Software Technology for Flexible multi-task neutral and flexible network for content ranking

Facebook, Inc. (Menlo Park, CA)

When a piece of content has unknown tasks that a user viewing the content performs in an online system the system online predicts the likelihood of interaction with each content item with a prediction model that is associated with a range of tasks. The model for prediction comprises several layers: many are separate layers, a few shared layers, as well as several separate layers. Each independent layer is configured to identify features, specific to each task, that aren’t common across all tasks. The shared layers are configured to collect common features that are shared across the various tasks. Based on features from the shared layer and the plurality each layer could be used to predict the likelihood that the viewer will perform the task associated with it.

This disclosure is general in nature and is related to content ranking. It is focused more on ranking content based on prediction of interactions between content creators and viewers using flexible multitask neutral networks.

The term feed, sometimes referred to as Newsfeed, is an ongoing updating list of content items (e.g. posts) that are available to a user via an online platform. The feed is ranked according to how likely it is that a user will find each story worthwhile. The neural network model, which can predict how likely a person will interact with each piece on the feed, is a way to rank content items in feeds. A neural network model is composed of several layers that are connected to each one. Each layer has many interconnected neurons (also known as nodes). A prediction procedure that is based on the neural network model needs large amounts of computations to calculate the weight of each neuron and for eachconnection. This process of prediction causes high usage of processing units (e.g., central processor unit, or graphics processing unit). The high usage of processor units could prevent the neural network’s ability to construct a huge neural structure, anticipate huge numbers of events per content item, or build the neural model using huge amounts of data for training.

A system online can determine the probability of a user doing a task or taking action with a content object for various tasks. You can share, like or comment on and post a content item to share with a message. An online system predicts the possibility that users will be able to perform various tasks when using a content object. Each content item is assigned a feature vector in the online system. A feature vector could include characteristics of a corresponding content item, the characteristics of a poster who posted the item in question, the characteristics of the viewer and the relationship between the poster, the content item and the viewer. The online system forecasts the likelihood of interaction with each content item through a model of prediction that is linked with a range of tasks. The model of prediction comprises a plurality of independent layers, a plurality of shared layers as well as a plurality of separate layers. Each layer is able to discover features specific to the task at hand. The shared layers are configured to collect common features that are shared across the variety of tasks. Each separate layer is configured to predict likelihood of the user viewing tasks associated with the separatelayer based on the features extracted from the plurality of independent layers and the many shared layers. Based on the predictions that the system makes, it evaluates the content items of the various layers. The ranking is used to place the content items in feeds where the user could perform tasks with the content item.

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