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Building a Deep Learning Based Retrieval System for Personalized Recommendations

Categories: Coding & Developer Tools, Marketing & Ads, Presentations  |  Pricing: Freemium  |  Official Website ↗

A step-by-step guide on how to build a state-of-the-art recommender system in an industrial setting.

A step-by-step guide on how to build a state-of-the-art recommender system in an industrial setting.

Key Features

• Deep Learning for embeddings (NLP)
• Approximate Nearest Neighbor (ANN) for retrieval
• Personalized item recommendations
• Three-phase system architecture (Offline, NRT Hybrid, NRT)
• Hadoop for data aggregation
• Spark for ETL jobs
• PyTorch and pytorch-lightning frameworks

Pros

• Provides a step-by-step guide for building a recommender system
• Focuses on large-scale, high-volume traffic production systems
• Details use of deep learning for personalized recommendations
• Explains real-time retrieval using Approximate Nearest Neighbor
• Shares eBay's practical experience and architecture

Cons

• Assumes existing e-commerce website and UI module
• Assumes a fully trained best-in-class ML model
• Specific to eBay's internal infrastructure (e.g., Krylov)
• May require significant engineering and ML expertise
• Does not cover model training in detail

Use Cases

• Building personalized recommendation modules on homepages
• Generating 'items based on your recent views' features
• Developing real-time, low-latency recommendation systems
• Creating user and item embeddings for similarity search
• Scaling recommendation systems for millions of users

Best For

• Machine Learning Engineers
• Data Scientists
• E-commerce platforms
• Companies building large-scale recommendation systems

Integrations: Hadoop, Spark, PyTorch, pytorch-lightning

Watch demo on YouTube ↗

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