CN116091112A - Consumer portrait generating device and portrait analyzing method - Google Patents
Consumer portrait generating device and portrait analyzing method Download PDFInfo
- Publication number
- CN116091112A CN116091112A CN202211716219.2A CN202211716219A CN116091112A CN 116091112 A CN116091112 A CN 116091112A CN 202211716219 A CN202211716219 A CN 202211716219A CN 116091112 A CN116091112 A CN 116091112A
- Authority
- CN
- China
- Prior art keywords
- consumer
- data
- portrait
- unit
- label
- 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.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06Q30/0202—Market predictions or forecasting for commercial activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention relates to the technical field of data processing, in particular to a consumer portrait generating device and a portrait analyzing method, which comprises a data acquisition module, a label generating module and a portrait generating module, wherein the data acquisition module is used for acquiring consumer consumption data and preprocessing the consumer consumption data to obtain effective data; the label generation module is used for classifying the consumer products to obtain classified labels; the portrait generation module comprises a matching unit, a grading unit and a portrait generation unit, wherein the matching unit is used for matching the effective data based on the classified labels to obtain a label group; a grading unit for grading the labels of the plurality of label groups according to the keyword setting range; and the image generation unit is used for generating the individual images within the set range of the keywords by combining based on the classified labels, so that the individual images within the set range can be obtained through the image generation unit, the keywords can be overlapped for multiple times to generate the individual images, and the consumer image generation is more accurate.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a consumer portrait generation device and a portrait analysis method.
Background
User portrayal, also known as user role, is widely used in various fields as an effective tool for outlining target users, contacting user appeal and design direction. We often combine the user's attributes, behaviors, and expected data transformations in the most superficial and life-oriented utterances during the course of actual operation. As a virtual representation of an actual user, the user image forms a user character that is not built outside of the product and market, and the formed user character needs to be representative to represent the primary audience and target group of the product.
The conventional image generation method generates an inaccurate image, thereby reducing the practicality.
Disclosure of Invention
The invention provides a consumer portrait generating device and a portrait analyzing method, which aims to generate a user portrait better and improve the practicability.
In order to achieve the above object, according to a first aspect of the present invention, there is provided a consumer portrait creation device, including a data acquisition module, a tag creation module, and a portrait creation module, where the data acquisition module, the tag creation module, and the portrait creation module are sequentially connected;
the data acquisition module is used for acquiring consumption data of a consumer and preprocessing the consumption data to obtain effective data;
the label generation module is used for classifying the consumer products to obtain classified labels;
the portrait generation module comprises a matching unit, a grading unit and a portrait generation unit, wherein the matching unit is used for matching the effective data based on the classification labels to obtain a label group; the grading unit is used for grading the labels of the label groups according to the keyword setting range; the portrait generation unit is used for generating individual portraits within a keyword setting range based on the combination of the classified labels.
Wherein the keywords include region, age, amount consumed, and gender.
The consumer portrait generation device further comprises a consumption prediction module, wherein the consumption prediction module is used for predicting consumption behaviors based on the generated user images.
The consumer portrait generation device further comprises a calibration module, wherein the calibration module is used for calibrating the consumer portrait based on actual consumption information.
The image generation unit comprises a tag group generation subunit, a range grading unit, a general image generation subunit and an individual image generation subunit, wherein the tag group generation subunit, the range grading unit, the general image generation subunit and the individual image generation subunit are sequentially connected;
the label group generating subunit is used for matching the consumption data based on the classified labels to obtain a label group;
the range grading unit is used for grading the labels of the label groups according to the set range of the keywords;
the general portrait generation subunit is used for generating a user portrait of a keyword setting range based on the combination of the classified labels;
the individual portrait generation subunit is used for generating individual portraits based on the user portraits, the consumption frequency and the consumption amount under each label.
The data acquisition module comprises an online data acquisition unit, an offline data acquisition unit and a data processing unit, wherein the online data acquisition unit, the offline data acquisition unit and the data processing unit are sequentially connected, and the online data acquisition unit is used for acquiring online mall consumption data; the off-line data acquisition unit is used for acquiring the super-business consumption data, and the data processing unit is used for processing and integrating the on-line mall consumption data and the super-business consumption data to obtain effective data.
In a second aspect, the present invention also provides a consumer portrait analysis method, including: the method comprises the steps of obtaining consumption data of a consumer and preprocessing the consumption data to obtain effective data;
classifying the consumer products to obtain classification labels;
matching the effective data based on the classified labels to obtain a label group;
classifying the labels of the plurality of label groups according to the keyword setting range;
and combining the labels based on the grades to generate an individual portrait within the set range of the keywords.
