CN116166716A - Data pushing method and device - Google Patents
Data pushing method and device Download PDFInfo
- Publication number
- CN116166716A CN116166716A CN202310180701.7A CN202310180701A CN116166716A CN 116166716 A CN116166716 A CN 116166716A CN 202310180701 A CN202310180701 A CN 202310180701A CN 116166716 A CN116166716 A CN 116166716A
- Authority
- CN
- China
- Prior art keywords
- data
- user
- tag
- target
- target user
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012360 testing method Methods 0.000 claims abstract description 29
- 230000006399 behavior Effects 0.000 claims description 52
- 238000002372 labelling Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 description 5
- 238000013461 design Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000001914 filtration Methods 0.000 description 4
- 238000007418 data mining Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000611 regression analysis Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- 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
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- 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
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a data pushing method and a device, which relate to the field of data pushing, and the method comprises the following steps: acquiring test data of equipment to be pushed, and setting a data tag for the test data of the equipment to be pushed to obtain the data to be pushed after the mark is placed; acquiring a data operation log of a target user, and determining historical operation behavior information of the target user on various labels based on the data operation log; setting a user tag for the target user according to the operation behavior information; and determining target pushing data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the marking. And setting labels with association relations between the data to be pushed and the target user, and accurately pushing the data to the target user according to the association relations of the labels.
Description
Technical Field
The present invention relates to the field of data processing and pushing, and in particular, to a data pushing method and apparatus.
Background
At present, the world new military revolution rapidly develops, the military high and new technology with information technology as a core is daily and monthly, the remote accurate, intelligent, stealth and unmanned trends of weaponry are more obvious, and the war morphology is accelerated to evolve into informatization intelligent war. The method is suitable for equipment test identification, new changes are also generated, new characteristics are presented, data generated by various equipment tests are exponentially increased, the test data has the characteristics of large data volume, multiple sources, multiple types, complex relationship, complex structure and the like, and the application of massive complex test data becomes a current problem to be solved urgently.
With the rapid increase of the amount of equipment test data, it is directly difficult for users to query and acquire the real needed equipment test data in time and quickly, and the problem is increasingly remarkable with the increase of the data.
The invention provides a data pushing method and device aiming at the problems.
Disclosure of Invention
The invention aims to provide a data pushing method and device, which are used for constructing data theme classification, analyzing user history operation records, establishing association between the data theme classification and the user history operation records and pushing data based on association relation, so that required data can be accurately pushed to a target user.
In order to achieve the above object, the present invention provides the following solutions:
a data pushing method, the method comprising:
acquiring test data of equipment to be pushed, and setting a data tag for the test data of the equipment to be pushed to obtain the data to be pushed after the mark is placed;
acquiring a data operation log of a target user, and determining historical operation behavior information of the target user on various labels based on the data operation log; setting a user tag for the target user according to the operation behavior information; the user tag comprises data tag information of various test data in the operation behavior information;
and determining target pushing data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the marking.
Optionally, determining historical operation behavior information of the target user on various labels based on the data operation log; setting a user tag for the target user according to the operation behavior information, specifically including:
determining operation behavior information of the target user on historical operation data of various labels according to the data operation log;
determining comprehensive index values of the historical operation data of the target user on various labels according to the operation behavior information;
a K neighbor algorithm is applied according to each comprehensive index value to determine neighbor users of the target users;
determining the user label of the target user according to the user label of the neighbor user; the user tag comprises data tag information and comprehensive index values corresponding to each type of data tag.
Optionally, determining, according to the operation behavior information, a comprehensive index value of the historical operation data of the target user on various labels, specifically including:
the historical operation data of each type of tag determines weight values of various types of operation behaviors of the target user according to the operation behavior information;
and calculating the comprehensive index value corresponding to the historical operation data of each type of tag according to the weight value and each type of operation behavior.
Optionally, determining the neighbor user of the target user by applying a K-nearest neighbor algorithm according to each comprehensive index value specifically includes:
calculating a label Euclidean distance according to the comprehensive index values of the target users and the non-target users; and determining the neighbor users according to the Euclidean distance of the labels.
Optionally, determining the target push data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the tagging specifically includes:
and determining the target push data of the data to be pushed after the mark is placed according to each comprehensive index value in the user label of the target user.
