Abstract: In some embodiments, a computing system extrapolates aggregated interaction data associated with users of an online platform by applying a mixed granularity model to generate extrapolated interaction data for each user in the users. The aggregated interaction data includes a total number of occurrences of a target action performed by the users with respect to the online platform. The extrapolated data includes a series of actions leading to the target action for each user. The computing system identifies an impact of each action in the series of actions for each user on leading to the target action based, at least in part, upon the extrapolating a series of actions associated with the user. User interfaces presented on the online platform can be modified based on at least the identified impacts to improve customization of the user interfaces to the users or enhance an experience of the users.

Abstract: This application provides a recommendation model training method in the artificial intelligence (AI) field. The training method includes: obtaining a first training sample; processing attribute information of a first user and information about a first recommended object based on an interpolation model, to obtain an interpolation prediction label of the first training sample; and performing training by using the attribute information of the first user and the information about the first recommended object as an input to a recommendation model and using the interpolation prediction label of the first training sample as a target output value of the recommendation model, to obtain a trained recommendation model. According to the technical solutions of this application, impact of training data bias on recommendation model training can be alleviated, and recommendation model accuracy can be improved.

Abstract: This application provides a recommendation model training method in the artificial intelligence (AI) field. The training method includes: obtaining a first training sample; processing attribute information of a first user and information about a first recommended object based on an interpolation model, to obtain an interpolation prediction label of the first training sample; and performing training by using the attribute information of the first user and the information about the first recommended object as an input to a recommendation model and using the interpolation prediction label of the first training sample as a target output value of the recommendation model, to obtain a trained recommendation model. According to the technical solutions of this application, impact of training data bias on recommendation model training can be alleviated, and recommendation model accuracy can be improved.

Abstract: The invention relates to an assembled vertebral body used in a cervical reconstruction operation, comprising: an upper connecting element, an artificial vertebral body element and a lower connecting element, wherein the upper connecting element is disposed at an upper part of the artificial vertebral body element, the lower connecting element is disposed at an lower part of the artificial vertebral body element, and wherein the artificial vertebral body element is assembled with the upper connecting element and the lower connecting element, respectively. The invention has the advantages that the artificial vertebral body element is customized in accordance with the characteristics of patients and printed in 3D, and the length of the artificial vertebral body element can be precisely adjusted to adapt to the patient.

Abstract: The invention relates to an assembled vertebral body used in a cervical reconstruction operation, comprising: an upper connecting element, an artificial vertebral body element and a lower connecting element, wherein the upper connecting element is disposed at an upper part of the artificial vertebral body element, the lower connecting element is disposed at an lower part of the artificial vertebral body element, and wherein the artificial vertebral body element is assembled with the upper connecting element and the lower connecting element, respectively. The invention has the advantages that the artificial vertebral body element is customized in accordance with the characteristics of patients and printed in 3D, and the length of the artificial vertebral body element can be precisely adjusted to adapt to the patient.