Under the hood: Facebook Marketplace powered by artificial intelligence

This post details the application of AI and ML to Facebook Marketplace, focusing on building a multimodal product index using Lumos (image understanding) and DeepText (text understanding). It describes concatenating image encoder outputs (ResNet) and text embeddings into a complete product model, and incorporating a buyer model based on demographic and search data. Cosine similarity is used for ranking. FAISS is employed for efficient similarity search to reduce computing load. The post also discusses distributed training of neural networks for millions of products, improving QPS by tuning classification threads, and optimizing memory purging with `MALLOC_CONF`. For sellers, AI is used for autosuggesting categories and pricing, and enhancing image lighting. The 'M' assistant in Messenger uses AI for intent recognition, entity extraction (Duckling), and suggestion ranking, including translation via NMT. Future work includes visual search from real-world photos and recommending complementary products.