CN112131477A - Library book recommendation system and method based on user portrait - Google Patents
Library book recommendation system and method based on user portrait Download PDFInfo
- Publication number
- CN112131477A CN112131477A CN202011033243.7A CN202011033243A CN112131477A CN 112131477 A CN112131477 A CN 112131477A CN 202011033243 A CN202011033243 A CN 202011033243A CN 112131477 A CN112131477 A CN 112131477A
- Authority
- CN
- China
- Prior art keywords
- customer
- module
- wireless transmission
- transmission network
- book
- 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
- 238000000034 method Methods 0.000 title claims description 14
- 230000005540 biological transmission Effects 0.000 claims abstract description 20
- 230000001815 facial effect Effects 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims abstract description 8
- 238000012163 sequencing technique Methods 0.000 claims abstract description 5
- 238000012544 monitoring process Methods 0.000 claims abstract description 4
- 238000004422 calculation algorithm Methods 0.000 description 9
- 239000013598 vector Substances 0.000 description 8
- 239000010410 layer Substances 0.000 description 6
- 238000001514 detection method Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000005286 illumination Methods 0.000 description 4
- 238000005065 mining Methods 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 238000007781 pre-processing Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 238000012896 Statistical algorithm Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000003607 modifier Substances 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 239000002344 surface layer Substances 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
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9536—Search customisation based on social or collaborative filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Human Computer Interaction (AREA)
- Computing Systems (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Multimedia (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
A user representation-based library book recommendation system, comprising: a monitor installed at an entrance of a library for extracting an avatar of a customer; the facial recognition module is connected with the monitoring module and used for extracting facial features of the customer so as to judge whether the customer is an old customer; the book recommendation module is used for searching similar readers of the target reader by using social attributes and interest attributes in the user portrait of the customer through a similarity calculation method, finding out books which are interested by the similar readers, and sequencing and recommending the books in parallel; the wireless transmission network module is used for receiving the position information sent by the mobile phone of the customer through a wireless transmission network; the processor is connected with the book recommending module and the wireless transmission network module and is used for integrating the recommended books and the positions of the customers; and the LED display module is used for projecting the recommended book route fed back from the processor to the LED screen for display through the wireless transmission network module.
Description
Technical Field
The invention relates to a library book recommendation system and method based on user portraits.
Background
In the face of "information overload" on the internet, it is becoming increasingly difficult to rely solely on search engines to quickly find information that is needed by itself. Because many times the needs of people are often not well defined. The same problem is faced in the field of books, many readers do not know their needs and often have nothing to do with tens of thousands of books, so that personalized book recommendation systems are becoming mature. At present, different recommendation methods are designed according to various technical schemes to facilitate users, for example, personalized recommendation is performed on readers by constructing a latent semantic model to calculate interestingness; and book personalized recommendation based on the subscription record collaborative filtering recommendation method and the like.
User images were first proposed by father a. Cooper of interactive design, which can extract personal information of users comprehensively, is a model of target users established on real data, and is currently applied to a plurality of fields. Histogram of oriented gradients HOG is a feature descriptor used for object detection in computer vision and image processing. It constructs features by calculating and counting the histogram of gradient direction of local area of image. The HOG feature has been widely used in image recognition, and has been highly successful in pedestrian detection in particular.
The prior art is as follows: the patent with the application number of 201610154471.7 discloses a book recommendation system and a book recommendation method, and the technology comprises the steps of preprocessing borrowing records of users in a library database, constructing a user model according to a preprocessing result, carrying out fuzzy clustering processing on the user model, obtaining a user clustering center and membership degrees of the users in clustering, calculating similarity between a target user and the users according to the membership degrees, and obtaining a target user proximity set formed by the users with higher similarity with the target user to recommend books to the target user according to book borrowing conditions of the target user proximity set.
According to the technical scheme, the user model is built according to the borrowing records of the user, but not every book borrowed by the user can represent the preference of the user, and even the user can help other people to borrow the books, so that the user model is built only by using the borrowing records. And the user does not know the specific position of the recommended book, a large number of librarians are needed for guidance, and the labor cost is high.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide a library book recommendation system and method based on user portraits.
