Facebook, Inc.

Google Inc.

Dinkar Jain

Tianshi Gao

Qian Yan

Sina Jafarpour

Rajat Raina

Yifei Zhang

Anand Sumatilal Bhalgat

Robert Oliver Burns Zeldin

Jurgen Anne Francois Marie Van Gael

Ashish Gupta

Rose Marie Philip

Christian Alexander Martine

Darshan Kantak

Satya Satyavarta

Systems and methods for providing a personalized visual display of multiple products are provided. A described method includes receiving product information for a set of multiple products and user information for a particular user or user device and selecting a plurality of the multiple products estimated to be most relevant to the particular user or user device based on the product information for the set of multiple products and the user information for the particular user or user device. The method further includes generating a personalized visual display of the selected products including product images for multiple of the selected products. All of the products in the personalized visual display may be associated with the same content provider. The method further includes causing the personalized visual display to be presented via the user device.

Systems and methods for providing a personalized visual display multiple products

An online system determines a stability metric that indicates overlap between the set of entities associated with a particular entity when embeddings have been adjusted due to modifications in the input data of an embedding model. The online system generates a stability score for the embedding model by computing a statistic for one or more stability metrics. The online system determines a stability metric for a particular content provider by identifying a first cluster of content providers in a set of first embeddings, and a second cluster of content providers in a set of second embeddings. The second embeddings are generated after modifications have been made to input data. The online system determines the stability metric based on an overlap between the first cluster and the second cluster of content providers. The stability score can be an indicator of model performance that can be used to select embedding models.

Determining stability for embedding models

An online system maintains one or more models that determine likelihoods of a user performing various interactions after being presented with a content item. Additionally, the online system receives information identifying interactions by users with content, and generates embeddings for various users based on the interactions by the users with content. When determining whether to present a content item including an objective identifying an interaction to a user, the online system applies a maintained model to determine a likelihood of the user performing the interaction identified by the objective after being presented with the content item. Additionally, the online system determines a similarity of the embedding of the user to embeddings of users who performed the interaction identified by the objective. Based on a combination of the likelihood determined by the model and the similarity, the online system determines whether to present the content item to the user.

Accounting for organically occurring interactions with content when selecting content items for presenstation to users of an online system

A system predicts user intent to take an action and delivers content items to the user that match that intent. A plurality of features or attributes for each tracking pixel in a set of tracking pixels can be acquired based on content items and landing pages associated with each tracking pixel. For example, features for a tracking pixel can be determined based on information associated with a content item that enabled a user to access a landing page from which the tracking pixel was fired or triggered. In this example, features for the tracking pixel can also be determined based on information associated with the landing page. The features for the tracking pixels can be utilized to train a machine learning model. The machine learning model can be trained to predict whether or not a particular user intends to produce a conversion (e.g., make a purchase).

Determining intent based on user interaction data

An online system receives tracking requests from client devices interacting with a web page to analyze user interactions with the web page. The online system extracts parameters from tracking requests such as a uniform resource locator (URL) associated with the web page that generated the tracking request and/or data tokens describing keywords within the URL. The online system may extract parameters by crawling web pages that generate tracking requests. The online system may compare extracted parameters to a taxonomy of categories maintained by the online system to determine a category describing the item displayed on the web page. The online system determines a category describing the item via an item catalog maintained by the online system comprised of previously determined categories for various items. The online system uses the determined categories, attributes, and temporal relevance scores to direct content to users.

Analyzing tracking requests generated by client devices based on attributes describing items

In one embodiment, a method includes receiving a query inputted by the user; identifying a set of objects matching the query; calculating, for each identified object, a plurality of category-scores corresponding to a plurality of categories, respectively, wherein each category-score is calculated based on a plurality of sub-scores corresponding to a plurality of scoring axes; categorizing each identified object into a category of the plurality of categories based on the category-scores for the identified object; and sending, to the client system in response to the query, one or more search results corresponding to one or more of the categorized objects for display, each search result referencing the respective categorized object, wherein the one or more categorized objects of the search results comprises objects categorized into one or more selected categories.

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Menlo Park, CA, United States of America

Dinkar Jain