Abstract: An interactive recommendation system is provided. The system comprises an information generation module configured to: provide a set of information contents for outputting in an information interface, wherein each of the set of information contents is associated with an information item; receive a selection of one of the set of information contents that signals the user's interest or disinterest in an information item; determine, based on a model and history data related to prior activities of the user including a plurality of selections from the set of information contents, one or more information items to be included in recommendation information for the user; provide the recommendation information for outputting in a recommendation information interface; and update, based on the history data, the set of information contents for outputting in the information interface.

Abstract: A method for text sequence style transfer by two encoder-decoders, including generating, by a first encoder-decoder network model, an output sequence based on a first input sequence and an input sequence style, wherein the output sequence is associated with a second sequence, generating, by a second-encoder decoder network model, a prediction of the first input sequence based on the first input sequence, the output sequence, and a first input sequence style associated with the first input sequence, generating, by a classifier, a prediction of the first input sequence style based on the prediction of the first input sequence, and updating the neural network model based on comparisons between the output sequence and the second sequence, between the prediction of the first input sequence and the first input sequence, and between the prediction of the first input sequence style and the first input sequence style.

Abstract: A method for text sequence style transfer by two encoder-decoders, including generating, by a first encoder-decoder network model, an output sequence based on a first input sequence and an input sequence style, wherein the output sequence is associated with a second sequence, generating, by a second-encoder decoder network model, a prediction of the first input sequence based on the first input sequence, the output sequence, and a first input sequence style associated with the first input sequence, generating, by a classifier, a prediction of the first input sequence style based on the prediction of the first input sequence, and updating the neural network model based on comparisons between the output sequence and the second sequence, between the prediction of the first input sequence and the first input sequence, and between the prediction of the first input sequence style and the first input sequence style.

Abstract: A method for generating one or more push notifications to a user device is described. The method comprises: obtaining history data representing a history of online activities of a user and candidate data representing a set of candidate information; generating, based on the history data and the candidate data, user profile vectors representing a user profile associated with the user and content vectors representing a set of content profiles associated with the set of candidate information; generating, based on a machine learning model trained with a history of online activities, embedding user feature vectors and embedding content feature vectors based on the history data and the candidate data; and providing for transmission information for one or more push notifications including first candidate information of to a user device associated with the user, the first candidate information being determined from the set of candidate information based on the aforementioned vectors.

Abstract: An interactive recommendation system is provided. The system comprises an information generation module configured to: provide a set of information contents for outputting in an information interface, wherein each of the set of information contents is associated with an information item; receive a selection of one of the set of information contents that signals the user's interest or disinterest in an information item; determine, based on a model and history data related to prior activities of the user including a plurality of selections from the set of information contents, one or more information items to be included in recommendation information for the user; provide the recommendation information for outputting in a recommendation information interface; and update, based on the history data, the set of information contents for outputting in the information interface.

Abstract: Data processing method, apparatus, device and computer readable storage media are provided. A first product object is selected. One or more candidate product objects that are similar to the first product object are determined. A candidate question set is constructed for the first product object based on existing user questions of the one or more candidate product objects. Based on the candidate question set of the first product object, a recommended question set of the first product object is constructed. The data processing method, apparatus, device and computer readable storage media of the embodiments of the present disclosure are capable of pushing questions.

Abstract: A method for generating one or more push notifications to a user device is described. The method comprises: obtaining history data representing a history of online activities of a user and candidate data representing a set of candidate information; generating, based on the history data and the candidate data, user profile vectors representing a user profile associated with the user and content vectors representing a set of content profiles associated with the set of candidate information; generating, based on a machine learning model trained with a history of online activities, embedding user feature vectors and embedding content feature vectors based on the history data and the candidate data; and providing for transmission information for one or more push notifications including first candidate information of to a user device associated with the user, the first candidate information being determined from the set of candidate information based on the aforementioned vectors.