Optionally, determining the target push data of the data to be pushed after the tagging according to each comprehensive index value in the user tag of the target user specifically includes:
comparing the sizes of the comprehensive index values in the user labels of the target users;
selecting the comprehensive index value larger than a preset value and marking the comprehensive index value as a target comprehensive index value;
and matching the data tag corresponding to the target comprehensive index value with the data tag of the data to be pushed after the marking, and determining the target pushing data.
Optionally, determining the target push data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the tagging specifically includes:
determining data characteristics corresponding to data tag information in a user tag of the target user, and comparing the data characteristics with the data characteristics in the data tag of the data to be pushed after the marking to obtain common data characteristics;
and pushing the common data characteristics in the data to be pushed after the marking to the target user.
Optionally, determining the target push data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the tagging specifically includes:
determining similar users of the target user according to the user labels of the target user;
and pushing difference data which is different from the historical operation data of the target user in the historical operation data of the similar user to the target user.
The invention also provides a data pushing device, which comprises:
the data marking module is used for acquiring the test data of the equipment to be pushed and setting a data tag for the test data of the equipment to be pushed to obtain marked data to be pushed;
the user marking module is used for acquiring a data operation log of a target user and determining historical operation behavior information of the target user on various labels based on the data operation log; setting a user tag for the target user according to the operation behavior information;
and the data pushing module is used for determining target pushing data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the marking.
Optionally, the user markup module specifically includes:
an operation behavior information acquisition unit, configured to determine operation behavior information of historical operation data of the target user on various tags according to the data operation log;
the comprehensive index value determining unit is used for determining the comprehensive index value of the historical operation data of the target user on various labels according to the operation behavior information;
a neighbor user determining unit, configured to determine a neighbor user of the target user by applying a K-neighbor algorithm according to each of the comprehensive index values;
the user labeling unit is used for determining the user label of the target user according to the user label of the adjacent user; the user tag comprises data tag information and comprehensive index values corresponding to each type of data tag.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a data pushing method and a device, wherein the method comprises the following steps: firstly, setting data tags for test data of equipment to be pushed, and determining historical operation behavior information of the target user on various tags based on the data operation log; setting a user tag for the target user according to the operation behavior information, wherein the operation behavior information comprises operation behaviors of each type of data, so that the user tag information comprises data tag information; and the data labels and the user labels have an association relationship, so that data is accurately pushed to the target user according to the association relationship of the labels.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of a data pushing method provided in embodiment 1 of the present invention;
fig. 2 is a flowchart of a data pushing method provided in embodiment 1 of the present invention;
FIG. 3 is a schematic diagram of a data tag system according to embodiment 1 of the present invention;
FIG. 4 is a schematic diagram of a user tag system according to embodiment 1 of the present invention;
fig. 5 is a schematic diagram of application of the K-nearest neighbor algorithm provided in embodiment 1 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Technical concepts such as data association mining based on big data are proposed in some fields, however, the big data technology is difficult to solve the problems completely at present, that is, the accuracy of the data mining technology is low, and the inherent relationship of the data cannot be accurately mined and pushed. The root of the method is that the design and rule setting of the data mining algorithm of the system are not reasonable enough, and complex demands of users are difficult to capture, so that data really needed are difficult to push to the users.
The invention aims to provide a data pushing method and device, which are used for constructing data theme classification, analyzing user history operation records, establishing association between the data theme classification and the user history operation records and pushing data based on association relation, so that required data can be accurately pushed to a target user.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
First, the general idea of the recommendation method provided in this embodiment will be described with reference to fig. 1:
(1) and constructing a label system, and comprehensively covering the data characteristics and the user characteristics.
The label system constructed by the invention comprises test data labels and user labels, and the characteristics of the data and the users are comprehensively described through various subjects and labels.
(2) Selecting a data tag according to actual service, and describing original data and historical data;
(3) the marked data is sent to a business system (or a data center) for storage management;
(4) the device acquires a user operation log from a service system (or a data center);
(5) analyzing a user log, and marking the user according to the history use record to form a user portrait;
(6) based on a data pushing algorithm, matching and associating the marked data with the user, and sending matching and associating information to a service system (or a data center);
(7) the data is pushed to a particular user by the business system (or data center) based on the matching information.
The following specifically describes a data pushing method provided in this embodiment with reference to fig. 2, where the method includes:
s1: obtaining test data of equipment to be pushed, setting a data tag for the test data of the equipment to be pushed, and obtaining the data to be pushed after the mark is placed. The specific information included in the data tag system is shown in fig. 3.
The equipment test data is mostly structured data, and data description information is usually contained in a data file, including business information, equipment information, professional information, source information and the like. Therefore, the marking of the equipment test data can be realized by two methods, namely automatic marking and manual marking through data analysis.
