AI/ML for E-Commerce in India€¦ · Daily ~13M ~600k Weekly ~85M ~2.7M Monthly ~400M ~8.8M. What...
Transcript of AI/ML for E-Commerce in India€¦ · Daily ~13M ~600k Weekly ~85M ~2.7M Monthly ~400M ~8.8M. What...
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AI/ML for E-Commerce in India
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Why does India need homegrown AI solutions?
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“Many Indias” - Require a differentiated approach
$30,000-35,000
$10,000-15,000
$4,000-5,000
$3,000-3,500
$2,500-3,000
$2,000-2,500
$1,500-2,000
$1,000-1,500
<$1,000 Bangladesh Uganda
Kenya Egypt
Mexico Brazil
UK Canada
11%
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AI For India
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‘Data’ is the new oil!
Visits13 + mn
Sessions : 18+ mn
Searches : 15+ mn
Pageviews : 200+ mn
Visits30+ mm
Sessions : 100+ mn
Searches : 50+ mn
Pageviews : 800+ mn
Average Big Billion DayTypical Day
Period Visits Txn Users
Daily ~13M ~600k
Weekly ~85M ~2.7M
Monthly ~400M ~8.8M
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What data do we collect?
isMarried?Fake Detection
GenderCategory clicksProduct clicks
Add to cart
Product searches
● GPS location● Mobile Device type*● Shipping Address*● Search Filters● Sizes ● Ratings/Reviews● Page scrolls
● Product Cards served
● Offers enabled● Ship SLA promised
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How do we process data, generate metrics & Insights?
Ingested data
Processed Insights &
Metrics
ML
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What do we infer from all this data?
Age isStudent?
isMarried?
Behavioral
Fake & Fraud Detection
Brand Affinity
CLTV
Trust & Safety
Demographic
Location
Kids (& Age)
Gender
IsMarried, IsStudent
Address
Price Affinity
Browse Location
Returns
Store Affinity
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How do we scale our ML models?
a.
b.
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AI-First vision: Just tech is not sufficient...
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AI, Knowing our CustomersCase Study : Personalization
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Understanding our Customers
BROWSE & PURCHASE ACTIVITY
on FLIPKART
Home Page Visits, Searches, Product page views, Add to cart/Wishlist,Orders, Store Visits etc.
INDIVIDUAL ATTRIBUTESGender, Location, Age, Spending Pattern.
ENVIRONMENTFestivals, Weather, Natural Calamities,
Seasons etc.
SEGMENT SIMILARSRFM- Platinum etc., Segmentation: Visit & Purchase Cohorts
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Merchandising, Recommendations: 70% Clicks : We serve intent & enableBusiness goals.
Optimised Merchandising and Ads Content
PersonalisedRecommendations
Personalised User State driven content
User cohort based on past behaviour. Current behaviour prediction done by ML model.
New clicks drive model recompute, serving up personal content in real-time.
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Search Landscape: Unique for Every Query Type, User, Category
More than 2/3rd Searches are Broad (Eg: Mobile in Tier 2, iPhone X in Metros)
Semantics Differ Too: “Action” is a Brand, “Action” refers to sports shoes
Category Brand Facet Line Product
Fashion Shoes Nike Shoes Silk Saris
Mobile Mobiles Mi Mobiles Galaxy S8
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Prediction based on user activity, Brand, Category, 6 views help
Using user’s device and location information to predict the price-affinity of new users
Watches: Low ASP users Vs Premium users
Personalised Search Results: Driving consumers closer to their choice
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Conversation Funnels : Glean User Intent
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Conversation Funnels : Progressive Questions and Answers
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Conversation Funnels : Customer Speak
Single/Couple - 6 kgFamily of 3 - 8 kgFamily of 4 - 10 kgLarge Families - 12 kg
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Shaping Intent : Visual Curated Collections
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Shaping Intent : Trending in India & Less Explored
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AI, Human ModerationCase Study : Visual Similar
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For customers, inspiration is everywhere…
FACT: A picture is indeed worth a 1,000 words
No search query can capture a customer’s inspiration
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Previously, this is how “Visual Search” worked
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Collaborative filtering is often not enough
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AI methods produce better matches…
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Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce, Shankar, Devashish; Narumanchi, Sujay; Ananya, H A; Kompalli, Pramod; Chaudhury, Krishnendu
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AI, Progressive InnovationCase Study : Customer Reviews, Sentiment, Aspects
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● Mix of English and local languages (transliterated)
● Spelling and grammatical mistakes, internet abbreviations and slang
● 6 months of human-tagged data for training, though very noisy
Problem : Moderate Reviews, Understand Sentiment, Attribution
“Product is cheap, camera does not work!”
“Please buy this product from my shop”
“Flipkart service has always been great”
Accepted
Rejected
Discouraged
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Aspect Reviews, Aggregate Opinion of our Customers- Input to
Product Design
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Other Application:Auto Titling & Sentiment Analysis
Title suggested: Nice Product, good fitting
Use parts of speech patterns in text. Typical flow: Tag each word Superb_ADJ product_NOUN has_VERB great_ADJ camera_NOUN
Sentiment Suggested: Positive, Rating: 3.8
Used a variant of the Open AI sentiment nueron. The network can classify text into positive and negative sentiment. The sentiment score can be normalised to a star rating.
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Generator
Z
X
Discriminator
Real or Fake Set of randomly generated images
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Generator Losses Discriminator Losses
Makkapati V., Patro A. (2017) Enhancing Symmetry in GAN Generated Fashion Images. SGAI 2017.
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Dresses Tops
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Checks Stripes
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Problem Statement(s)...
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Problem Statement(s)...
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