Core Shopping Recommendation System
Industrial-grade recommendation system framework with both retrieval and ranking models. Includes state-of-the-art deep learning architectures and reusable neural network layers for building production-ready e-commerce recommendations.
Tech Stack
Problem
Building scalable recommendation systems requires implementing multiple complex models (retrieval + ranking) with proper feature engineering, evaluation metrics, and production considerations.
Solution
Created a modular framework with reusable layers (attention, cross-net, embeddings) and implementations of state-of-the-art ranking models (DIN, DIEN, DCNv2, BST) plus retrieval models (Two-Tower, Collaborative Filtering).
Impact
Accelerated recommendation system development with battle-tested implementations based on industrial-grade patterns.
Key Features
- •Ranking Models: DIN, DIEN (AUGRU), DCNv2, BST, XGBoost/LightGBM Rankers
- •Retrieval Models: Two-Tower (Dual Encoder), Matrix Factorization + BPR
- •Reusable Layers: DIN attention, Multi-head attention, CrossNet, FM, SENet
- •Custom Activations: Dice, GELU for CTR prediction
- •Comprehensive Metrics: AUC, NDCG, MRR, gAUC, MAP
- •Feature Processor for encoding and normalization