inferring networks of substitute and complementary products
TRANSCRIPT
Jure Leskovec (@jure) Pinterest and Stanford
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Recommendations drive whole businesses!
Jure Leskove, Pinterest & Stanford University
People and Items
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People Items
Fundamental problem: Making items discoverable!
Jure Leskove, Pinterest & Stanford University
Understanding Products
To make relevant recommendations we need to understand the products
and how they fit together
Discovering relationships between products
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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
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Product Graph: Relations
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Substitutes:
Purchase
instead
Complements:
Purchase
in addition
Jure Leskove, Pinterest & Stanford University
Product Graph: Description
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: cleaner; quieter
: cheaper; high power
: well made, easy to install
: fits perfectly, great value Jure Leskove, Pinterest & Stanford University
Product Graph: Overview
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substitute complement
Jure Leskove, Pinterest & Stanford University
Product Graph: What it does?
1. Understands the notions of substitute and complement goods
is substitutable for
complements
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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:
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Product Graph: What it does?
3. Discovers micro-categories of products
Small clusters of tightly related products:
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Product Graph: What it does?
4. Recommends baskets of related products
Query: Suggested outfit:
Query: Suggested outfit:
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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?
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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
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Problem Setting
How to learn
from data
Train by maximum likelihood:
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X Complementary
Not Complementary
Jure Leskove, Pinterest & Stanford University
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?
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Shoes Female
Jure Leskove, Pinterest & Stanford University
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
Challenges: Relation Direction
why do people who view X eventually buy Y?
Relationships we want to learn are not symmetric
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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
Challenges: Multiple Relations
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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
Challenges: Micro-Categories
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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
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
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Example: Product Graph
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Example: Product Graph
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Edge Prediction Accuracy
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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
Results: Micro-Categories
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How does all this fit into Pinterest?
Jure Leskove, Pinterest & Stanford University
Connecting People & Objects
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Pins: Richly Annotated Objects
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Pins are Collected in Boards
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30+ Billion Pins categorized by people into more than
750 Million Boards
50% of pins have been created
in the last 6 months 31
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Discovering relationships between objects
Jure Leskove, Pinterest & Stanford University
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.
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