1.
Machine Learning For
Building a Product Catalog
Kalpit Desai

2.
Outline
1. Why there’s a need for the Product Catalog
2. How is a Product Catalog useful?
3. Challenges in building a product catalog
4. How are we solving them

3.
How is a Product Catalog Useful
‘Search and Pick’ experience
Faster data entry
Improved data hygiene
Effortless data standardization
Add-on services - Product classification, updates, aggregations,...
Greater efficiency & capacity for business operations

4.
Is this a solved problem?

5.
We want to serve the MSMEs
Chaotic and unstructured
Diversity of content
Varied representations
Sheer scale

6.
<< pic for diversity of content >>
<< pic for diversity of representation >>
<< pic for scale >>
<< pic for chaos >>

7.
Geographical Differences
<< Add pictures for the two examples >>

8.
Product universe is dynamic and growing
Scrapehero

9.
How are we solving it?
Resolve
references
Build
catalogue
Maintain
catalogue

10.
Resolve References Vs Extract Attributes
Map parts of inputs to known “references”
“Aapple” maps to a brand with name “Apple” which
sells mobile phones with sub-brand “iPhone”
●Requires Dictionaries of Reference Entities
Brands, Ind segments, colors, sizes,…
●Richer Catalog
●Deeper queries possible
Label parts of inputs as attributes
“Aapple” represents a brand / sub-brand, “Iphn”
represents a brand / sub-brand
●No Reference Dictionaries are necessary
●Deeper queries not possible
“Aapple Iphn 7”

11.
Building The Catalog

12.
Maintaining The Catalog

13.
Reference Resolution Engine

14.
Dictionary Enrichment Engine
Features: lexical sequence, image
hash, relative position, contextual
cues
ML Models: RNN, CRF, SP, CNN

15.
Conclusion
Building a Catalog for MSMEs -- far from solved
Reference Resolution -- promising framework for the problem
Machine Learning – a tool at rescue

16.
kalpit.desai@clustr.co.in

17.
Backup

18.
Notes
India has 22 languages and 720 dialects
Amazon sells 3 billion products worldwide and 600 million products in USA.
In USA clothing, shoes, jewellery make the top most category with nearly 30%
of the products.

19.
Challenges
Serving the MSMEs
Most chaotic and unstructured business landscape
Extreme diversity of content as well as representation
Sheer scale
Stark Geographical Differences
Catalog Evolution