#Recommendation Systems

Explore a curated selection of over 30 deep learning and machine learning project ideas suitable for academic and industry contexts. These projects cover skill levels from beginner to advanced research, featuring domains like natural language processing, time series forecasting, and recommendation systems. Discover innovative approaches in image and video processing, music and audio analysis. Engage in hackathon opportunities and explore advanced topics such as semantic search and knowledge base QA. A valuable resource for students, researchers, and developers seeking to broaden their understanding of AI and machine learning.

This resource offers a curated collection of significant deep learning papers focused on enhancing search, recommendation, and advertising in industrial contexts. It covers key research areas like embedding, matching algorithms, and ranking methods, providing insights into the latest machine learning strategies. This compilation is essential for professionals and researchers aiming to leverage deep learning for improved digital experiences.

TorchRec is a PyTorch library designed for optimizing large-scale recommender systems through advanced sparsity and parallelism techniques. It facilitates efficient multi-device training with GPU-sharded embedding tables. Features include diverse sharding strategies, automated sharding plans, and kernels optimized by FBGEMM. TorchRec also supports quantization for precision reduction in training and inference, offering practical applications like DLRM. Used by platforms such as Twitter and Meta, TorchRec is a key tool for modern recommendation system development.