S2: acquiring a data operation log of a target user, and determining historical operation behavior information of the target user on various labels based on the data operation log; and setting a user tag for the target user according to the operation behavior information. The user tag comprises data tag information of various test data in the operation behavior information. Specific information of the user tag system is shown in fig. 4.
The K nearest neighbor algorithm based on the label comprehensive index is based on the recommendation of the similarity of different data by a user, and the algorithm establishes a user-label comprehensive index matrix table by adopting regression analysis, statistics and other methods, calculates and obtains the nearest K nearest neighbor, and takes the most similar label as the label of the user. As shown in fig. 5.
(1) Building user-label comprehensive index scoring matrix
TABLE 1 user's tag comprehensive index Table
In the table, m×n data are combined, wherein the columns represent the comprehensive indexes of the labels and are represented by n; user stands for User and is denoted by m. Dmn represents the overall index of the mth target user to the nth data tag. The larger the Dmn value, the higher the user's preference for this label. Conversely, the smaller the value of Dmn, the lower the preference level. Converted to a mathematical model, the matrix score is expressed as:
(2) obtaining neighbor tag distance of target user
Because there are n tag synthesis indexes, the k-neighborhood of the user tag is a distance problem in n-dimensional space, where the Euclidean distance formula can be expressed as:
wherein:
U x ,U y respectively representUser x and user y;
D xi comprehensive index representing user x to data label i, D yi And the same is true.
(3) Labeling users according to k neighbor labels
And selecting a proper k value, and automatically marking the user according to the label distance to form a user portrait.
In summary, step S2 specifically includes:
s21: and determining the operation behavior information of the historical operation data of the target user on various labels according to the data operation log.
S22: and determining comprehensive index values of the historical operation data of the target user on various labels according to the operation behavior information.
Specifically, step S22 includes:
s222: and the historical operation data of each type of tag is used for determining the weight value of each type of operation behavior of the target user according to the operation behavior information.
S223: and calculating the comprehensive index value corresponding to the historical operation data of each type of tag according to the weight value and each type of operation behavior.
S23: and determining the neighbor users of the target user by applying a K neighbor algorithm according to each comprehensive index value.
The step S23 specifically includes:
calculating a label Euclidean distance according to the comprehensive index values of the target users and the non-target users; and determining the neighbor users according to the Euclidean distance of the labels.
S24: determining the user label of the target user according to the user label of the neighbor user; the user tag comprises data tag information and comprehensive index values corresponding to each type of data tag.
S3: and determining target pushing data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the marking.
In the first mode, the larger value of the comprehensive index can be determined directly based on the magnitude of the comprehensive index value corresponding to the user label, the data label corresponding to the larger value is used as an index, and the target push data is determined from the data to be pushed.
Specifically, step S3 includes:
and determining the target push data of the data to be pushed after the mark is placed according to each comprehensive index value in the user label of the target user. The method specifically comprises the following steps:
(1) Comparing the sizes of the comprehensive index values in the user labels of the target users;
(2) Selecting the comprehensive index value larger than a preset value and marking the comprehensive index value as a target comprehensive index value;
(3) And matching the data tag corresponding to the target comprehensive index value with the data tag of the data to be pushed after the marking, and determining the target pushing data.
In the second approach, content recommendation is an extension and development of information filtering technology, which is a recommendation made on top of the content information of an item. By means of machine learning to identify and analyze the content, the characteristics of the content are classified according to certain standards, so that interesting characteristics of users are obtained, and further, the recommendation of expansibility is achieved.
Finding a data set of interest of the User 1;
finding concrete contents of the data set, such as model, specialty, field, place and the like;
listing commonalities in content, such as model J10, optical specialty, outfield, etc.;
and searching data meeting the conditions according to the common content, and pushing the data to a User1.
The specific recommendation steps for the content recommendation mode are as follows:
the step S3 specifically comprises the following steps:
(1) And determining data characteristics corresponding to the data label information in the user label of the target user, and comparing the data characteristics with the data characteristics in the data label of the data to be pushed after the label is placed, so as to obtain common data characteristics.
(2) And pushing the common data characteristics in the data to be pushed after the marking to the target user.
In a third way, collaborative filtering recommendation may be employed, where collaborative filtering recommendation is to recommend data of interest to a user using preferences of similar groups.
