Facebook Marketplace powered by artificial intelligence
Facebook Marketplace was introduced in 2016 as a place for people to buy and sell items within their local communities. Today in the U.S., more than one in three people on Facebook use Marketplace, buying and selling products in categories ranging from cars to shoes to dining tables. Managing the posting and selling of that volume of products with speed and relevance is a daunting task, and the fastest, most scalable way to handle that is to incorporate custom AI solutions.
On Marketplace’s second anniversary, we are sharing how we use AI to power it. Whether someone is discovering an item to buy, listing a product to sell, or communicating with a buyer or seller, AI is behind the scenes making the experience better. In addition to the product index and content retrieval systems, which leverage our AI-based computer vision and natural language processing (NLP) platforms, we recently launched some new features that make the process simpler for both buyers and sellers.
Multimodal ranking system that incorporates Lumos, our image understanding platform, and DeepText, our text understanding engine. As a result, the index for each product includes layers for both the text and the images. To model the word sequence, we map each word from the title and description to an embedding and then feed them into the convolutional neural networks. The image portion comes from a 50-layer ResNet encoder pretrained on the image classification task. The output of the image encoder and the text embedding are concatenated and sent into a multilayer perceptron, where they can be trained and stored together as the complete product model.
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On Marketplace’s second anniversary, we are sharing how we use AI to power it. Whether someone is discovering an item to buy, listing a product to sell, or communicating with a buyer or seller, AI is behind the scenes making the experience better. In addition to the product index and content retrieval systems, which leverage our AI-based computer vision and natural language processing (NLP) platforms, we recently launched some new features that make the process simpler for both buyers and sellers.
Multimodal ranking system that incorporates Lumos, our image understanding platform, and DeepText, our text understanding engine. As a result, the index for each product includes layers for both the text and the images. To model the word sequence, we map each word from the title and description to an embedding and then feed them into the convolutional neural networks. The image portion comes from a 50-layer ResNet encoder pretrained on the image classification task. The output of the image encoder and the text embedding are concatenated and sent into a multilayer perceptron, where they can be trained and stored together as the complete product model.
Full article >>
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