The Fifth Elephant 2018

The seventh edition of India's best data conference

Improving product discovery via relevance and ranking optimization

Submitted by Akash Khandelwal (@akash099) on Saturday, 31 March 2018

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Technical level

Intermediate

Section

Full talk

Status

Confirmed & Scheduled

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Total votes:  +4

Abstract

In e-commerce, recommendations play a key role not only in customer satisfaction by improving discovery but also helps fulfill business objectives. In this talk, I will focus on our iterative journey starting from feature engineering, adding features incrementally and learning on them, thus moving from a rule based system to launching a machine learnt system in production.

The Flipkart product recommender system consist of two layers - relevance and ranking.
1. Relevance layer takes into account user behavioural events including browse and purchases and computes similarity score for product pairs using collaborative filtering. This similarity score is used to generate a selection set for powering product recommendations.
2. In the Ranking Layer, we rank these selected candidates using Learning to Rank technique to generate our final recommendation list. As a part of this talk, I will cover the feature set and algorithms used for predicting the conversion probability of recommended products.

Outline

The talk will cover following topics :
a) User shopping journey at Flipkart and importance of product discovery
b) Types of product recommendations : similar products, cross selling etc.
c) Architecture of Recommender System : Relevance and Ranking modules
d) Using product textual and visual attributes for computing product similarity
e) Using crowdsourced activity data to compute the set of relevant products
f) Formulation of ranking as an machine learning problem towards optimising conversion rates
g) Our learnings from various iterations over feature-sets and ML models

Speaker bio

Akash is a software developer with Search Relevance team at Flipkart, working on improving Autosuggest. Previously, he has worked on building Flipkart Recommendation System. He designed real time and batch pipelines to power recommendations, including use cases such as product bundling, similar products and personalisation. He is interested in applying Machine Learning for pattern mining, and deploying data processing pipelines at scale. He graduated with a dual degree in Computer Science & Engineering from IIT Delhi.

Links

Slides

https://docs.google.com/presentation/d/1CDOzZN3HF_EmJ_lVkOGfPgZvgzdtUg0eCGrb89QwLqk/edit?usp=sharing

Preview video

https://youtu.be/GUVbKM26Dbc

Comments

  • 1
    Zainab Bawa (@zainabbawa) Reviewer 8 months ago

    Look forward to seeing the slides, and what is the takeaway for the audience from this talk.

  • 1
    Akash Khandelwal (@akash099) Proposer 8 months ago

    Hi Zainab, I’ve updated the preview video and attached slides with outline for the talk..

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