Tencent Technology (Shenzhen) Company Limited

Ping an Technology (Shenzhen) Co., Ltd.

Luyang Cao

Jing Jing Xiao

Jianming Wang

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.

Pipeline-based machine learning method and apparatus, electronic device, and computer readable storage medium

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 predictive values of the plurality of user preference degrees to be predicted in the sparse matrix.

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Shenzhen, China

Luyang Cao