PEARS is a team that develops a personalization and recommendation system (PEARS) for an e-commerce company. The system analyzes user, product, and transaction data to provide personalized recommendations that have led to a 40% increase in orders and 5% boost in sales. It makes recommendations to over 25 million users from over 50,000 sellers and handles 100 terabytes of data daily using machine learning and data mining techniques.

1. PEARS

2. About PEARS
We are a team of Engineers, researchers and product managers.
PEARS “Personalization and Recommendation System”
40%
Snapdeal orders
5%
Sales boost after
launching Personalized Products
25 Million
Users are recommended products
50K+
Sellers
3M+
Daily users
We generate
100TB+
Data per day

3. PEARS 360 degree analysis

4. 360°
USER PRODUCT

5. Purchase Data Search Data Click/View Data Wish-list Data Cart Data
Social Signals
Customer Care
data
User DemographicReviewsGeolocation
Recommended
Products
Category Affinity Brand Affinity Filter Affinity
Real-time Purchase
Probability
User categorization
Real-time promos/
discounts
USER

6. Product Explicit
data
Price history Seller details Sale history Product visits data Offer/Promo
FeedbackAdwords dataSocial dataOutside dataTags
User Affinity
Geo-location based
Targeting
Sale prediction
Product QualitySeller Quality
Product
categorization
PRODUCT

7. PEARS Architecture

8. PEARS Architecture

9. PEARS Data Ingestion Layer

10. Consumer layerQueuing layer
WEB
LOGS
DATABASE
NOSQL
EXTERNAL
Data Ingestion Layer
gulpR
Data Sources Persistence layer
CAMUS

11. PEARS Data Science Engine

12. Data Science Engine
• Machine Learning
• Data Mining
• Text Mining
• Statistical Analysis
Technologies

13. PEARS Data Products

14. Recommended Product Feed

15. Trending Now

16. People who viewed this item also viewed

17. Similar products

18. Frequently bought together

19. Data Analytics & Business
Intelligence

20. Data
Sources
Analytics
Cloud
Data
Aggregation
Data
Insights
Transaction Data
Orders
Supply Chain
Financial
Customer Care
Etc
Data Cluster Scalable to
Peta Bytes
Non Txn Data
Web Logs
MongoDB
User Profile
Catalog Data
Browsing Data
External Data
Omniture
Social
Columnar Database
Data Warehouse
Snapdeal API
Business Intelligence
Reports
Adhoc & Periodic
API Client
Internal & External
Data Analytics & Business Intelligence

21. Online Data Processing

22. Online Data Processing
• High Speed Product Updates 2 million per hour
• High Speed Search Indexing 1 million per hour
• Seller Ranking 1 million per hour
• Optimized Courier Allocation 26 million rule
combinations
• Flash Sale 100K orders per
minute
• Rich Product Listing 200K per second

23. Challenges

24. Challenges
• Real-time Analytics
– Personalization
– Business Intelligence
• Large Scale Data Processing within minimal time with
least resources
• On-line Data Processing
– Growing Catalog
– Growing Seller Base
– Extremely High read-write Systems

25. Thank You!