Similarity Search for Product Matching @ Semantics3

Jul 2019

22 Mon

23 Tue

24 Wed

25 Thu 09:15 AM – 05:45 PM IST

26 Fri 09:20 AM – 05:30 PM IST

27 Sat

28 Sun

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Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

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The Fifth Elephant Winter edition starts at 9:30 am; live stream for members This update is for participants only

##The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:

Model management, including data cleaning, instrumentation and productionizing data science.
Bad data and case studies of failure in building data products.
Identifying and handling fraud + data security at scale
Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
Feature engineering and ML platforms.
What it takes to create data-driven cultures in organizations of different scales.

##Highlights:

1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.

##Why you should attend?

Network with peers and practitioners from the data ecosystem
Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
Demo your ideas in the demo session
Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.

##Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule

##Contact details:
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email info@hasgeek.com

#Sponsors:

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Similarity Search for Product Matching @ Semantics3

Submitted Apr 15, 2019

Session type: Lecture Session type: Full talk of 40 mins

One of the major offerings of Semantics3 is our universal product data catalog gathered through large scale indexing of the public web. For each catalog, duplicated entries of the same product across multiple retailers need to be merged/removed. In this talk, we will go through the technical challenges in such a large scale “product matching” system, where millions of products are often compared against millions of others (leading to trillions of pair-wise comparisons). Both traditional and state-of-the-art approaches will be discussed in solving this task.

Outline

Introduction [~ 5 mins]

This section will present an overview of the problem, the use cases that motivate it and establish the tone for the rest of the presentation.

Topics Covered:

Product Matching: What is it and Why is it important?
Similarity Search for Product Matching: What is it and how does it speed up matching?
Example Case for Similarity Search: Sample product document and sample query document to explain the following sections.

Traditional Text Search Approaches [~ 5 mins]

This section will cover our intial attempt at the similarity search problem using traditional text based methods largely leveraging elasticsearch.

Topics Covered:

Overview of how we set up the problem
Bottlenecks we hit and available tuning options
Examples of real queries

Lessons from Traditional Text Search Approaches [~ 5 mins]

This section will cover some of the key insights we gleaned from traditional text approaches and how we needed to reframe the problem.

Topics Covered:

The nature of our data/problem and why elasticsearch wasn’t a good fit.
Need for indexing multi-modal data
Examples of failed cases
Search is only as good as the document’s representation.

Representation Learning [~ 10 mins]

This section would cover how we reframed this as a representation learning problem and the different network architectures we tried, how we suited it to our needs, what worked/didn’t work and the challenges we faced along the way.

Topics Covered:

How we reframed the problem
Different network architectures we tried and their results.
Examples of success cases which had failed previously.
Infrastructure and scaling challenges

Infrastructure Challenges [~ 5 mins]

Solving the representation problem didn’t necessarily solve the similarity search problem. We only had a way to sufficiently represent all the product information on the vector space. This section will cover the infrastructure challenges, the options we considered and how we ended up choosing FAISS.

Topics Covered:

Challenges, Constraints
Re-evaluating Elasticsearch
Evaluating FAISS
Key bencmarks

Conlusion [~ 2 mins]

Requirements

Familiarity with text search paradigms will be a good-to-have (not essential).

Speaker bio

Abishek is a member of the data science team at Semantics3, which offers data and AI solutions for ecommerce marketplaces (catalog generation & enrichment, seller on-boarding) and logistics companies (HTS/tariff classification, attribute enrichment). Among these, Abishek is the lead data scientist working on product matching and catalog generation.