Abstract: One or more computing devices, systems, and/or methods are provided. In an example, an internet resource identification item associated with one or more internet resources may be identified. User activity information associated with a plurality of events may be analyzed to determine a plurality of sets of text associated with the internet resource identification item, wherein each set of text of the plurality of sets of text is associated with an event, of the plurality of events, associated with an internet resource of the one or more internet resources. A plurality of term representations may be determined based upon the plurality of sets of text. A user intention-based representation associated with the internet resource identification item may be generated based upon the plurality of term representations. A content item may be selected for presentation via a client device based upon the user intention-based representation.

Abstract: The present teaching relates to method, system, medium, and implementations for joint prediction. Training data is obtained with information about a plurality of users collected from different sources and ground truth demographics/interests associated with each of the plurality users. Based on the training data, a joint prediction model is trained for simultaneously predicting multiple pieces of demographic/interest information. When information about a user from different sources is received, a joint feature vector is derived therefrom, which is then used by the trained joint prediction model to predict multiple pieces of demographic/interest information about the user.

Abstract: The present teaching relates to method and system for prediction of demographic/interest based on data from different sources (DFDS) relating to users. The DFDS is processed to link data from different sources associated with each of the users. The linked DFDS associated with each user is used to obtain a joint feature vector for simultaneously predicting, based on a joint prediction model, multiple pieces of demographic/interest information of the user. Based on the predicted demographics/interests for different users, content is distributed to target users identified based on their respective predicted demographics/interests.

Abstract: One or more computing devices, systems, and/or methods for generating time-preserving embeddings are provided. User trails of user activities performed by users are generated. Frequencies at which the activities were performed are identified. Indices are assigned to a set of activities identified from the activities as having frequencies above a threshold. Activity descriptions of the set of activities are mapped to the indices to generate a vocabulary. A model is trained using the user trails, timestamps of the activities, and the vocabulary to learn a set of time-preserving embeddings.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, an internet resource identification item associated with one or more internet resources may be identified. User activity information associated with a plurality of events may be analyzed to determine a plurality of sets of text associated with the internet resource identification item, wherein each set of text of the plurality of sets of text is associated with an event, of the plurality of events, associated with an internet resource of the one or more internet resources. A plurality of term representations may be determined based upon the plurality of sets of text. A user intention-based representation associated with the internet resource identification item may be generated based upon the plurality of term representations. A content item may be selected for presentation via a client device based upon the user intention-based representation.

Abstract: This teaching relates to predictive targeting. Training data are obtained with pairs of data. Each pair includes an ad opportunity context corresponding to an ad served to a plurality of audiences and a label vector having a plurality of labels, each of which indicates a reaction, with respect to the ad served, of a corresponding one of the audiences in the ad opportunity context. Based on the training data, model parameters of a joint predictive model are learned via machine learning based on an initialized model with initial model parameters by minimizing a loss in an iterative process. The learned joint predictive model is to be used to map an input context of an ad opportunity to an output label vector having a plurality of probabilities, each of which predicts a likelihood of a reaction of a corresponding one of the audiences to the input context of the ad opportunity.

Abstract: One or more computing devices, systems, and/or methods are provided. In an example, an internet resource identification item associated with one or more internet resources may be identified. User activity information associated with a plurality of events may be analyzed to determine a plurality of sets of text associated with the internet resource identification item, wherein each set of text of the plurality of sets of text is associated with an event, of the plurality of events, associated with an internet resource of the one or more internet resources. A plurality of term representations may be determined based upon the plurality of sets of text. A user intention-based representation associated with the internet resource identification item may be generated based upon the plurality of term representations. A content item may be selected for presentation via a client device based upon the user intention-based representation.

Abstract: One or more computing devices, systems, and/or methods for generating time-preserving embeddings are provided. User trails of user activities performed by users are generated. Frequencies at which the activities were performed are identified. Indices are assigned to a set of activities identified from the activities as having frequencies above a threshold. Activity descriptions of the set of activities are mapped to the indices to generate a vocabulary. A model is trained using the user trails, timestamps of the activities, and the vocabulary to learn a set of time-preserving embeddings.