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Mobile application download prediction method based on cluster

A forecasting method, technology for mobile applications, used in data processing applications, business, instrumentation, etc.

Active Publication Date: 2015-08-19
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But currently there is no way to discover potential high-quality apps

Method used

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  • Mobile application download prediction method based on cluster
  • Mobile application download prediction method based on cluster
  • Mobile application download prediction method based on cluster

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Experimental program
Comparison scheme
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Embodiment Construction

[0043] Refer to attached picture.

[0044] The method for predicting download volume of the present invention comprises the following steps:

[0045] 1) Obtain the historical data of the app to be predicted from the background data, including the download amount of the app within the known m days;

[0046] 2) Source data processing: process the data in step 1) to generate a discrete time series x of length L to represent the download curve of each app, so far the download curves of all apps form a discrete time series training data set, specifically Include the following steps:

[0047] Described step 2) specifically comprises the following steps:

[0048] (1) The download threshold thr is given; thr is an artificially specified parameter, and the default thr=0.1. On the basis of thr determination, the L of the entire training data set can be calculated 1 and L 2 , generally adjust thr so ​​that L 1 +L 2 Not less than 2 / 3 of the length of the original sequence.

[0049]...

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Abstract

The invention provides a mobile application download prediction method based on cluster. The method comprises: 1) collecting all known app historical data from background data; 2) performing source data processing; 3) and performing mode clustering to predict downloads. The method performs app download prediction in an innovative manner, and is in obvious need in the app field, and has good expansion. The method has very wide application prospect in other E-commerce fields. Based on giving consideration on app download curve features, a K-means algorithm is modified, and solution efficiency is optimized. The whole cluster process can be completed offline, and predicted value calculation of downloads is completed online, so that user experience is optimized.

Description

technical field [0001] The invention belongs to the field of mobile Internet applications. It mainly uses the cosine similarity and K center clustering method to realize a prediction mechanism for mobile application (app) downloads. Background technique [0002] With the advent of the mobile Internet tide, mobile applications (apps) have flourished in recent years. Taking Google Play as an example, the number of apps currently exceeds 1 million, and the cumulative downloads exceed 50 billion. Regardless of the app market for android or ios, its web pages or display pages on the application side are very limited, and most apps cannot be effectively displayed. The industry has gradually introduced the recommendation mechanism of traditional e-commerce into the app field, as a way for users to find high-quality apps of interest, and it is also an effective way for some less popular high-quality apps to be exposed. But there is currently no method for discovering potential hig...

Claims

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Application Information

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IPC IPC(8): G06Q30/00
Inventor 吴健邱奇波陈亮邓水光李莹尹建伟吴朝晖
Owner ZHEJIANG UNIV
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