Online model training method, pushing method, device and equipment

Abstract

The embodiment of the invention discloses an online model training method. The method comprises the steps of obtaining a training sample from streaming data, determining an objective function of the model according to the training sample, historical model parameters and non-convex regular terms, determining current model parameters enabling the objective function to be minimum, and updating the model according to the current model parameters. In the online training process, since the non-convex regular term is adopted to replace the L1 regular term for feature screening, the penalty deviationcan be reduced, effective features can be screened out, the sparsity is guaranteed, and the generalization performance of the model is improved. The invention further provides an information pushing method. The method comprises: obtaining user feature data and content feature data, based on the pushing model obtained by the online training model method, determining the probability that a target user is interested in target information according to the user feature data, the content feature data and the pushing model, and determining whether pushing is conducted or not according to the probability that the target user is interested in. The invention further provides an online model training device and an information pushing device.

Description

technical field [0001] The present application relates to the field of Internet technologies, and in particular to a method for online training models based on non-convex regular terms, a push method, device and equipment. Background technique [0002] With the rapid development of Internet technology, the amount of information on the Internet is already in an explosive state. If all the content is placed on the home page of the website, users will not be able to read it, and the utilization rate of information will be very low. Therefore, a push system is needed to help users filter out low-value information. A good notification system can make users visit a site more frequently, and can always push the products or content they want to buy or read for users. [0003] At present, the push system is based on the push algorithm model to push content of interest to users with different needs. The quality of the push system mainly depends on the quality of the push algorithm mo...

AI Technical Summary

Problems solved by technology

However, on higher-dimensional data, when the model is sparse, the model is limited to retain only a very low proportion of effective features, and the generalization performance of the model cannot be guaranteed.

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