Facebook, Inc.

Alisson Gusatti Azzolini

Xiao Li

Yofay Kari Lee

Maxime Boucher

Michael Benjamin Cohen

Lars Eilstrup Rasmussen

Stuart Michael Bowers

Lars Rasmussen

Hussein Mohamed Hassan Mehanna

Jeffrey Scott Dunn

Pamela Shen Vagata

Xiaowen Xie

Rodrigo Bouchardet Farnham

James Robert Paton

Aleksandr Sidorov

Kathryn Hymes

Amy Campbell

Michael Curtiss

Chaitanya Mishra

David Paul Capel

Sai Nishanth Parepally

Andrey Malevich

In particular embodiments, a method includes receiving, from a client system of a first user, a structured query comprising references to one or more objects associated with the online social network, identifying a set of target objects corresponding to the structured query, wherein each target object in the set is an object that is connected within the online social network to at least one of the objects referenced in the structured query, filtering the set of target objects based on a privacy setting of each of the connections to the target object, and generating search results comprising references to each target object from the post-filtered set that is connected to the first user within the online social network by a series of connections that have a visibility that is visible to the first user.

Filtering search results for structured search queries

An online system identifies an additional feature to evaluate for inclusion in a machine learned model. The additional feature is based on characteristics of one or more dimensions of information maintained by the online system. To generate data for evaluating the additional feature, the online system generates various partitions of stored data, where each partition includes characteristics associated with one or more dimensions on which the additional feature is based. Using values of characteristics in a partition, the online system generates values for the additional feature and includes the values of the additional feature in the partition. Values for the additional feature are generated for various partitions based on the values of characteristics in each partition. The online system combines multiple partitions that include values for the additional feature to generate a training set for evaluating a machine learned model including the additional feature.

Evaluating modifications to features used by machine learned models applied by an online system

Some embodiments include a workflow authoring tool that accesses a text string representation of a workflow and a text string representation of at least a data processing operator type. The workflow authoring tool enables definition of one or more data processing operator types that can be referenced in defining the machine learning workflow. When scheduling a workflow, the text string representation of the workflow can be parsed and traversed to generate an interdependency graph of one or more data processing operators. The text string representation of the data processing operator type can identify operator attributes associated with the data processing operator type.

Machine learning system flow authoring tool

In particular embodiments, a method includes receiving, from a client device associated with a first user of an online social network, a text query comprising one or more character strings, identifying one or more data objects that match at least a portion of one or more of the character strings, wherein each of the identified data objects is connected to the first user within the online social network, generating one or more recommended queries that each comprise the character strings of the text query and references to one or more of the identified data objects, and sending, to the client device associated with the first user in response to receiving the text query, one or more of the recommended queries for display to the first user.

Personalized structured search queries for online social networks

Some embodiments include an experiment management interface for a machine learning system. The experiment management interface can manage one or more workflow runs related to building or testing machine learning models. The experiment management interface can receive an experiment initialization command to create a new experiment associated with a new workflow. A workflow can be represented by an interdependency graph of one or more data processing operators. The experiment management interface enables definition of the new workflow from scratch or by cloning and modifying an existing workflow. The workflow can define a summary format for its inputs and outputs. In some embodiments, the experiment management interface can automatically generate a comparative visualization at the conclusion of running the new workflow based on an input schema or an output schema of the new workflow.

Machine learning system interface

Some embodiments include a method of machine learner workflow processing. For example, a workflow execution engine can receive an interdependency graph of operator instances for a workflow run. The operator instances can be associated with one or more operator types. The workflow execution engine can assign one or more computing environments from a candidate pool to execute the operator instances based on the interdependency graph. The workflow execution engine can generate a schedule plan of one or more execution requests associated with the operator instances. The workflow execution engine can distribute code packages associated the operator instances to the assigned computing environments. The workflow execution engine can maintain a memoization repository to cache one or more outputs of the operator instances upon completion of the execution requests.

Machine learning system flow processing

In particular embodiments, a method includes receiving, from a client device associated with a first user of an online social network, an unstructured text query, parsing the text query to identify one or more n-grams, determining a score for each n-gram, wherein the score for each n-gram is a probability that the n-gram corresponds to a particular data object of a plurality of data objects associated with the online social network, identifying one or more data objects based on the scores of their corresponding n-grams, each identified data object corresponding to at least one of the n-grams, and generating one or more structured queries that each comprise references to one or more of the identified data objects.

Detecting social graph elements for structured search queries

In particular embodiments, a method includes receiving an unstructured text query, identifying nodes and edges from a social graph that correspond to n-grams in the text query, generating structured queries that include references to the identified nodes and edges, receiving a selection of a structured query, identifying target nodes that correspond to the structured query, and then generating search results that include target nodes with privacy settings where the nodes and edges along the path connecting the target node and the querying user are all visible to the user.

Filtering structured search queries based on privacy settings

In one embodiment, a method includes accessing a social graph that includes a plurality of nodes and edges, receiving an unstructured text query, identifying nodes and edges that correspond to n-grams of the text query, accessing a context-free grammar model, identifying grammars having query tokens that correspond to the identified nodes and edges, determining a score for each identified grammar, and then generating structured queries based on the identified grammars based on strings generated by the grammars.

Grammar model for structured search queries

In particular embodiments, a method includes receiving an unstructured text query, parsing the text query to identify n-grams; determining a score that the n-grams correspond to particular nodes and edges from a social graph, identifying those nodes and edges with a score greater than a threshold score, and then generating structured queries that include references to the identified nodes and edges.

In particular embodiments, a method includes receiving an text query, identifying nodes and edges from a social graph that correspond to character strings from the text query, and then generating recommended queries that include references to the identified nodes and edges.

In particular embodiments, a method includes receiving an unstructured text query, identifying nodes and edges from a social graph that correspond to n-grams in the text query, and then generating structured queries that include references to the identified nodes and edges.

Growing community of inventors

San Francisco, CA, United States of America

Alisson Gusatti Azzolini