According to the consumer portrait generating device and the portrait analyzing method, the data acquisition module can acquire consumer consumption data, an online or offline acquisition mode can be adopted, so that consumer consumption data can be collected, some of the collected consumption data are invalid data, processing and deleting are needed, for example, the time is long, the data are incomplete, screening can be conducted through a data comparison mode, effective data can be obtained, consumer products are classified through the label generating module, the consumer products are mainly classified according to purposes, for example, home decoration, food, mother and infant, food, enterprises, digital, department stores, books, clothes and the like, labels of the consumer products are generated, and corresponding classification labels can be automatically generated according to products sold in a market through the label generating module. Then the portrait generation module carries out matching based on a certain piece of effective data to obtain a tag group of the effective data, the whole effective data is traversed in the mode to obtain all tag groups, then a range is set through keywords, the keywords comprise regions, ages, consumption amounts and sexes, the keywords are classified according to preset rules within a range limited by the keywords, such as the occurrence frequency, the weight ratio is set through the frequency, and therefore individual portrait in the set range can be obtained through the portrait generation unit, and the keywords can be overlapped for multiple times to generate the individual portrait, so that the consumer portrait generation is more accurate.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the 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 block diagram of a consumer image generating device according to a first embodiment of the present invention.
Fig. 2 is a block diagram of an image generating module according to a first embodiment of the present invention.
Fig. 3 is a block diagram of an image generating unit according to a first embodiment of the present invention.
Fig. 4 is a block diagram of a data acquisition module according to a first embodiment of the present invention.
Fig. 5 is a block diagram of a consumer image generating apparatus according to a second embodiment of the present invention.
FIG. 6 is a flow chart of a consumer representation analysis method according to a third embodiment of the present invention.
101-data acquisition module, 102-tag generation module, 103-image generation module, 104-matching unit, 105-ranking unit, 106-image generation unit, 107-tag group generation subunit, 108-range ranking unit, 109-general image generation subunit, 110-individual image generation subunit, 111-on-line data acquisition unit, 112-off-line data acquisition unit, 113-data processing unit, 201-consumption prediction module, 202-calibration module.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
First embodiment
Referring to fig. 1 to 4, fig. 1 is a block diagram of a consumer image generating apparatus according to a first embodiment of the present invention. Fig. 2 is a block diagram of an image generating module according to a first embodiment of the present invention. Fig. 3 is a block diagram of an image generating unit according to a first embodiment of the present invention. Fig. 4 is a block diagram of a data acquisition module according to a first embodiment of the present invention. The invention provides a consumer portrait generating device:
the system comprises a data acquisition module 101, a label generation module 102 and a portrait generation module 103, wherein the data acquisition module 101, the label generation module 102 and the portrait generation module 103 are sequentially connected;
the data acquisition module 101 is configured to acquire consumption data of a consumer and perform preprocessing to obtain valid data;
the label generating module 102 is configured to classify consumer products to obtain classified labels;
the portrait generation module 103 comprises a matching unit 104, a grading unit 105 and a portrait generation unit 106, wherein the matching unit 104 is used for matching the effective data based on the classified labels to obtain a label group; the grading unit 105 is configured to grade the labels of the plurality of label groups according to the keyword setting range; the image generation unit 106 is used for generating individual images within a keyword setting range based on the classified labels.
In this embodiment, the data acquisition module 101 may acquire consumer consumption data, and may acquire consumer consumption data online or offline, so that consumer consumption data may be collected, some of the collected consumption data is invalid data, and needs to be processed and deleted, for example, long time, incomplete data, and may be screened out by means of data comparison, so that valid data may be obtained, and then consumer products are classified by the label generation module 102, where consumer products are mainly classified according to purposes, such as home decoration, food, mother and infant, food, enterprise, digital, department, book, clothing, etc., so as to generate labels of the consumer products, and products sold according to the needs of a mall may be input into the product by the label generation module 102 to generate corresponding classification labels. Then the portrait generation module 103 performs matching based on a certain piece of effective data to obtain a tag group of the effective data, traverses the whole effective data in this way to obtain all tag groups, and then sets a range through keywords, wherein the keywords comprise regions, ages, consumption amounts and sexes, the keywords are classified according to preset rules within a range defined by the keywords, such as occurrence frequency, and a weight ratio is set through the frequency, so that individual portrait in the set range can be obtained through the portrait generation unit 106, and individual portrait generation can be performed by overlapping the keywords for multiple times, so that consumer portrait generation is more accurate.
