{
  "patent_number": "US 10402750",
  "country": "US",
  "title": "How Facebook Uses Deep Learning to Predict What You Might Like",
  "original_title": "Identifying entities using a deep-learning model",
  "summary": "A method for training AI models to recommend new content by comparing a user's past interactions with unseen items in a social network.",
  "what_it_does": "This patent describes a way to teach a computer model to better understand user preferences. It takes a list of things a user has already interacted with, like posts or pages, and turns them into mathematical lists called vectors. By temporarily hiding one of those items and using the rest to build a profile of the user, the system can test if it correctly predicts the hidden item. It then compares this user profile against items the user has never seen, updating the model's math so that relevant items move closer to the user in a virtual space. This helps the system learn to suggest content that is more likely to interest the user.",
  "what_it_does_not_cover": [
    "Does not cover non-deep-learning recommendation methods like simple keyword matching.",
    "Does not cover systems that do not use vector-based embedding spaces for entity comparison.",
    "Does not cover methods that do not involve removing a target entity from the interaction set to perform the training feedback loop."
  ],
  "filed": "2015-12-30",
  "granted": "2019-09-03",
  "expires": null,
  "status": "active",
  "holder": "Facebook Inc",
  "holder_url": "https://patentbrief.org/company/facebook-inc",
  "inventors": [
    {
      "name": "Keith Adams",
      "url": "https://patentbrief.org/inventor/keith-adams"
    },
    {
      "name": "Jason E. Weston",
      "url": "https://patentbrief.org/inventor/jason-e-weston"
    },
    {
      "name": "Sumit Chopra",
      "url": "https://patentbrief.org/inventor/sumit-chopra"
    }
  ],
  "times_cited": 8,
  "tags": [
    "ai_ml",
    "software",
    "ecommerce",
    "consumer_electronics"
  ],
  "abstract": "In one embodiment, a method includes accessing a first set of entities, with which a user has interacted, and a second set of entities in a social-networking system. A first set of vector representations of the first set of entities are determined using a deep-learning model. A target entity is selected from the first set of entities, and the vector representation of the target entity is removed from the first set. The remaining vector representations in the first set are combined to determine a vector representation of the user. A second set of vector representations of the second set of entities are determined using the deep-learning model. Similarity scores are computed between the user and each of the target entity and the entities in the second set of entities. Vector representations of entities in the second set of entities are updated based on the similarity scores using the deep-learning model.",
  "url": "https://patentbrief.org/patent/us/10402750/automl-neural-architecture-search",
  "markdown_url": "https://patentbrief.org/patent/us/10402750/automl-neural-architecture-search/md",
  "google_patents_url": "https://patents.google.com/patent/US10402750",
  "relatedPatents": []
}