[go: up one dir, main page]

CN110147517B - Third-party prediction method for activeness of news client - Google Patents

Third-party prediction method for activeness of news client Download PDF

Info

Publication number
CN110147517B
CN110147517B CN201910433268.7A CN201910433268A CN110147517B CN 110147517 B CN110147517 B CN 110147517B CN 201910433268 A CN201910433268 A CN 201910433268A CN 110147517 B CN110147517 B CN 110147517B
Authority
CN
China
Prior art keywords
news
liveness
news client
unit time
client
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910433268.7A
Other languages
Chinese (zh)
Other versions
CN110147517A (en
Inventor
王严博
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chinaso Information Technology Co ltd
Original Assignee
Chinaso Information Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chinaso Information Technology Co ltd filed Critical Chinaso Information Technology Co ltd
Priority to CN201910433268.7A priority Critical patent/CN110147517B/en
Publication of CN110147517A publication Critical patent/CN110147517A/en
Application granted granted Critical
Publication of CN110147517B publication Critical patent/CN110147517B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Development Economics (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Databases & Information Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Game Theory and Decision Science (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Machine Translation (AREA)

Abstract

The invention discloses a third-party prediction method for activeness of news clients, which comprises the steps of acquiring news contents of all news clients by using crawlers; according to the obtained news content, defining the manuscript sending amount, the total reading number, the APP reading number balance factor, the manuscript sending number curvature in unit time, the reading number curvature in unit time, the manuscript comment number and the comment number in unit time; and predicting the liveness of each news client by adopting a liveness numerical formula according to the defined parameters. The advantages are that: by using the method, the self-adaptive parameter adjustment can be smoothly and accurately carried out according to different clients, and the problem that the transverse contrast of the statistical data of a single client cannot be measured is avoided; by adopting the machine learning-based method, the vitality prediction of the news client is realized, and news workers, advertisement delivery workers and public opinion workers can further utilize the prediction result to predict the working effect in advance.

