5 Lessons I’ve Learned Tackling Product Matching for E-commerce

Slides & video from my talk @ Fifth Elephant

Recently, I gave a talk at Fifth Elephant, a “conference on big data and machine learning”. The talk drew on insights that our data-science team has gleaned working on the challenge of product matching.

Slides

Video

The audio’s choppy, but have a listen

Talk Outline

I’ve been working on this problem in various capacities for a few years now at Semantics3. During this period, I’ve made a fair number of mistakes which in turn have taught me useful lessons about applying deep/machine learning in an industry setting.

During this talk, I’d like to walk you through 5 specific scenarios in which I attempted to achieve a specific goal in the context of product matching, but ran into an unexpected problem that threw a spanner in the works. I’ll then talk about the root cause that sprouted the problem in the first place and the lesson I learned having made this discovery. Where relevant, I’ll bring in examples from outside the retail domain to broaden the perspective offered.

The goal of the talk isn’t to provide a guidebook for solving the product matching problem — the goal is to give you insight into the ups and downs of working through a specific data-science problem, and in the process, delivering packaged lessons that you could potentially draw on in your own field of work.


To learn more about Semantics3's offerings for Matching, Categorization or Feature Enhancement, get in touch with us.

The Ecommerce Intelligencer

A look at how data is shaping the future of e-commerce, gleaned from our stockpile of Ecommerce product, pricing and customer metadata. Also see www.semantics3.com/blog

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Govind Chandrasekhar

Written by

Co-founder @ semantics3.com; govindc.com

The Ecommerce Intelligencer

A look at how data is shaping the future of e-commerce, gleaned from our stockpile of Ecommerce product, pricing and customer metadata. Also see www.semantics3.com/blog

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