Wherein the image generation unit 106 includes a tag group generation subunit 107, a range hierarchy unit 108, a general image generation subunit 109, and an individual image generation subunit 110, and the tag group generation subunit 107, the range hierarchy unit 108, the general image generation subunit 109, and the individual image generation subunit 110 are sequentially connected;
the tag group generating subunit 107 is configured to match the consumption data based on the classification tag, to obtain a tag group; the specific mode is that consumption data of a preset user are firstly obtained, then, based on classification labels and products in the consumption data, matching is carried out, and a label group of the preset user is generated, so that the combination of labels of home decoration, food, mother and infant, food and enterprises can be included under the data of a certain user.
The range grading unit 108 is configured to grade the labels of the plurality of label groups according to the keyword setting ranges; the method comprises the steps of obtaining tag group data of all users, calculating the occurrence frequency of each tag in the tag group data according to a range limited by keywords, obtaining grading tags based on screening keywords, for example, the tag group data only comprises home decoration, food, mother and infant, food and enterprises, filtering the data through the keywords, grading the rest data, and referring to the occurrence frequency of each tag.
The general portrait generation subunit 109 is configured to generate a user portrait within a keyword setting range by combining the hierarchical labels; after the classified labels are recombined, the user portrait within the limited range of the keywords can be generated in a mode of calculating the weight ratio, and a plurality of keywords can be overlapped to enable the user portrait to be more accurate.
The individual portrait generation subunit 110 is configured to generate an individual portrait based on the user portrait, the consumption frequency and the consumption amount under each label. In addition, in order to make the prediction more accurate, the consumption frequency and the consumption amount under each label can be further obtained for each user based on the consumption data of the user, and then the individual user portraits are further generated in the specific mode that: generating a first image based on the consumption frequency duty ratio under each label class; a second representation is generated based on the consumption amount duty ratio under each tag class.
The data acquisition module 101 includes an online data acquisition unit 111, an offline data acquisition unit 112, and a data processing unit 113, where the online data acquisition unit 111, the offline data acquisition unit 112, and the data processing unit 113 are sequentially connected, and the online data acquisition unit 111 is configured to acquire online mall consumption data; the off-line data acquisition unit 112 is configured to acquire super-consumer data of a business, and the data processing unit 113 is configured to process and integrate the super-consumer data of the online mall and the super-consumer data of the business to obtain effective data.
Second embodiment
Referring to fig. 5, fig. 5 is a block diagram of a consumer image generating apparatus according to a second embodiment of the present invention. The invention also provides a consumer representation generating device, which further comprises a consumption prediction module 201, wherein the consumption prediction module 201 is used for predicting the consumption behavior based on the generated user image.
The consumption prediction module 201 includes a user behavior acquisition unit and a matching unit 104, where the user behavior acquisition unit is configured to acquire click information and search information of a user, and the matching unit 104 is configured to, based on the click information and the search information, match a user image or an individual image, and push a product corresponding to a corresponding label.
The consumer representation generation apparatus further comprises a calibration module 202, the calibration module 202 being adapted to calibrate the consumer representation based on the actual consumption information. The duty ratio of the weight of all the labels of the user can be adjusted in a preset time, and the specific mode is to combine the consumption information in a certain time with the past label information of the user to conduct comparison, if no label appears, the duty ratio is adjusted downwards, and if the label appears, the corresponding duty ratio is adjusted upwards to dynamically adjust the individual portrait, so that the accuracy is higher.
Third embodiment
Referring to fig. 6, fig. 6 is a flowchart of a consumer image analysis method according to a third embodiment of the present invention. The invention also provides a consumer portrait analysis method, which comprises the following steps:
s101, acquiring consumption data of a consumer and preprocessing the consumption data to obtain effective data;
the data acquisition module 101 can acquire consumer consumption data, and can acquire the consumer consumption data in an online or offline mode, so that consumer consumption data can be collected, and part of the collected consumption data is invalid data and needs to be processed and deleted, such as long-time, incomplete data, and can be screened out by a data comparison mode, so that valid data can be obtained
S102, classifying consumer products to obtain classification labels;
the label generating module 102 classifies consumer products, such as home decoration, food, mother and infant, food, enterprise, digital, department store, book, clothing, etc., according to the purpose, so as to generate labels of the consumer products, and the products to be sold according to the need of the mall can be input into the product generating module 102 to generate corresponding classified labels
S103, matching the effective data based on the classification labels to obtain a label group;
the portrait generation module 103 performs matching based on a certain piece of effective data to obtain a tag group of the piece of effective data, and traverses the whole effective data in this way to obtain all tag groups.