User1 likes data A and data C;
user2 likes data B;
user3 likes data A, data C and data D;
the behavior of User2 and User1, user3 here is quite different. And the data liked by User1 and User3 are similar.
So we deduce that User1 and User3 are similar users and that User3 likes data D, we recommend data D to User1.
Without any user information and data information here, we look at the similarity, i.e. whether you like the same.
The specific recommendation steps aiming at the collaborative filtering recommendation mode are as follows:
the step S3 specifically comprises the following steps:
(1) And determining similar users of the target user according to the user labels of the target user.
(2) And pushing difference data which is different from the historical operation data of the target user in the historical operation data of the similar user to the target user.
In a fourth way, an association rule recommendation way can be adopted, wherein an association rule is a concept in data mining, and the association between data is found by analyzing the data.
The relevance of the user in the daily business when using the data is found out from a large amount of data pushing information and user operation information;
for example, through a large number of user operation records, quality personnel can search for design requirement data and historical similar fault data while browsing operation fault data;
according to the association rule, the design data and the historical fault data are pushed simultaneously when the fault data are pushed to the user.
Fifth mode, mixed recommendation can be adopted
Because various recommendation methods have advantages and disadvantages, in practical use, a method of combining multiple recommendation modes is often used for mixed recommendation, namely, a recommendation prediction result is generated based on various methods respectively, and then the results are combined. The method has the advantage of combining different recommendation algorithms to make the result more accurate.
The present embodiment has the following effects:
the method has the advantages that workload is saved, data pushing efficiency is improved, meanwhile, the user and the data are marked, and more accurate data can be recommended to the target user by utilizing the association relation between the labels.
The searching and positioning of the mass data is a precondition of data application, but with the exponential increase of the data volume, valuable target data such as 'sea fishing needle' is searched in the mass data.
Example 2
The embodiment provides a data pushing device, which includes:
the data marking module is used for acquiring the test data of the equipment to be pushed, setting a data tag for the test data of the equipment to be pushed, and obtaining the data to be pushed after marking.
The user marking module is used for acquiring a data operation log of a target user and determining historical operation behavior information of the target user on various labels based on the data operation log; setting a user tag for the target user according to the operation behavior information; the user tag comprises data tag information of various test data in the operation behavior information.
The user mark setting module specifically comprises:
and the operation behavior information acquisition unit is used for determining the operation behavior information of the historical operation data of the target user on various tags according to the data operation log.
And the comprehensive index value determining unit is used for determining the comprehensive index value of the historical operation data of the target user on various labels according to the operation behavior information.
And the neighbor user determining unit is used for determining the neighbor user of the target user by applying a K neighbor algorithm according to each comprehensive index value.
The user labeling unit is used for determining the user label of the target user according to the user label of the adjacent user; the user tag comprises data tag information and comprehensive index values corresponding to each type of data tag.
And the data pushing module is used for determining target pushing data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the marking.
The device provided by the embodiment is matched with a service system (or a data center) for use, and on one hand, the data is sent to the service system (or the data center) for storage after being marked; on the other hand, a user operation log is obtained and analyzed from a service system (or a data center), and a user is marked to form a user image; and matching the marked data with the user through an algorithm, transmitting the matched information to a service system (or a data center), and pushing the data to the specific user by the service system (or the data center).
The device provided by the implementation has the following specific effects:
(1) Low technical threshold
The device provides a visual and guide type use interface, professional training is not needed, and a user can configure data association rules according to requirements to carry out intelligent customization pushing of data.
(2) Cost reduction
The device has simple structure and low development, use and maintenance cost, can expand user nodes as required, and has greatly reduced cost compared with the traditional data pushing device based on the data center.
For device interface design:
USB interfaces, 1;
RJ45 interfaces, 1;
for the device composition structure:
(1) Hardware environment
The hardware environment of the device has a processor and a storage device, wherein the processor can be used for processing user information data, and the storage device can provide a data storage space for the operation of the device.
(2) Software environment
The system software operating environment is as follows:
in this specification, each embodiment is mainly described in the specification as a difference from other embodiments, and the same similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (10)
1. A data pushing method, the method comprising:
acquiring test data of equipment to be pushed, and setting a data tag for the test data of the equipment to be pushed to obtain the data to be pushed after the mark is placed;
acquiring a data operation log of a target user, and determining historical operation behavior information of the target user on various labels based on the data operation log; setting a user tag for the target user according to the operation behavior information; the user tag comprises data tag information of various test data in the operation behavior information;
and determining target pushing data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the marking.