Description

Third-party prediction method for activeness of news client
Technical Field
The invention relates to the field of statistics, in particular to a third-party news client liveness prediction method.
Background
News information is one of the most concerned industry applications in the internet industry, news clients are more endless, and in order to evaluate the influence of news media, the evaluation of the news client is more important under the current internet. The one-way declaration mode of the number of active users of each news client lacks a public and uniform measurement scale for upstream and downstream users.
Disclosure of Invention
The invention aims to provide a third-party news client liveness prediction method, so that the problems in the prior art are solved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a third-party prediction method for activeness of news client comprises the following steps,
a third-party prediction method for activeness of news client comprises the following steps,
s1, acquiring news contents of each news client by using a crawler, setting sampling constants of each news client according to the difference of postings numbers of different news clients, wherein all the news clients follow the same sampling period;
s2, defining draft sending quantity, total reading number, APP reading number balance factor, draft sending number curvature in unit time, reading number curvature in unit time, manuscript comment number and comment number in unit time according to the obtained news content;
and S3, predicting the liveness of each news client by adopting a liveness numerical formula according to the parameters defined in the step S2.
Preferably, the contribution amount is the sum of the number of contributions issued by a certain news client in a sampling period; the manuscript refers to an article visible in the news client list and is defined as Ps; the reading total is the sum of all manuscripts of the news client in a sampling period and is defined as Vs; the APP reading number balance factor is a balance factor for fitting the reading number of the news client to a uniform reference and is defined as Avgs; the curvature of the number of releases in unit time is the curvature of the number of releases in unit time of the news client in a sampling period, and is defined as Dpr, and the value is taken through the following formula,
Figure BDA0002069699460000021
the curvature of the reading number in unit time is the curvature of the reading number of the news client in unit time in a sampling period, and is defined as Vpr, and the value is taken through the following formula,
Figure BDA0002069699460000022
the number of the comments of the manuscript is the sum of the number of the comments of all original manuscripts of the news client; the number of reviews in unit time is the number of reviews of the news client in unit time in a sampling period, and is defined as Cpr, and the Cpr is taken as the value through the following formula,
Figure BDA0002069699460000023
preferably, the numerical formula of the activity is as follows,
Figure BDA0002069699460000024
wherein, Dau is the liveness of the news client; rri is a penalty coefficient; i is a calculation period; maxi is the maximum number of activities in a calculation period; mini is the minimum number of activities in a calculation period.
Preferably, the penalty factor takes the following values,
Figure BDA0002069699460000025
where x represents the review browsing activity ratio.
Preferably, x is calculated by the following formula,
Figure BDA0002069699460000026
Figure BDA0002069699460000027
Figure BDA0002069699460000031
Figure BDA0002069699460000032
wherein y represents the ratio of the manuscript browsing liveness; cr represents review liveness; dr represents the activity of manuscript sending; vr represents browsing liveness.
Preferably, the comment liveness, the draft liveness and the browsing liveness are respectively obtained by the following formulas,
Figure BDA0002069699460000033
Figure BDA0002069699460000034
Figure BDA0002069699460000035
wherein,
Figure BDA0002069699460000036
c represents the number of comments of a single article; d represents the number of articles; v represents a single article reading number; j represents the sampling period; os represents the original manuscript quantity.
The invention has the beneficial effects that: 1. by the method, universal prediction can be performed on any news client. 2. The method can smoothly and accurately adjust the self-adaptive parameters according to different clients, and avoids the problem that the transverse contrast of the statistical data of a single client cannot be measured. 3. By adopting the machine learning-based method, the vitality prediction of the news client is realized, and news workers, advertisement delivery workers and public opinion workers can further utilize the prediction result to predict the working effect in advance.
Drawings
FIG. 1 is a flow chart illustrating a prediction method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the present invention provides a third party news client liveness prediction method, which includes the following steps,
s1, acquiring news contents of each news client by using a crawler, setting sampling constants of each news client according to the difference of postings numbers of different news clients, wherein all the news clients follow the same sampling period;
s2, defining draft sending quantity, total reading number, APP reading number balance factor, draft sending number curvature in unit time, reading number curvature in unit time, manuscript comment number and comment number in unit time according to the obtained news content;
and S3, predicting the liveness of each news client by adopting a liveness numerical formula according to the parameters defined in the step S2.
In this embodiment, the contribution amount is the total number of contributions issued by a certain news client in a sampling period; the manuscript refers to an article visible in the news client list and is defined as Ps; the reading total is the sum of all manuscripts of the news client in a sampling period and is defined as Vs; the APP reading number balance factor is a balance factor for fitting the reading number of the news client to a uniform reference and is defined as Avgs; the curvature of the number of releases in unit time is the curvature of the number of releases in unit time of the news client in a sampling period, and is defined as Dpr, and the value is taken through the following formula,
Figure BDA0002069699460000041
the curvature of the reading number in unit time is the curvature of the reading number of the news client in unit time in a sampling period, and is defined as Vpr, and the value is taken through the following formula,
Figure BDA0002069699460000042
the number of the comments of the manuscript is the sum of the number of the comments of all original manuscripts of the news client; the number of comments in unit time is the number of comments in unit time of the news client in a sampling period, and is defined as Cpr, the value of the Cpr is obtained by taking down a formula,
Figure BDA0002069699460000043
in this embodiment, the activity numerical formula is as follows,
Figure BDA0002069699460000044
wherein, Dau is the liveness of the news client; rri is a penalty coefficient; i is a calculation period; maxi is the maximum number of activities in a calculation period; mini is the minimum number of activities in a calculation period.
In this embodiment, the penalty factor takes the following values,
Figure BDA0002069699460000051
where x represents the review browsing activity ratio.
In this embodiment, x is calculated by the following formula,
Figure BDA0002069699460000052
Figure BDA0002069699460000053
Figure BDA0002069699460000054
Figure BDA0002069699460000055
wherein y represents the ratio of the manuscript browsing liveness; cr represents review liveness; dr represents the activity of manuscript sending; vr represents browsing liveness.
In this embodiment, the review liveness, the submission liveness and the browsing liveness are respectively obtained by the following formulas,
Figure BDA0002069699460000056
Figure BDA0002069699460000057
Figure BDA0002069699460000058
wherein,
Figure BDA0002069699460000061
c represents the number of comments of a single article; d represents the number of articles; v represents a single article reading number; j represents the sampling period; os represents the original manuscript quantity.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention provides a third-party news client liveness prediction method, which can be used for universally predicting any news client; the method can smoothly and accurately adjust the self-adaptive parameters according to different clients, and avoids the problem that the transverse contrast of the statistical data of a single client cannot be measured; meanwhile, the machine learning-based method is adopted, so that the vitality prediction of the news client is realized, and news workers, advertisement delivery workers and public opinion workers can further utilize the prediction result to predict the working effect in advance.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.