The invention provides a library book recommendation system based on user portrait, which comprises:
a monitor installed at an entrance of a library for extracting an avatar of a customer;
the facial recognition module is connected with the monitoring module and used for extracting facial features of the customer so as to judge whether the customer is an old customer;
the book recommendation module is used for searching similar readers of the target reader by using social attributes and interest attributes in the user portrait of the customer through a similarity calculation method, finding out books which are interested by the similar readers, and sequencing and recommending the books in parallel;
the wireless transmission network module is used for receiving the position information sent by the mobile phone of the customer through a wireless transmission network;
the processor is connected with the book recommending module and the wireless transmission network module and is used for integrating the recommended books and the positions of the customers;
and the LED display module is used for projecting the recommended book route fed back from the processor to the LED screen for display through the wireless transmission network module.
The invention also provides a library book recommendation method based on the user portrait, which comprises the following steps:
extracting the head portrait of the customer through a monitor installed at an entrance of the library;
extracting facial features of the customer through a facial recognition module, and judging whether the customer is an old customer;
through a book recommendation module, using social attributes and interest attributes in the user portrait of the customer, searching similar readers of the target reader through a similarity calculation method, finding out books which are interested by the similar readers, and sequencing and recommending in parallel;
receiving position information sent by a customer mobile phone through a wireless transmission network module;
integrating the recommended books and the positions of the customers through a processor;
and throwing the recommended book route fed back from the processor to an LED screen for display through a wireless transmission network module.
Drawings
FIG. 1 is a schematic diagram of a library book recommendation system based on user profiles.
FIG. 2 is a flow chart of user portrayal.
FIG. 3 is a flow diagram of an embodiment of a user representation.
Detailed Description
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
In the description that follows, reference is made to the terms "first \ second \ third, etc. or module a, module B, module C, etc. merely for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order, it being understood that specific orders or sequences may be interchanged if permitted to implement embodiments of the invention described herein in other than the order illustrated or described herein.
In the following description, reference to reference numerals indicating steps, such as S110, S120 … …, etc., does not necessarily indicate that the steps are performed in this order, and the order of the preceding and following steps may be interchanged or performed simultaneously, where permissible.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
As shown in FIG. 1, the present invention discloses a library book recommendation system based on user portraits, comprising: a monitor installed at an entrance of a library for extracting an avatar of a customer; the face recognition module is connected with the monitoring module, extracts the facial features of the customers by using an HOG feature extraction algorithm and is used for judging whether the customers are old customers or not; the book recommendation module is used for searching similar readers of the target reader by using social attributes and interest attributes in the user portrait of the customer by means of a similarity calculation method, finding out books in which the similar readers are interested, and selecting the first 5 recommended books for recommendation; the wireless transmission network module is used for receiving the position information sent by the mobile phone of the customer through a wireless transmission network; the processor is connected with the book recommending module and the wireless transmission network module and is used for integrating the recommended books and the positions of the customers; and the LED display module is used for throwing the recommended book route fed back from the processor to the LED screen through the wireless transmission network module, so that the customer can conveniently check the recommended book route.
User portrait: the user representation refers to a tagged user model abstracted according to information such as user attributes, user preferences, living habits, user behaviors and the like. Colloquially, a user is labeled, and the label is a highly refined characteristic mark obtained by analyzing user information. By tagging, a user may be described with some highly generalized, easily understandable features that may make it easier for a person to understand the user and may facilitate computer processing. The user portrait can be used for mining user interests, preferences and demographic characteristics, and the method mainly aims at improving marketing accuracy and recommending matching degree, and finally aims at improving product service and improving enterprise profits.
The core of the user portrait is the establishment of the label, and the models and algorithms used in each stage of the user portrait label establishment are as follows.
An original data layer. For original data, an algorithm of text mining is mainly used for analyzing algorithms such as common TF-IDF, LDA and the like, and the algorithms are mainly used for preprocessing and cleaning the original data and matching and identifying user data. The TF-IDF algorithm TF is used herein to mean Term frequency (Term-frequency), and IDF means inverse Document frequency (inverse Document frequency). TF-IDF is a conventional statistical algorithm for evaluating the importance of a word in a Document set to a certain Document. It is proportional to the word frequency of this word in the current document and inversely proportional to the other word frequencies in the document set.
First, a method for calculating the TF (word frequency) which refers to the word frequency of the current document,
in this formula, the numerator represents the number of occurrences of a modifier in a document, and the denominator represents the sum of the number of occurrences of all keywords in the document.
