ºÝºÝߣshows by User: govind201 / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: govind201 / Tue, 22 Aug 2017 08:13:27 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: govind201 5 Lessons I’ve Learned Tackling Product Matching for E-commerce /slideshow/5-lessons-ive-learned-tackling-product-matching-for-ecommerce/79045298 5lessonsivelearnedtacklingproductmatchingfore-commerce-170822081327
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.]]>
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.]]> Tue, 22 Aug 2017 08:13:27 GMT /slideshow/5-lessons-ive-learned-tackling-product-matching-for-ecommerce/79045298 govind201@slideshare.net(govind201) 5 Lessons I’ve Learned Tackling Product Matching for E-commerce govind201 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. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5lessonsivelearnedtacklingproductmatchingfore-commerce-170822081327-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 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.
5 Lessons I’ve Learned Tackling Product Matching for E-commerce from Govind Chandrasekhar
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