S104, classifying the labels of the plurality of label groups according to the keyword setting range;
grading within the limits of the keywords according to preset rules, such as frequency of occurrence
S105, combining and generating individual portraits within a keyword setting range based on the classified labels.
By setting the weight ratio frequently, the individual images within the set range can be obtained by the image generation unit 106, and the keywords can be overlapped for multiple times to generate the individual images, so that the consumer image generation is more accurate.
The above disclosure is only a preferred embodiment of the present invention, and it should be understood that the scope of the invention is not limited thereto, and those skilled in the art will appreciate that all or part of the procedures described above can be performed according to the equivalent changes of the claims, and still fall within the scope of the present invention.
Claims (7)
1. A consumer portrait generating device is characterized in that,
the system comprises a data acquisition module, a label generation module and a portrait generation module, wherein the data acquisition module, the label generation module and the portrait generation module are sequentially connected;
the data acquisition module is used for acquiring consumption data of a consumer and preprocessing the consumption data to obtain effective data;
the label generation module is used for classifying the consumer products to obtain classified labels;
the portrait generation module comprises a matching unit, a grading unit and a portrait generation unit, wherein the matching unit is used for matching the effective data based on the classification labels to obtain a label group; the grading unit is used for grading the labels of the label groups according to the keyword setting range; the portrait generation unit is used for generating individual portraits within a keyword setting range based on the combination of the classified labels.
2. A consumer image generating apparatus according to claim 1, wherein,
the keywords include region, age, amount consumed, and gender.
3. A consumer image generating apparatus according to claim 2, wherein,
the consumer representation generation apparatus further includes a consumption prediction module for predicting consumption behavior based on the generated user image.
4. A consumer image generating apparatus according to claim 3, wherein,
the consumer representation generation device further comprises a calibration module for calibrating the consumer representation based on the actual consumption information.
5. A consumer image generating device according to claim 4, wherein,
the image generation unit comprises a tag group generation subunit, a range grading unit, a general image generation subunit and an individual image generation subunit, wherein the tag group generation subunit, the range grading unit, the general image generation subunit and the individual image generation subunit are sequentially connected;
the label group generating subunit is used for matching the consumption data based on the classified labels to obtain a label group;
the range grading unit is used for grading the labels of the label groups according to the set range of the keywords;
the general portrait generation subunit is used for generating a user portrait of a keyword setting range based on the combination of the classified labels;
the individual portrait generation subunit is used for generating individual portraits based on the user portraits, the consumption frequency and the consumption amount under each label.
6. A consumer image generating apparatus according to claim 5, wherein,
the data acquisition module comprises an online data acquisition unit, an offline data acquisition unit and a data processing unit, wherein the online data acquisition unit, the offline data acquisition unit and the data processing unit are sequentially connected, and the online data acquisition unit is used for acquiring online mall consumption data; the off-line data acquisition unit is used for acquiring the super-business consumption data, and the data processing unit is used for processing and integrating the on-line mall consumption data and the super-business consumption data to obtain effective data.