Claims (4)

1. A third-party news client liveness prediction method is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1, acquiring news contents of each news client by using a crawler, setting sampling constants of each news client according to the difference of postings numbers of different news clients, wherein all the news clients follow the same sampling period;
s2, defining draft sending quantity, total reading number, APP reading number balance factor, draft sending number curvature in unit time, reading number curvature in unit time, manuscript comment number and comment number in unit time according to the obtained news content;
s3, predicting the liveness of each news client by adopting a liveness numerical formula according to the parameters defined in the step S2;
the manuscript sending amount is the sum of manuscripts issued by a certain news client in a sampling period; the manuscript refers to an article visible in the news client list and is defined as Ps; the reading total is the sum of all manuscripts of the news client in a sampling period and is defined as Vs; the APP reading number balance factor is a balance factor for fitting the reading number of the news client to a uniform reference and is defined as Avgs; the curvature of the number of releases in unit time is the curvature of the number of releases in unit time of the news client in a sampling period, and is defined as Dpr, and the value is taken through the following formula,
Figure FDA0002459530340000011
the curvature of the reading number in unit time is the curvature of the reading number of the news client in unit time in a sampling period, and is defined as Vpr, and the value is taken through the following formula,
Figure FDA0002459530340000012
the number of the comments of the manuscript is the sum of the number of the comments of all original manuscripts of the news client; the number of reviews in unit time is the number of reviews of the news client in unit time in a sampling period, and is defined as Cpr, and the Cpr is taken as the value through the following formula,
Figure FDA0002459530340000013
the numerical formula of the activity degree is as follows,
Figure FDA0002459530340000014
wherein, Dau is the liveness of the news client; rri is a penalty coefficient; i is a calculation period; maxi is the maximum number of activities in a calculation period; mini is the minimum number of activities in a calculation period; cr represents review liveness; dr represents the activity of manuscript sending; vr represents browsing liveness.
2. The news client liveness third party prediction method of claim 1, wherein: the penalty factor takes the value as follows,
Figure FDA0002459530340000021
where x represents the review browsing activity ratio.
3. The news client liveness third party prediction method of claim 2, wherein: the x is calculated by the following formula,
Figure FDA0002459530340000022
Figure FDA0002459530340000023
Figure FDA0002459530340000024
Figure FDA0002459530340000025
wherein y represents the draft browsing liveness ratio.
4. The news client liveness third party prediction method of claim 3, wherein: the comment liveness, the manuscript sending liveness and the browsing liveness are respectively obtained by the following formulas,
Figure FDA0002459530340000026
Figure FDA0002459530340000027
Figure FDA0002459530340000031
wherein,
Figure FDA0002459530340000032
c represents the number of comments of a single article; d represents the number of articles; v represents a single article reading number; j represents the sampling period; os represents the original manuscript quantity.
CN201910433268.7A 2019-05-23 2019-05-23 Third-party prediction method for activeness of news client Active CN110147517B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910433268.7A CN110147517B (en) 2019-05-23 2019-05-23 Third-party prediction method for activeness of news client

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910433268.7A CN110147517B (en) 2019-05-23 2019-05-23 Third-party prediction method for activeness of news client

Publications (2)

Publication Number Publication Date
CN110147517A CN110147517A (en) 2019-08-20
CN110147517B true CN110147517B (en) 2020-06-09

Family

ID=67592785

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910433268.7A Active CN110147517B (en) 2019-05-23 2019-05-23 Third-party prediction method for activeness of news client

Country Status (1)

Country Link
CN (1) CN110147517B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114511133A (en) * 2021-12-29 2022-05-17 深圳市网联安瑞网络科技有限公司 Network platform heat information prediction method, system and terminal based on user activity