Then we say the calculation method of IDF (inverse word frequency), which refers to a measure of the popularity of a certain vocabulary.
A fact label layer. Through a text mining method, factual data information such as population attribute information, user behavior information, consumption information and the like is extracted from data as much as possible. The main algorithms used are classification and clustering. The classification is mainly used for predicting information of new users and users with incomplete information and predicting and classifying the users. The clustering is mainly used for analyzing and excavating group information with the same characteristics, and carrying out audience segmentation and market segmentation. For the feature data of the text, similarity calculation is mainly used, such as cosine included angle, Euclidean distance and the like. Herein using the formula for cosine angle
Cosine phaseThe similarity uses the cosine value of the included angle between two vectors in the vector space as the measure of the difference between two individuals. The cosine value is closer to 1, which indicates that the included angle is closer to 0 degree, namely the two vectors are more similar, which is called cosine similarity.
And (5) a model label layer. And (3) a machine learning method is used and a recommendation algorithm is combined. And the model label layer completes label modeling and user identification of the user. Through modeling analysis, the group characteristics and the individual weight characteristics of the users can be further mined, so that the value measurement of the users, the service satisfaction measurement and the like are perfected.
And predicting the layer. And also a marketing model prediction layer in the label system. The hierarchy utilizes a prediction algorithm, such as supervised learning in machine learning, to realize loyalty prediction, interest degree prediction and the like of users, so that accurate marketing, personalization and customization services are realized.
The user portrait basic steps are as follows:
after the user portrait orientation is determined according to specific business rules, user portrait analysis is performed, and in general, referring to fig. 2, a user portrait flow includes the following three steps: a base orientation of the user representation; collecting user data; and modeling the user label.
Histogram of Oriented Gradient (HOG) features are a kind of feature descriptors used for object detection in computer vision and image processing. It constructs features by calculating and counting the histogram of gradient direction of local area of image. The Hog feature has been widely used in image recognition, and has been highly successful in pedestrian detection in particular. It first divides the image into small connected regions, which we call the cell units. And then acquiring the gradient or edge direction histogram of each pixel point in the cell unit. Finally, combining these histograms to form a feature descriptor, as shown in fig. 3, the specific implementation process is as follows:
1) normalized gamma space and color space
To reduce the influence of the illumination factor, the whole image needs to be normalized first. In the texture intensity of the image, the local exposure contribution of the surface layer is large in proportion, so that the compression processing can effectively reduce the local shadow and illumination change of the image. Because the color information has little effect, the color information is usually converted into a gray scale image;
2) computing image gradients
Calculating the gradients of the horizontal coordinate and the vertical coordinate of the image, and calculating the gradient direction value of each pixel position according to the gradients; the derivation operation not only captures contours, shadows and some texture information, but also further weakens the influence of illumination. The gradient of the pixel points in the image is:
Gx(x，y)＝H(x+1，y)-H(x-1，y)
Gy(x,y)＝H(x,y+1)-H(x,y-1)
in the formula Gx(x，y)，Gy(x, y), and H (x, y) respectively represent a horizontal direction gradient, a vertical direction gradient, and a pixel value at a pixel point (x, y) in the input image. The gradient amplitude and gradient direction at pixel point (x, y) are respectively:
3) construction of a gradient direction histogram for each cell unit
The purpose of the third step is to provide a coding for the local image areas while maintaining a weak sensitivity to the pose and appearance of the human object in the image. We divide the image into several "cell cells", e.g. 6 x 6 pixels per cell. Let us assume that we use a histogram of 9 bins to count the gradient information of these 6 x 6 pixels. That is, the gradient direction of the cell is divided into 9 direction blocks by 360 degrees, as shown in the figure: for example: if the gradient direction of the pixel is 20-40 degrees, the count of the 2 nd bin of the histogram is incremented, so that the gradient direction histogram of the cell can be obtained by performing weighted projection (mapping to a fixed angle range) on each pixel in the cell in the histogram by using the gradient direction, namely the 9-dimensional feature vector corresponding to the cell (because of 9 bins).
