Linkedin Corporation

Microsoft Technology Licensing, LLC

Jonathan David Traupman

Liang Zhang

Deepak Agarwal

Bo Long

Frank Emmanuel Astier

Anmol Bhasin

Yurong Shi

Lizabeth Li

Venu Javarappa

Tarun Kumar

Techniques for predicting a user response to content are described. According to various embodiments, a configuration file is accessed, where the configuration file includes a user-specification of raw data accessible via external data sources and raw data encoding rules. In some embodiments, the raw data includes raw member data associated with a particular member and raw content data associated with a particular content item. Thereafter, source modules encode the raw data from the external data sources into feature vectors, based on the raw data encoding rules. An assembler module assembles one or more of the feature vectors into an assembled feature vector, based on user-specified assembly rules included in the configuration file. A prediction module performs a prediction modeling process based on the assembled feature vector and a prediction model, to predict a likelihood of the particular member performing a particular user action on the particular content item.

Systems and methods for content response prediction

The disclosed embodiments relate to a system that uses data from an online social network to optimize subscription offers. During operation of the online social network, the system gathers data associated with subscription offers that were presented to members of the online social network, including information about which subscription offers were converted. Next, the system uses a machine-learning technique to train a model based on the gathered data. Finally, the system uses the trained model to select subscription offers to present to a member of the online social network.

Using data obtained from an online social network to optimize subscription offers

Techniques for predicting a user response to content are provided. In example embodiments, one or more feature vectors are assembled into an assembled feature vector. A particular one of the feature vectors not being available is determined. In response to determining that the particular one of the feature vectors is not available, the particular feature vector is ignored based on an importance value associated with the particular feature vector. A substitute value associated with the particular feature vector is inserted into a portion of the assembled feature vector associated with the particular feature vector. A prediction modeling process based on the assembled feature vector and a prediction model is performed to predict a likelihood of a particular member performing a particular user action on a particular content item.

Content response prediction

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Oakland, CA, United States of America

Jonathan David Traupman