Indix at Fifth Elephant 2015

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Indix Director of Engineering Nikhil Ketkar talks about "Matching Retail Products at Scale" at HasGeek's Fifth Elephant 2015.

Technology

Nikhil
Ketkar

Data
Science
@
Indix

16
July
2015

Crawler

Matching

Product

Pages

Groups
of

Matching
URLS

Focus
of

the
Talk

¡  Competitive
Landscape

§  Who
are
your
competitors?

§  How
are
they
pricing
products?

§  What
other
products
do
they
carry?

¡  Scale

§  Products

§  Sites

§  Categories

Store

Product

Match

Matching
is

Central
to

answering
key

questions

in

retail
analytics

1.  Title

2.  Image
URL

3.  Price

4.  Description

5.  Tables

Challenges:
Scale,

Depth,
Diversity,

Change

DOM
Tree

Title
or
Not:
Binary

Classiﬁcation

Class

Imbalance

DOM
Tree

HTML
Features

Visual
Features

Random
Forest

Model

1.  Title

2.  Image
URL

3.  Price

4.  Description

5.  Tables

Category

Taxonomy

Challenges:
Large

Taxonomy,
Lack
of
Training

Data,
Changes
in
Taxonomy

Linear
SVM

CNN

Ensemble

Breadcrumb

Mapping

Background

Knowledge

Merge
Groups

Bucketing/

Clustering

Mass
Join
LSH

Challenges:
Pairwise

Distance
Computation,

Match
at
a
Store
Constraint

Store

Product

Match

1.  Pairwise
Distance
Computation

2.  Constrained
Clustering

¡  Constraint
Type

§  Must
Link

§  Cannot
Link

¡  Examples

§  UPC

§  MPN

§  Match
at
a
Store

Must
Link

Cannot

Link

May
Link

D(P, P0
)
Use
Constrained

Clustering

Parsing
Classiﬁcation

Attribute

Extraction

Blocking

Match

Inference

HTML

Product

Record

Classiﬁed

Products

Attributes

Product

Groups

Matches

Reported

Actual

Correct

Correct

Actual

Reported

¡  Precision

§  Sample
and
Spot-‐check

¡  Recall

§  Hard
to
estimate

§  Rare
population

§  Manually
search
products
on

a
site
to
produce
blind
sets

Lack
of
Ground

Truth
is
the

biggest
road
block

Correct

This document discusses challenges in retail analytics matching related to scale, diversity, and changes. It presents approaches for parsing product pages to extract attributes using techniques like DOM tree analysis, classification, and clustering. Constraint-based clustering is proposed to match products while respecting constraints like products only matching within a single store. Evaluation of matching is difficult due to the lack of ground truth data.

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Indix at Fifth Elephant 2015

1. Nikhil Ketkar Data Science @ Indix 16 July 2015

5. Crawler Matching Product Pages Groups of Matching URLS Focus of the Talk

6. ¡  Competitive Landscape §  Who are your competitors? §  How are they pricing products? §  What other products do they carry? ¡  Scale §  Products §  Sites §  Categories Store Product Match Matching is Central to answering key questions in retail analytics

7. 1.  Title 2.  Image URL 3.  Price 4.  Description 5.  Tables Challenges: Scale, Depth, Diversity, Change DOM Tree Title or Not: Binary Classiﬁcation Class Imbalance

8. DOM Tree HTML Features Visual Features Random Forest Model

9. 1.  Title 2.  Image URL 3.  Price 4.  Description 5.  Tables Category Taxonomy Challenges: Large Taxonomy, Lack of Training Data, Changes in Taxonomy

10. Linear SVM CNN Ensemble Breadcrumb Mapping Background Knowledge

11. 1.  Title 2.  Image URL 3.  Price 4.  Description 5.  Tables Challenges: Large number of attributes, bad/missing data, variability 1.  Brand 2.  Size 3.  Color 4.  Packs 5.  … Schema

12. Brand:Nike Brand:Reebok

13. Brand:Nike Color:Black/Neo Lime Total-‐Crimson Sole:Rubber

14. 1.  Title 2.  Image URL 3.  Price 4.  Description 5.  Tables Challenges: No single approach works well 1.  Brand 2.  Size 3.  Color 4.  Packs 5.  … Category Enriched Product Record

15. Merge Groups Bucketing/ Clustering Mass Join LSH

16. Challenges: Pairwise Distance Computation, Match at a Store Constraint Store Product Match 1.  Pairwise Distance Computation 2.  Constrained Clustering

17. D(P, P0 ) 1.  Title BOW 2.  Brand 3.  Category 4.  Attributes

18. ¡  Constraint Type §  Must Link §  Cannot Link ¡  Examples §  UPC §  MPN §  Match at a Store Must Link Cannot Link May Link D(P, P0 ) Use Constrained Clustering

19. Parsing Classiﬁcation Attribute Extraction Blocking Match Inference HTML Product Record Classiﬁed Products Attributes Product Groups Matches

20. Reported Actual Correct Correct Actual Reported ¡  Precision §  Sample and Spot-‐check ¡  Recall §  Hard to estimate §  Rare population §  Manually search products on a site to produce blind sets Lack of Ground Truth is the biggest road block Correct

21.

22.

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