2. The method of claim 1, wherein the historical operational behavior information of the target user for each type of tag is determined based on the data operation log; setting a user tag for the target user according to the operation behavior information, specifically including:
determining operation behavior information of the target user on historical operation data of various labels according to the data operation log;
determining comprehensive index values of the historical operation data of the target user on various labels according to the operation behavior information;
a K neighbor algorithm is applied according to each comprehensive index value to determine neighbor users of the target users;
determining the user label of the target user according to the user label of the neighbor user; the user tag comprises data tag information and comprehensive index values corresponding to each type of data tag.
3. The method according to claim 2, wherein determining, according to the operation behavior information, a comprehensive index value of the historical operation data of the target user on each type of tag specifically includes:
the historical operation data of each type of tag determines weight values of various types of operation behaviors of the target user according to the operation behavior information;
and calculating the comprehensive index value corresponding to the historical operation data of each type of tag according to the weight value and each type of operation behavior.
4. The method according to claim 2, wherein determining the neighbor users of the target user by applying a K-nearest neighbor algorithm according to each of the comprehensive index values, specifically comprises:
calculating a label Euclidean distance according to the comprehensive index values of the target users and the non-target users; and determining the neighbor users according to the Euclidean distance of the labels.
5. The method according to claim 4, wherein determining the target push data to be pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the tagging specifically comprises:
and determining the target push data of the data to be pushed after the mark is placed according to each comprehensive index value in the user label of the target user.
6. The method according to claim 5, wherein determining the target push data of the data to be pushed after the tagging according to the comprehensive index values in the user tag of the target user specifically comprises:
comparing the sizes of the comprehensive index values in the user labels of the target users;
selecting the comprehensive index value larger than a preset value and marking the comprehensive index value as a target comprehensive index value;
and matching the data tag corresponding to the target comprehensive index value with the data tag of the data to be pushed after the marking, and determining the target pushing data.
7. The method according to claim 4, wherein determining the target push data to be pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the tagging specifically comprises:
determining data characteristics corresponding to data tag information in a user tag of the target user, and comparing the data characteristics with the data characteristics in the data tag of the data to be pushed after the marking to obtain common data characteristics;
and pushing the common data characteristics in the data to be pushed after the marking to the target user.
8. The method according to claim 4, wherein determining the target push data to be pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the tagging specifically comprises:
determining similar users of the target user according to the user labels of the target user;
and pushing difference data which is different from the historical operation data of the target user in the historical operation data of the similar user to the target user.
9. A data pushing device, the device comprising:
the data marking module is used for acquiring the test data of the equipment to be pushed and setting a data tag for the test data of the equipment to be pushed to obtain marked data to be pushed;
the user marking module is used for acquiring a data operation log of a target user and determining historical operation behavior information of the target user on various labels based on the data operation log; setting a user tag for the target user according to the operation behavior information; the user tag comprises data tag information of various test data in the operation behavior information;
and the data pushing module is used for determining target pushing data pushed to the target user according to the user tag of the target user and the data tag of the data to be pushed after the marking.
10. The device according to claim 9, wherein the user tagging module specifically comprises:
an operation behavior information acquisition unit, configured to determine operation behavior information of historical operation data of the target user on various tags according to the data operation log;
the comprehensive index value determining unit is used for determining the comprehensive index value of the historical operation data of the target user on various labels according to the operation behavior information;
a neighbor user determining unit, configured to determine a neighbor user of the target user by applying a K-neighbor algorithm according to each of the comprehensive index values;