7. A consumer image analysis method using the consumer image generating device according to claim 1, characterized in that,
comprising the following steps: obtaining consumption data of a consumer and preprocessing the consumption data to obtain effective data;
classifying the consumer products to obtain classification labels;
matching the effective data based on the classified labels to obtain a label group;
classifying the labels of the plurality of label groups according to the keyword setting range;
and combining the labels based on the grades to generate an individual portrait within the set range of the keywords.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211716219.2A CN116091112A (en) | 2022-12-29 | 2022-12-29 | Consumer portrait generating device and portrait analyzing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211716219.2A CN116091112A (en) | 2022-12-29 | 2022-12-29 | Consumer portrait generating device and portrait analyzing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116091112A true CN116091112A (en) | 2023-05-09 |
Family
ID=86198558
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211716219.2A Pending CN116091112A (en) | 2022-12-29 | 2022-12-29 | Consumer portrait generating device and portrait analyzing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116091112A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105893406A (en) * | 2015-11-12 | 2016-08-24 | 乐视云计算有限公司 | Group user profiling method and system |
| CN107038237A (en) * | 2017-04-18 | 2017-08-11 | 昆山数泰数据技术有限公司 | User's portrait system and portrait method based on big data |
| CN109118283A (en) * | 2018-08-10 | 2019-01-01 | 云南数金科技有限公司 | Precision marketing service system based on big data |
| WO2019157928A1 (en) * | 2018-02-13 | 2019-08-22 | 阿里巴巴集团控股有限公司 | Method and apparatus for acquiring multi-tag user portrait |
| CN110442670A (en) * | 2019-06-11 | 2019-11-12 | 天津交通职业学院 | A kind of consumer representation generation method based on document indexing |
| CN113010795A (en) * | 2021-04-12 | 2021-06-22 | 北京明略软件***有限公司 | User dynamic portrait generation method, system, storage medium and electronic device |
| CN114219580A (en) * | 2021-12-31 | 2022-03-22 | 江苏玖益贰信息科技有限公司 | Consumer portrait generation device and portrait analysis method |
| CN115221267A (en) * | 2022-07-04 | 2022-10-21 | 江苏海事职业技术学院 | Consumer portrait generation method and device |
-
2022
- 2022-12-29 CN CN202211716219.2A patent/CN116091112A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105893406A (en) * | 2015-11-12 | 2016-08-24 | 乐视云计算有限公司 | Group user profiling method and system |
| CN107038237A (en) * | 2017-04-18 | 2017-08-11 | 昆山数泰数据技术有限公司 | User's portrait system and portrait method based on big data |
| WO2019157928A1 (en) * | 2018-02-13 | 2019-08-22 | 阿里巴巴集团控股有限公司 | Method and apparatus for acquiring multi-tag user portrait |
| CN109118283A (en) * | 2018-08-10 | 2019-01-01 | 云南数金科技有限公司 | Precision marketing service system based on big data |
| CN110442670A (en) * | 2019-06-11 | 2019-11-12 | 天津交通职业学院 | A kind of consumer representation generation method based on document indexing |
| CN113010795A (en) * | 2021-04-12 | 2021-06-22 | 北京明略软件***有限公司 | User dynamic portrait generation method, system, storage medium and electronic device |
| CN114219580A (en) * | 2021-12-31 | 2022-03-22 | 江苏玖益贰信息科技有限公司 | Consumer portrait generation device and portrait analysis method |
| CN115221267A (en) * | 2022-07-04 | 2022-10-21 | 江苏海事职业技术学院 | Consumer portrait generation method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220335501A1 (en) | Item recommendations using convolutions on weighted graphs | |
| CN107424043B (en) | Product recommendation method and device and electronic equipment | |
| WO2019157928A1 (en) | Method and apparatus for acquiring multi-tag user portrait | |
| US8738436B2 (en) | Click through rate prediction system and method | |
| Agarwal et al. | Statistical methods for recommender systems | |
| CN107516235B (en) | Commodity preference estimation method and device | |
| CN109165975B (en) | Label recommending method, device, computer equipment and storage medium | |
| CN116703485B (en) | Advertisement accurate marketing method and system based on big data | |
| CN110929138A (en) | Recommendation information generation method, device, equipment and storage medium | |
| JP2019125007A (en) | Information analyzer, information analysis method and information analysis program | |
| CN105868254A (en) | Information recommendation method and apparatus | |
| CN110990695A (en) | Recommendation system content recall method and device | |
| CN110119448B (en) | Semi-supervised cross-domain text classification method based on dual automatic encoders | |
| Maneewongvatana et al. | A recommendation model for personalized book lists | |
| Liow et al. | Jommachinelearning: bringing artwork nearer with designlab | |
| CN112800109A (en) | Information mining method and system | |
| CN111625726A (en) | User portrait processing method and device | |
| CN109146606B (en) | Brand recommendation method, electronic equipment, storage medium and system | |
| US20130332440A1 (en) | Refinements in Document Analysis | |
| CN113327132A (en) | Multimedia recommendation method, device, equipment and storage medium | |
| CN110188277B (en) | Resource recommendation method and device | |
| US11514481B2 (en) | Sales support marketing system | |
| JP5197491B2 (en) | Action recommendation device, method and program | |
| CN116091112A (en) | Consumer portrait generating device and portrait analyzing method | |
| Kaur et al. | A Review on Sentimental Analysis on Facebook Comments by using Data Mining Technique |
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 |