Facebook, Inc.

Meta Platforms, Inc.

Wenlin Chen

Tianshi Gao

Liang Xiong

Xianjie Chen

To present one or more content items to users of an online system, the online system identifies a content evaluation pipeline including an order of a plurality of stages having one or more computer models for evaluating a likelihood of user interaction with a content item. The content evaluation pipeline selects a decreasing number of content items, from each stage of the order, according to the order of the stages. The online system optimizes the selection of content items selected at the plurality of stages of the content evaluation pipeline by training the computer models to predict content selection values that the subsequent model would generate for a content items in a training data set and content items that the subsequent model would select for input to the next stage of the content evaluation pipeline.

Model compression for selecting content

An online system trains a content selection model based on a selected subset of presented content items as well as a sampled set of content items. The content selection model is configured to receive a set of features characterizing a user-content item pair and output a likelihood that the user will interact with the content item. The sampled set of content items may include content items that were not selected for display based on their likelihoods in addition to those that were selected, and may represent a wider distribution of user-content item pairs than the selected subset. By incorporating the sampled set of content items as well as the selected subset of content items in the training process, the online system can reduce bias in the content selection process such that content items similar to the unselected subset can also be adequately represented.

Training content selection models with reduced bias

A preprocessing module of a neural network has a first input and second input. The module generates multiple, different first latent vector representations of its first input, and multiple, different second latent vector representations of its second input. The module then models pairwise interactions between every unique pairwise combination of the first and second latent vector representations. The module then produces an intermediate output by combining the results of the modeled pairwise interactions.

Mixed machine learning architecture

In one embodiment, a method includes a preprocessing stage of a neural network model, where the preprocessing stage includes first and second preprocessing modules. Each of the two modules has first input that may receive a dense input and a second input that may receive a sparse input. Each module generates latent vector representations of their respective first and second inputs, and combine the latent vectors with the original first input to define an intermediate output. The intermediate output of the first module is fed into the first input of the second module.

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Mountain View, CA, United States of America

Wenlin Chen