CN110609856A - Method for recommending AB report statistics based on artificial intelligence - Google Patents
Method for recommending AB report statistics based on artificial intelligence Download PDFInfo
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- CN110609856A CN110609856A CN201910778266.1A CN201910778266A CN110609856A CN 110609856 A CN110609856 A CN 110609856A CN 201910778266 A CN201910778266 A CN 201910778266A CN 110609856 A CN110609856 A CN 110609856A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention discloses a method for recommending AB report statistics based on artificial intelligence, which comprises the following steps: (1) a terminal user requests a recommendation list from an AI artificial intelligence recommendation system, a back-end system returns the recommendation list and AB shunting strategy information to a front end, the AB shunting strategy information is matched with the recommendation list, and the AB shunting strategy information is a certain number of AB users which are randomly preset; (2) firstly, applying response and rendering presentation by a front end, then implementing by an AB user, and finally reporting AB user behavior data to an acquisition system through a data buried point; (3) the acquisition system provides the user behavior data to the data analysis module, and uniform report data is formed through data cleaning and analysis; (4) the data report system reads and presents report data and provides multi-dimensional report statistics meeting product requirements; (5) the operator decides a relatively better algorithm to carry out the whole network online according to the report statistics; the method realizes an algorithm with online rapid decision making and better.
Description
Technical Field
The invention relates to the field of online business algorithms, in particular to a method for recommending AB report statistics based on artificial intelligence.
Background
Along with the development of AI artificial intelligence, AI artificial intelligence recommendation is introduced into related software products in a large quantity in succession, along with the brought problems that report statistics become more and more troublesome according to a traditional mode AB shunting, a business algorithm is quickly on-line, and report statistics and transverse comparison of each algorithm need to be carried out on the algorithm every time a new algorithm is on-line, so that the product maintenance cost is higher and higher.
Disclosure of Invention
In view of the above, the present invention provides a method for recommending AB report statistics based on artificial intelligence, which can quickly make a decision on a better algorithm on line, and can save product maintenance cost.
The purpose of the invention is realized by the following technical scheme:
a method for recommending AB report statistics based on artificial intelligence comprises the following steps:
(1) a terminal user requests a recommendation list from an AI artificial intelligence recommendation system, a back-end system returns the recommendation list and AB shunting strategy information to a front end, the AB shunting strategy information is matched with the recommendation list, and the AB shunting strategy information is a certain number of AB users which are randomly preset;
(2) firstly, applying response and rendering presentation by a front end, then implementing by an AB user, and finally reporting AB user behavior data to an acquisition system through a data buried point;
(3) the acquisition system provides the user behavior data to the data analysis module, and uniform report data is formed through data cleaning and analysis;
(4) the data report system reads and presents report data and provides multi-dimensional report statistics meeting product requirements;
(5) and the operator decides a relatively better algorithm to carry out online of the whole network according to the report statistics.
Further, the multiple dimensions include exposure, click, order, and play.
The invention has the beneficial effects that:
the method for recommending the AB report statistics based on the artificial intelligence can quickly decide a better algorithm on line, can save the product maintenance cost, and can support a plurality of sets of AB algorithms to perform on-line verification simultaneously.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof.
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In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of the present invention.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
The AB in the present invention is a synonym, and has no specific meaning.
As shown in fig. 1, a method for recommending AB report statistics based on artificial intelligence includes the following steps:
(1) a terminal user requests a recommendation list from an AI artificial intelligence recommendation system, wherein the recommendation list is an algorithm list, the AI artificial intelligence recommendation system is in the prior art, a back-end system returns the recommendation list and AB shunting strategy information to a front end, the back-end system is a server, the AB shunting strategy information is matched with the recommendation list, and the AB shunting strategy information is a certain number of randomly preset AB users;
(2) firstly, applying response and rendering presentation by a front end, wherein the front end is a webpage end, then implementing by an AB user, and finally reporting AB user behavior data to an acquisition system through a data burying point;
(3) the acquisition system provides the user behavior data to the data analysis module, and forms uniform report data through data cleaning and analysis, wherein the acquisition system and the data analysis module are in the prior art;
(4) the data report system reads and presents report data and provides multi-dimensional report statistics meeting product requirements;
(5) and the operator decides a relatively better algorithm to carry out online of the whole network according to the report statistics.
The multiple dimensions include exposure, click, order, and play.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (2)
1. A method for recommending AB report statistics based on artificial intelligence is characterized by comprising the following steps: the method comprises the following steps:
(1) a terminal user requests a recommendation list from an AI artificial intelligence recommendation system, a back-end system returns the recommendation list and AB shunting strategy information to a front end, the AB shunting strategy information is matched with the recommendation list, and the AB shunting strategy information is a certain number of AB users which are randomly preset;
(2) firstly, applying response and rendering presentation by a front end, then implementing by an AB user, and finally reporting AB user behavior data to an acquisition system through a data buried point;
(3) the acquisition system provides the user behavior data to the data analysis module, and uniform report data is formed through data cleaning and analysis;
(4) the data report system reads and presents report data and provides multi-dimensional report statistics meeting product requirements;
(5) and the operator decides a relatively better algorithm to carry out online of the whole network according to the report statistics.
2. The method for recommending AB report statistics based on artificial intelligence as claimed in claim 1, wherein: the multiple dimensions include exposure, click, order, and play.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910778266.1A CN110609856B (en) | 2019-08-22 | 2019-08-22 | Method for recommending AB report statistics based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201910778266.1A CN110609856B (en) | 2019-08-22 | 2019-08-22 | Method for recommending AB report statistics based on artificial intelligence |
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| Publication Number | Publication Date |
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| CN110609856A true CN110609856A (en) | 2019-12-24 |
| CN110609856B CN110609856B (en) | 2023-06-13 |
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| CN107424043A (en) * | 2017-06-15 | 2017-12-01 | 北京三快在线科技有限公司 | A kind of Products Show method and device, electronic equipment |
| US20180322796A1 (en) * | 2017-05-03 | 2018-11-08 | Coursera, Inc. | A/b testing for massive open online courses |
| CN109101425A (en) * | 2018-08-14 | 2018-12-28 | 阿里巴巴集团控股有限公司 | A kind of index of the page AB test of mobilism buries point methods and device |
| CN109587527A (en) * | 2018-11-09 | 2019-04-05 | 青岛聚看云科技有限公司 | A kind of method and device that individualized video is recommended |
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2019
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| US20140304106A1 (en) * | 2013-03-15 | 2014-10-09 | LogiPref, Inc. | Systems and methods for determining attribute-based user preferences and applying them to make recommendations |
| CN107067289A (en) * | 2016-10-28 | 2017-08-18 | 广东亿迅科技有限公司 | A kind of personal marketing commending system |
| US20180322796A1 (en) * | 2017-05-03 | 2018-11-08 | Coursera, Inc. | A/b testing for massive open online courses |
| CN107424043A (en) * | 2017-06-15 | 2017-12-01 | 北京三快在线科技有限公司 | A kind of Products Show method and device, electronic equipment |
| CN109101425A (en) * | 2018-08-14 | 2018-12-28 | 阿里巴巴集团控股有限公司 | A kind of index of the page AB test of mobilism buries point methods and device |
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| CN110609856B (en) | 2023-06-13 |
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