Abstract: A pipeline-based machine learning method includes: determining a plurality of target components from candidate components configured to construct a machine learning model; performing standardization processing on input data and output data of the plurality of target components to obtain a plurality of standardized components respectively corresponding to the plurality of target components; assembling, based on a connection relationship between the plurality of standardized components, the plurality of standardized components into a pipeline corresponding to the machine learning model; performing scheduling processing on the plurality of standardized components included in the pipeline, to obtain a scheduling result; and executing, based on THE scheduling result, a machine learning task corresponding to the machine learning model.

Abstract: A method of collaborative filtering in combination with time factor includes: establishing an exponential smoothing model; acquiring a time period proposed for the exponential smoothing model, the time period includes a plurality of time cycles; acquiring a plurality of user identifiers and user preference degree values of the user identifiers over a specified product during the plurality of time cycles; performing iterative calculations of the user preference degree values utilizing the exponential smoothing model, and obtaining smoothing results corresponding to the time cycles; generating a sparse matrix utilizing the user identifiers and the smoothing result corresponding to the time cycles, the sparse matrix includes a plurality of user preference degrees to be predicted; acquiring a collaborative filtering model and inputting the smoothing results corresponding to the time cycles into the collaborative filtering model; and training through the collaborative filtering model, calculating and obtaining pre

Abstract: A method of collaborative filtering in combination with time factor includes: establishing an exponential smoothing model; acquiring a time period proposed for the exponential smoothing model, the time period includes a plurality of time cycles; acquiring a plurality of user identifiers and user preference degree values of the user identifiers over a specified product during the plurality of time cycles; performing iterative calculations of the user preference degree values utilizing the exponential smoothing model, and obtaining smoothing results corresponding to the time cycles; generating a sparse matrix utilizing the user identifiers and the smoothing result corresponding to the time cycles, the sparse matrix includes a plurality of user preference degrees to be predicted; acquiring a collaborative filtering model and inputting the smoothing results corresponding to the time cycles into the collaborative filtering model; and training through the collaborative filtering model, calculating and obtaining pre

Abstract: Disclosed are a system, device, method, and readable storage medium for issuing an auto insurance investigation task. The system includes: a task issuance module that acquires associated crowdsourcing parameters of an auto insurance investigation task, determining one or more public investigators matching the investigation task based on a preset first analysis rule and a preset model, and issuing the investigation task carrying the crowdsourcing parameters to mobile terminals of the determined public investigators; a data acquisition module that obtains corresponding investigation data of the investigation task from a mobile terminal of at least one of the determined public investigators who have accepted the task after recognizing the issued investigation task has been accepted; and a data analysis module that analyzes the obtained investigation data based on a preset second analysis rule and finding out the investigation data that conforms to preset conditions as the task result to be adopted.