Inferring networks of substitute and complementary products

Jure Leskovec (@jure) Pinterest and Stanford

1 Jure Leskove, Pinterest & Stanford University

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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!


Understanding Products

To make relevant recommendations we need to understand the products

and how they fit together

Discovering relationships between products


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


Product Graph: Relations

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Substitutes:

Purchase

instead

Complements:

Purchase

in addition


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


Product Graph: What it does?

1. Understands the notions of substitute and complement goods

is substitutable for

complements



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:



3. Discovers micro-categories of products

Small clusters of tightly related products:



4. Recommends baskets of related products

Query: Suggested outfit:

Query: Suggested outfit:


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?


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


Problem Setting

How to learn

from data

Train by maximum likelihood:

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X Complementary

Not Complementary


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


Our Solution: SCEPTRE

Link Prediction

Review “topics”

Discover topics that “explain” product relations 17

Learn to discover topics that explain the product graph


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”


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


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:


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


Example: Product Graph


Example: Product Graph


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%


Results: Micro-Categories


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How does all this fit into Pinterest?


Connecting People & Objects


Pins: Richly Annotated Objects


Pins are Collected in Boards


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


We are hiring!

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[email protected]

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.


http://cs.stanford.edu/people/jure/pubs/prodgraph-kdd15.pdf

http://cs.stanford.edu/people/jure/pubs/prodgraph-kdd15.pdf

http://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf

http://cs.stanford.edu/people/jure/pubs/reviews-recsys13.pdf

http://cs.stanford.edu/people/jure/pubs/palelager-icdm12.pdf




Technology

Inferring networks of substitute and complementary products