CN110609856B - 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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- CN110609856B CN110609856B CN201910778266.1A CN201910778266A CN110609856B CN 110609856 B CN110609856 B CN 110609856B CN 201910778266 A CN201910778266 A CN 201910778266A CN 110609856 B CN110609856 B CN 110609856B
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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) The terminal user requests a recommendation list from the AI artificial intelligent recommendation system, the back-end system returns the recommendation list and AB distribution strategy information to the front end, the AB distribution strategy information is matched with the recommendation list, and the AB distribution strategy information is a certain number of AB users which are randomly scheduled; (2) The first front end application responds and renders and presents, then the AB user implements, and finally the AB user behavior data is reported to the acquisition system through the data burial point; (3) The acquisition system provides the user behavior data to the data analysis module, and forms unified report data through data cleaning and analysis; (4) The data reporting system reads and presents reporting data and provides multi-dimensional reporting statistics meeting the product requirements; (5) The operator makes a decision to perform full network online by a relatively better algorithm according to report statistics; the method realizes an online rapid decision-making and optimizing algorithm.
Description
Technical Field
The invention relates to the field of business algorithm online, in particular to a method for recommending AB report statistics based on artificial intelligence.
Background
With the development of AI artificial intelligence, AI artificial intelligence recommendation is introduced into related software products in a large quantity successively, and with the brought problems that the statistics of the AB shunt report form according to the traditional mode becomes more and more troublesome, a business algorithm is rapidly on line, and each time a new algorithm is on line, report form statistics and transverse comparison of the algorithms are required to be carried out on the algorithm, so that the maintenance cost of the product is higher and higher, and the method can effectively relieve the problem under the condition.
Disclosure of Invention
Therefore, the invention aims to provide a method for recommending AB report statistics based on artificial intelligence, which can rapidly decide a better algorithm on line and save the maintenance cost of products.
The invention aims at realizing the following technical scheme:
a method for recommending AB report statistics based on artificial intelligence comprises the following steps:
(1) The terminal user requests a recommendation list from the AI artificial intelligent recommendation system, the back-end system returns the recommendation list and AB distribution strategy information to the front end, the AB distribution strategy information is matched with the recommendation list, and the AB distribution strategy information is a certain number of AB users which are randomly scheduled;
(2) The first front end application responds and renders and presents, then the AB user implements, and finally the AB user behavior data is reported to the acquisition system through the data burial point;
(3) The acquisition system provides the user behavior data to the data analysis module, and forms unified report data through data cleaning and analysis;
(4) The data reporting system reads and presents reporting data and provides multi-dimensional reporting statistics meeting the product requirements;
(5) And the operator decides a relatively better algorithm to perform full network online according to report statistics.
Further, the multi-dimensions include exposure, clicking, ordering, and playing.
The beneficial effects of the invention are as follows:
the method for recommending AB report statistics based on artificial intelligence can rapidly decide a better algorithm on line, can save product maintenance cost, and can support a plurality of sets of AB algorithms to perform on-line verification at the same time.
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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For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further 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 presented by way of illustration only and not by way of limitation.
AB in the present invention is a term, 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) The terminal user requests a recommendation list from an AI artificial intelligent recommendation system, wherein the recommendation list is an algorithm list, the AI artificial intelligent recommendation system is in the prior art, a back-end system returns the recommendation list and AB shunt strategy information to the front end, the back-end system is a server, the AB shunt strategy information is matched with the recommendation list, and the AB shunt strategy information is a certain number of AB users which are randomly scheduled;
(2) The first front end responds and renders and presents, the front end is a webpage end, then the AB user implements the application, and finally the AB user behavior data is reported to the acquisition system through the data burial point;
(3) The acquisition system provides user behavior data for the data analysis module, and forms unified report data through data cleaning and analysis, wherein the acquisition system and the data analysis module are in the prior art;
(4) The data reporting system reads and presents reporting data and provides multi-dimensional reporting statistics meeting the product requirements;
(5) And the operator decides a relatively better algorithm to perform full network online according to report statistics.
The multi-dimensions include exposure, clicking, ordering, and playing.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (1)
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) The terminal user requests a recommendation list from the AI artificial intelligent recommendation system, the back-end system returns the recommendation list and AB distribution strategy information to the front end, the AB distribution strategy information is matched with the recommendation list, the AB distribution strategy information is a certain number of AB users which are randomly preset, the recommendation list is an algorithm list, and the front end is a webpage end;
(2) The first front end application responds and renders and presents, then the AB user implements, and finally the AB user behavior data is reported to the acquisition system through the data burial point;
(3) The acquisition system provides the user behavior data to the data analysis module, and forms unified report data through data cleaning and analysis;
(4) The data reporting system reads and presents reporting data and provides multi-dimensional reporting statistics meeting the product requirements;
(5) An operator decides an algorithm to perform full network online according to report statistics;
the multi-dimensions include exposure, clicking, ordering, and playing.
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| 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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| CN201910778266.1A CN110609856B (en) | 2019-08-22 | 2019-08-22 | Method for recommending AB report statistics based on artificial intelligence |
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| CN110609856A CN110609856A (en) | 2019-12-24 |
| CN110609856B true CN110609856B (en) | 2023-06-13 |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| US20180322796A1 (en) * | 2017-05-03 | 2018-11-08 | Coursera, Inc. | A/b testing for massive open online courses |
| CN107424043B (en) * | 2017-06-15 | 2021-06-25 | 北京三快在线科技有限公司 | Product recommendation method and device and electronic equipment |
| CN109101425B (en) * | 2018-08-14 | 2021-12-07 | 创新先进技术有限公司 | Index point burying method and device for dynamic page AB test |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
| 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 |
Non-Patent Citations (1)
| Title |
|---|
| 基于IPTV用户行为数据的个性化推荐系统的设计与实现;樊宇;《广播电视信息》;全文 * |
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