Facebook, Inc.

Hong Yan

Yofay Kari Lee

Akhil Wable

Spencer G Ahrens

Ming Hua

Guizhen Yang

Ryan Allen Stout

Mark E Zuckerberg

Gokul Rajaram

Aleksandar Ilic

Yanxin Shi

Gideon Wald

Mao Xue

Ami Vora

Igor Kabiljo

Wenjie Fu

Harish Srinivas

Jing Fang

In one embodiment, a method includes a computer server machine receiving information about a first media player event with the information about the first media player event including a viewing session identifier. Using a state machine, the computer server machine identifies a current state of the viewing session using the information about the first media player event. The computer server machine receives information about a second media player event. Using a state machine, the computer server machine identifies a subsequent state of the viewing session using the information about the second media player event. The computer server machine calculates metrics for the viewing session based on the current state and subsequent state, and stores the metrics. The computer server machine generates a session summary with the session summary including information about the metrics for the viewing session.

State-based logging for a viewing session

In one embodiment, a method includes managing delivery of messages to non-stationary mobile clients. A computing system receives a request from a first user to send a message to a second user. The computing system then accesses the location information of the second user which includes the velocity of the mobile client of the second user. The computing system further accesses the activity information of the second user which indicates if the second user is currently active on a social-networking application. If the velocity indicates that the second user is non-stationary, the computing system then determines an activity-level of the second user. If the activity-level is below a threshold activity-level, the computing system confirms with the first user whether the message to the second user should be sent. If the activity-level is above a threshold activity-level, the computing system sends the message to the receiver.

Managing delivery of messages to non-stationary mobile clients

A system environment supporting one or more advertisers and one or more ad publishers provides a means of sharing user-targeted advertisement. One or more hashing modules in the system environment provide the advertisers and ad publishers a means of sharing this information without revealing a user's personally identifiable information (PII). If a lossy hash is chosen, then an advertiser can communicate targeted advertisements to an ad publisher without revealing any PII to the ad publisher that the ad publisher did not already know.

Protecting identities of users to whom advertising is targeted

Online systems, for example, social networking systems store features describing relations between entities represented in the online system. The information describing the features is represented as a graph. The online system maintains a cumulative feature graph and an incremental feature graph. Feature values based on recent user actions are stored in the incremental graph and feature values based on previous actions are stored in the cumulative graph. Periodically, the information stored in the incremental feature graph is merged with the information stored in the cumulative feature graph. The incremental graph is marked as inactive during the merge and information based on new user actions is stored in an active incremental feature graph. If a request for feature information is received, the feature information obtained from the cumulative feature graph, inactive incremental feature graph and the active incremental feature graph are combined to determine the feature information.

Updating features based on user actions in online systems

Indexing and retrieving real time content in a social networking system is disclosed. A user-term index includes user-term partitions, each user-term partition comprising temporal databases. As a post is received from a user, a user identifier, a post identifier, and a post is extracted. An object store communicatively coupled to a temporal database for recently received content is queried to determine whether terms in the post has already been stored. A term identifier is stored in the user-term index with the user and post identifiers. A forward index stores the post by post identifier. Responsive to a search query, the user-term index is searched by the user's connections and the terms. A real time search engine compiles the results of the user-term index query and retrieves the stored posts from the forward index. The search results may then be ranked and cached before presentation to the searching user.

Real-time content searching in social network

Real time content searching in social network

Online systems generate predictors for predicting actions of users of the online system. The online system receives requests to generate predictor models for predicting whether a user is likely to take an action of a particular action type. The request specifies the type of action and criteria for identifying a successful instance of the action type and a failure instance of the action type. The online system collects data including successful and failure instances of the action type. The online system generates one or more predictors of different types using the generated data. The online system evaluates and compares the performance of the different predictors generated and selects a predictor based on the performance. The online system returns a handle to access the generated predictor to the requester of the predictor.

Customized predictors for user actions in an online system

Querying features based on user actions in online systems

Methods, apparatuses and systems for generating a nickname dictionary that includes associations between names of users and candidate nicknames based on statistical analysis of user communications observed at a network communications facility, such as a social network system, an email provider and the like.

Generation of nickname dictionary based on analysis of user communications

Growing community of inventors

Los Altos, CA, United States of America

Hong Yan