the user labeling unit is used for determining the user label of the target user according to the user label of the adjacent user; the user tag comprises data tag information and comprehensive index values corresponding to each type of data tag.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310180701.7A CN116166716B (en) | 2023-02-16 | 2023-02-16 | Data pushing method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310180701.7A CN116166716B (en) | 2023-02-16 | 2023-02-16 | Data pushing method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116166716A true CN116166716A (en) | 2023-05-26 |
| CN116166716B CN116166716B (en) | 2023-10-24 |
Family
ID=86413027
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310180701.7A Active CN116166716B (en) | 2023-02-16 | 2023-02-16 | Data pushing method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116166716B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106997358A (en) * | 2016-01-22 | 2017-08-01 | 中移（杭州）信息技术有限公司 | Information recommendation method and device |
| CN109087178A (en) * | 2018-08-28 | 2018-12-25 | 清华大学 | Method of Commodity Recommendation and device |
| CN109543111A (en) * | 2018-11-28 | 2019-03-29 | 广州虎牙信息科技有限公司 | Recommendation information screening technique, device, storage medium and server |
| CN110852852A (en) * | 2019-11-15 | 2020-02-28 | 成都航天科工大数据研究院有限公司 | Industrial Internet product recommendation system and method |
| CN111444428A (en) * | 2020-03-27 | 2020-07-24 | 腾讯科技（深圳）有限公司 | Information recommendation method and device based on artificial intelligence, electronic equipment and storage medium |
| CN112541121A (en) * | 2020-12-21 | 2021-03-23 | 深圳市彬讯科技有限公司 | Page data pushing method and device, computer equipment and storage medium |
| CN112579893A (en) * | 2020-12-15 | 2021-03-30 | 中国建设银行股份有限公司 | Information pushing method, information display method, information pushing device, information display device and information display equipment |
-
2023
- 2023-02-16 CN CN202310180701.7A patent/CN116166716B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106997358A (en) * | 2016-01-22 | 2017-08-01 | 中移（杭州）信息技术有限公司 | Information recommendation method and device |
| CN109087178A (en) * | 2018-08-28 | 2018-12-25 | 清华大学 | Method of Commodity Recommendation and device |
| CN109543111A (en) * | 2018-11-28 | 2019-03-29 | 广州虎牙信息科技有限公司 | Recommendation information screening technique, device, storage medium and server |
| CN110852852A (en) * | 2019-11-15 | 2020-02-28 | 成都航天科工大数据研究院有限公司 | Industrial Internet product recommendation system and method |
| CN111444428A (en) * | 2020-03-27 | 2020-07-24 | 腾讯科技（深圳）有限公司 | Information recommendation method and device based on artificial intelligence, electronic equipment and storage medium |
| CN112579893A (en) * | 2020-12-15 | 2021-03-30 | 中国建设银行股份有限公司 | Information pushing method, information display method, information pushing device, information display device and information display equipment |
| CN112541121A (en) * | 2020-12-21 | 2021-03-23 | 深圳市彬讯科技有限公司 | Page data pushing method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116166716B (en) | 2023-10-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111984689B (en) | Information retrieval method, device, equipment and storage medium | |
| CN109271539B (en) | Image automatic labeling method and device based on deep learning | |
| EP2192500B1 (en) | System and method for providing robust topic identification in social indexes | |
| CN110807085B (en) | Fault information query method and device, storage medium and electronic device | |
| CN105426550B (en) | Collaborative filtering label recommendation method and system based on user quality model | |
| CN111460252A (en) | Automatic search engine method and system based on network public opinion analysis | |
| CN116909991B (en) | NLP-based scientific research archive management method and system | |
| CN113407773A (en) | Short video intelligent recommendation method and system, electronic device and storage medium | |
| CN110543484A (en) | prompt word recommendation method and device, storage medium and processor | |
| CN112818111A (en) | Document recommendation method and device, electronic equipment and medium | |
| CN113139096B (en) | Video dataset labeling method and device | |
| CN104142955A (en) | Method and terminal for recommending learning courses | |
| CN114020960A (en) | Music recommendation method, device, server and storage medium | |
| Tallapragada et al. | Improved Resume Parsing based on Contextual Meaning Extraction using BERT | |
| CN112434173B (en) | Search content output method and device, computer equipment and readable storage medium | |
| CN116166716B (en) | Data pushing method and device | |
| CN111708942A (en) | Multimedia resource pushing method, device, server and storage medium | |
| CN116777692A (en) | Online learning method, device, equipment and storage medium based on data analysis | |
| CN115168637B (en) | Method, system and storage medium for adding label to picture | |
| CN106570116B (en) | Search result aggregation method and device based on artificial intelligence | |
| CN112052402B (en) | Information recommendation method and device, electronic equipment and storage medium | |
| CN117056392A (en) | Big data retrieval service system and method based on dynamic hypergraph technology | |
| CN114329181A (en) | Question recommendation method and device and electronic equipment | |
| CN113220843A (en) | Method, device, storage medium and equipment for determining information association relation | |
| CN113204697A (en) | Searching method, searching device, electronic equipment and storage medium |
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 | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Qin Jiashen Inventor after: Hong Xuechao Inventor after: Shi Fujie Inventor after: Yan Ruiqi Inventor before: Qin Jiashen Inventor before: Yan Ruiqi Inventor before: Shi Fujie Inventor before: Hong Xuechao |