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On-line serial content popularity prediction method based on autoregressive model

A technique of autoregressive models and forecasting methods, used in forecasting, special data processing applications, instruments, etc.

Active Publication Date: 2014-07-09
INST OF ADVANCED TECH UNIV OF SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to solve the defect in the prior art that there is no method for predicting the popularity of online serial content, and to provide a method for predicting the popularity of online serial content based on an autoregressive model to solve the above problems

Method used

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  • On-line serial content popularity prediction method based on autoregressive model
  • On-line serial content popularity prediction method based on autoregressive model
  • On-line serial content popularity prediction method based on autoregressive model

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Embodiment Construction

[0086] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0087] Such as figure 1 As shown, a kind of online serial content popularity prediction method based on autoregressive model of the present invention, comprises the following steps:

[0088] The first step is the acquisition of training data, crawling the overall playback volume trend of online serial content, analyzing the HTML source code of the overall playback volume trend page, and analyzing the HTML source code of each episode's playback volume trend page.

[0089] The acquisition of training data is the content of the existing technology. The training data includes many TV dramas that have been released recently and the broadcast volume of each episode in each unit time (the unit time may be a day or a week). These TV dramas sh...

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Abstract

The invention relates to an on-line serial content popularity prediction method based on an autoregressive model. Compared with the prior art, the method overcomes the defect that no on-line serial content popularity prediction method exists in the prior art. The method includes the steps that training data are acquired, wherein crawling of the overall playback amount trend of the on-line serial content is carried out, an HTML source code of an overall playback amount trend page is analyzed, and HTML source codes of single-episode playback amount trend pages are analyzed; the popularity is predicated, wherein the popularity of new serial content is predicated through the autoregressive model. By means of the method, detection of the popularity of the on-line serial content can be achieved, a new index for evaluating the quality of the serial content is designed through model parameters serving as derivatives of a diversion model, and therefore the method has great significance in content recommendation.

Description

technical field [0001] The invention relates to the technical field of online serial content popularity forecasting, in particular to an auto-regressive model-based online serial content popularity forecasting method. Background technique [0002] With the development of the modern Internet, sharing content online has become an important part of people's daily entertainment. As an important form of online content, online serial content refers to content that contains many episodes and is continuously updated regularly, such as TV series, variety entertainment programs, cartoons, serial novels, etc. More and more network service providers or websites (such as Youku, iQiyi, Tencent Video, Qidian Chinese Network, Hongxiu Tianxiang, etc.) provide users with online serial content directly, and keep online synchronization with other sources (such as TV stations) . For example, Youku, as the largest video website in China, provides TV dramas, cartoons, and entertainment programs ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06F17/30
Inventor 常标祝恒书谭昶陈恩红刘淇熊辉
Owner INST OF ADVANCED TECH UNIV OF SCI & TECH OF CHINA
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