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
Talk Outline
Product matching is the challenge of examining two different representations of retail products (think items that you see on e-commerce websites) and determining whether they both refer to the same product. Tackling this problem requires a mix of NLP (to deal with text data), computer vision (to deal with product images), ontology management and more (to ingest a host of other signals on offer).
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.