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102663101A (en) * 2012-04-13 2012-09-12 北京交通大学 Sina microblog-based user grade sequencing algorithm
CN104916004A (en) * 2014-03-13 2015-09-16 通用汽车环球科技运作有限责任公司 Method and apparatus of tracking and predicting usage tread of in-vehicle apps
CN105912599A (en) * 2016-03-31 2016-08-31 维沃移动通信有限公司 Ranking method and terminal of terminal application programs
CN107168986A (en) * 2017-03-23 2017-09-15 国家计算机网络与信息安全管理中心 The analysis method of news APP application liveness
CN108280073A (en) * 2017-01-05 2018-07-13 北大方正集团有限公司 The influence power analysis method and system of news client
CN109040218A (en) * 2018-07-25 2018-12-18 山东中创软件商用中间件股份有限公司 A kind of application system liveness determines method, apparatus, equipment and storage medium

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016183565A1 (en) * 2015-05-14 2016-11-17 Walleye Software, LLC Remote data object publishing/subscribing system having a multicast key-value protocol
CN107153908A (en) * 2017-03-24 2017-09-12 国家计算机网络与信息安全管理中心 Mobile news App influence power ranking methods
CN107577688B (en) * 2017-04-25 2020-11-10 上海市互联网信息办公室 Original article influence analysis system based on media information acquisition

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102663101A (en) * 2012-04-13 2012-09-12 北京交通大学 Sina microblog-based user grade sequencing algorithm
CN104916004A (en) * 2014-03-13 2015-09-16 通用汽车环球科技运作有限责任公司 Method and apparatus of tracking and predicting usage tread of in-vehicle apps
CN105912599A (en) * 2016-03-31 2016-08-31 维沃移动通信有限公司 Ranking method and terminal of terminal application programs
CN108280073A (en) * 2017-01-05 2018-07-13 北大方正集团有限公司 The influence power analysis method and system of news client
CN107168986A (en) * 2017-03-23 2017-09-15 国家计算机网络与信息安全管理中心 The analysis method of news APP application liveness
CN109040218A (en) * 2018-07-25 2018-12-18 山东中创软件商用中间件股份有限公司 A kind of application system liveness determines method, apparatus, equipment and storage medium

Also Published As

Publication number Publication date
CN110147517A (en) 2019-08-20

Similar Documents

Publication Publication Date Title
Scharkow et al. How social network sites and other online intermediaries increase exposure to news
US11222268B2 (en) Determining algorithmic multi-channel media attribution based on discrete-time survival modeling
US9183562B2 (en) Method and system for determining touchpoint attribution
US8396742B1 (en) System and method for optimizing paid search advertising campaigns based on natural search traffic
US20170221080A1 (en) Brand Analysis
US20090037257A1 (en) System for electronic commerce
AU2013204865B2 (en) Methods and apparatus to share online media impressions data
US20110231239A1 (en) Method and system for attributing an online conversion to multiple influencers
Weideman et al. Parallel search engine optimisation and pay-per-click campaigns: A comparison of cost per acquisition
US20130124316A1 (en) Audience segment estimation
US20170364931A1 (en) Distributed model optimizer for content consumption
JP2012528394A (en) Measuring the effectiveness of online advertising campaigns
US20160132906A1 (en) Surge detector for content consumption
US20170132645A1 (en) On-line behavior research method using client/customer survey/respondent groups
CN110147517B (en) Third-party prediction method for activeness of news client
US20140257966A1 (en) Method , computer readable medium and system for determining weights for attributes and attribute values for a plurality of touchpoint encounters
US20170300959A9 (en) Method, computer readable medium and system for determining true scores for a plurality of touchpoint encounters
KR20220137943A (en) pattern-based classification
CN112101980A (en) Method and system for analyzing purchase preference of user
CN113610573A (en) Multi-dimensional user experience measurement method, device and storage medium
CN119760249A (en) SAAS platform and method based on mobile digital marketing
WO2017116493A1 (en) Surge detector for content consumption
CN113781146A (en) Recommendation method, device, equipment and storage medium of product information
Xiao et al. Click without compromise: Online advertising measurement via per user differential privacy
CN118690083B (en) New media operation content recommendation system based on big data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant