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5 Lessons I’ve Learned Tackling Product Matching for E-commerce
Submitted by Govind Chandrasekhar (@gc20) on Saturday, 29 April 2017
Full talk for data engineering track
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.
Basic understanding of deep learning and experience working on real-world problems is ideal. Beginners should be able to follow.
Govind is a co-founder of Semantics3. Semantics3 provides Data APIs and AI APIs for e-commerce focused companies to make better decisions and grow their businesses. We’re a 5+ year old Y Combinator backed startup based in Bengaluru, San Francisco and Singapore.
Our data-science team works on e-commerce data problems like product categorization, product matching, named entity recognition and unsupervised content extraction.