Information popularity prediction method and device, and storage medium
A prediction method and popularity technology, applied in the computer field, can solve the problems of poor stability and accuracy of prediction results, and achieve the effect of improving prediction performance, accurate prediction results, and stable popularity prediction
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Embodiment 1
[0025] According to an embodiment of the present invention, a method for predicting information popularity is provided. Such as figure 1 Shown is a flow chart of the method for predicting information popularity according to the first embodiment of the present invention.
[0026] Step S110, acquiring information to be predicted.
[0027] In this embodiment, the information to be predicted is information released by the network platform.
[0028] In this embodiment, the information to be predicted may be long text information.
[0029] The long text information refers to text information whose content is normalized (prescribed format). For example: news information including title, text, picture, publication time, release time and other standardized content.
[0030] Step S120, extracting the popularity-influencing features of the information to be predicted according to the predetermined popularity-influencing feature categories.
[0031] In this embodiment, not only the f...
Embodiment 2
[0060] Before inputting the popularity-influencing features into a pre-trained multi-model predictor that integrates multiple prediction models, the present invention also requires training to obtain the multi-model predictor.
[0061] This embodiment builds a multi-model predictor based on the Stacking integration strategy.
[0062] The idea of Stacking is a hierarchical fusion model. For example, when merging base learners trained with different data, the base learner will be used as the base model, and then a meta-learner will be trained. This meta-learner is used to organize Use the output of the base learner, that is, use the output of the base layer model as the input of the meta-learner, and let the meta-learner assign weights to the output of the base-level model in order to select the most credible one from the output of the base learner. , as the final output.
[0063] This embodiment will describe in detail the steps of obtaining the multi-model predictor. Such ...
Embodiment 3
[0099] This embodiment provides an information popularity prediction device. Such as Figure 6 Shown is a structural diagram of an information popularity prediction device according to a third embodiment of the present invention.
[0100] In this embodiment, the device for predicting information popularity includes, but is not limited to: a processor 610 and a memory 620 .
[0101] The processor 610 is configured to execute the information popularity prediction program stored in the memory 620, so as to realize the information popularity prediction method described in Embodiment 1 to Embodiment 2.
[0102] Specifically, the processor 610 is configured to execute the information popularity prediction program stored in the memory 620, so as to realize the following steps: obtain the information to be predicted; Popularity influence features: input the popularity influence features into a pre-trained multi-model predictor integrating multiple prediction models, and obtain the p...
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