•1 min read•from InfoQ
Swiggy Improves Search Autocomplete Using Real Time Machine Learning Ranking
Swiggy detailed real-time machine-learning ranking system for autocomplete built on OpenSearch. The architecture separates candidate generation and ranking, uses feature stores for real time signals, and applies learning to rank models for improved relevance. It replaces heuristic ranking while maintaining strict latency constraints and enabling continuous model updates from user behavior signals.
By Leela KumiliWant to read more?
Check out the full article on the original site
Tagged with
#real-time data collaboration
#real-time collaboration
#machine learning in spreadsheet applications
#natural language processing for spreadsheets
#generative AI for data analysis
#Excel alternatives for data analysis
#AI formula generation techniques
#rows.com
#Swiggy
#search autocomplete
#real-time
#machine learning
#ranking system
#OpenSearch
#candidate generation
#feature stores
#real-time signals
#learning to rank
#relevance
#heuristic ranking