, 0, 0.1] [0.1, 0, 0, 0, 0.2, 0, 0, 0.1, 0, 0.2] Challenges: How do we discover right features? How do we explain relationships? How do we identify micro-categories? 16 Shoes Female Jure Leskove, Pinterest & Stanford University
- 17. Our Solution: SCEPTRE Link Prediction Review “topics” Discover topics that “explain” product relations 17 Learn to discover topics that explain the product graph Jure Leskove, Pinterest & Stanford University
- 18. Challenges: Relation Direction why do people who view X eventually buy Y? Relationships we want to learn are not symmetric 18 Relationships: Explained by product “properties” “baby, pajamas, pants, colorful” Directedness: Subjective/qualitative language “true size, fits well, items are the same color as on the picture” Jure Leskove, Pinterest & Stanford University
- 19. Challenges: Multiple Relations 19 We want to learn multiple relationships simultaneously Solution: Learn multiple regressors (one for each graph), that operate on a single set of topics Jure Leskove, Pinterest & Stanford University
- 20. Challenges: Micro-Categories 20 Model discovers thousands of topics but no micro-categories Solution: Product hierarchy Laptop charger specific topics are only active for chargers. These are micro-categories. Topics at the top are common to all electronics products, and will contain generic electronics language Associate each node in the category tree with a small number of topics: Jure Leskove, Pinterest & Stanford University
- 21. Building the Graph C++ implementation that runs on a single (large-memory) machine OpenMP to parallelize computations Experimental results: Active part of the Amazon catalog 10m products 150m reviews 250m relationships 21Jure Leskove, Pinterest & Stanford University
- 22. Example: Product Graph 22Jure Leskove, Pinterest & Stanford University
- 23. Example: Product Graph 23Jure Leskove, Pinterest & Stanford University
- 24. Edge Prediction Accuracy 24 Substitute Complement Men’s Clothing 96.7% 94.1% Women’s Clothing 95.9% 94.1% Books 93.8% 89.9% Electronics 95.7% 88.8% Movies 85.6% - Music 90.4% - OVERALL 94.83% 90.23% Jure Leskove, Pinterest & Stanford University
- 25. Results: Micro-Categories 25Jure Leskove, Pinterest & Stanford University
- 26. 27 How does all this fit into Pinterest? Jure Leskove, Pinterest & Stanford University
- 27. Connecting People & Objects 28Jure Leskove, Pinterest & Stanford University
- 28. Pins: Richly Annotated Objects 29Jure Leskove, Pinterest & Stanford University
- 29. Pins are Collected in Boards 30Jure Leskove, Pinterest & Stanford University
- 30. 30+ Billion Pins categorized by people into more than 750 Million Boards 50% of pins have been created in the last 6 months 31
- 31. 32 Discovering relationships between objectsJure Leskove, Pinterest & Stanford University
- 33. References Inferring Networks of Substitutable and Complementary Products by J. McAuley, R. Pandey, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2015. Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text by J. McAuley, J. Leskovec. ACM Conference on Recommender Systems (RecSys), 2013. Learning Attitudes and Attributes from Multi-Aspect Reviews by J. McAuley, J. Leskovec, D. Jurafsky. IEEE International Conference On Data Mining (ICDM), 2012. 34Jure Leskove, Pinterest & Stanford University