4) Grouping of cell units into large blocks, intra-block normalized gradient histograms
The range of variation of the gradient intensity is very large due to the variation of the local illumination and the variation of the foreground-background contrast. This requires normalization of the gradient strength. Normalization can further compress lighting, shadows, and edges. The method adopted by I is as follows: the individual cell units are grouped into large, spatially connected compartments (blocks). Thus, the feature vectors of all cells in a block are concatenated to obtain the HOG feature of the block. These intervals overlap each other, which means that: the features of each cell appear in the final feature vector multiple times with different results. We refer to the block descriptor (vector) after normalization as the HOG descriptor.
5) Collecting HOG characteristics
The last step is to collect the HOG features of all the overlapped blocks in the detection window and combine them into the final feature vector for classification.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (2)
1. A user profile based library book recommendation system, comprising:
a monitor installed at an entrance of a library for extracting an avatar of a customer;
the facial recognition module is connected with the monitoring module and used for extracting facial features of the customer so as to judge whether the customer is an old customer;
the book recommendation module is used for searching similar readers of the target reader by using social attributes and interest attributes in the user portrait of the customer through a similarity calculation method, finding out books which are interested by the similar readers, and sequencing and recommending the books in parallel;
the wireless transmission network module is used for receiving the position information sent by the mobile phone of the customer through a wireless transmission network;
the processor is connected with the book recommending module and the wireless transmission network module and is used for integrating the recommended books and the positions of the customers;
and the LED display module is used for projecting the recommended book route fed back from the processor to the LED screen for display through the wireless transmission network module.
2. A library book recommendation method based on user portraits is characterized by comprising the following steps:
extracting the head portrait of the customer through a monitor installed at an entrance of the library;
extracting facial features of the customer through a facial recognition module, and judging whether the customer is an old customer;
through a book recommendation module, using social attributes and interest attributes in the user portrait of the customer, searching similar readers of the target reader through a similarity calculation method, finding out books which are interested by the similar readers, and sequencing and recommending in parallel;
receiving position information sent by a customer mobile phone through a wireless transmission network module;
integrating the recommended books and the positions of the customers through a processor;
and throwing the recommended book route fed back from the processor to an LED screen for display through a wireless transmission network module.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011033243.7A CN112131477A (en) | 2020-09-27 | 2020-09-27 | Library book recommendation system and method based on user portrait |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011033243.7A CN112131477A (en) | 2020-09-27 | 2020-09-27 | Library book recommendation system and method based on user portrait |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112131477A true CN112131477A (en) | 2020-12-25 |
Family
ID=73840577
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011033243.7A Pending CN112131477A (en) | 2020-09-27 | 2020-09-27 | Library book recommendation system and method based on user portrait |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112131477A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113051144A (en) * | 2021-03-26 | 2021-06-29 | 中山大学 | Intelligent contract recommendation method and device |
| CN114022937A (en) * | 2021-11-10 | 2022-02-08 | 广州图创计算机软件开发有限公司 | Intelligent perception multifunctional intelligent terminal management system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488714A (en) * | 2013-09-11 | 2014-01-01 | 杭州东信北邮信息技术有限公司 | Book recommendation method and system based on social networking |
| CN104156436A (en) * | 2014-08-13 | 2014-11-19 | 福州大学 | Social association cloud media collaborative filtering and recommending method |
| CN109920174A (en) * | 2019-01-23 | 2019-06-21 | 平安科技（深圳）有限公司 | Book borrowing and reading method, apparatus, electronic equipment and storage medium |
| CN110706014A (en) * | 2018-07-10 | 2020-01-17 | 杭州海康威视系统技术有限公司 | Shopping mall store recommendation method, device and system |
| CN111144283A (en) * | 2019-12-25 | 2020-05-12 | 辽宁工程技术大学 | Intelligent supermarket commodity recommendation system based on face recognition |
| CN111222053A (en) * | 2019-11-27 | 2020-06-02 | 腾讯音乐娱乐科技（深圳）有限公司 | Object recommendation method and device and related equipment |
-
2020
- 2020-09-27 CN CN202011033243.7A patent/CN112131477A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103488714A (en) * | 2013-09-11 | 2014-01-01 | 杭州东信北邮信息技术有限公司 | Book recommendation method and system based on social networking |
| CN104156436A (en) * | 2014-08-13 | 2014-11-19 | 福州大学 | Social association cloud media collaborative filtering and recommending method |
| CN110706014A (en) * | 2018-07-10 | 2020-01-17 | 杭州海康威视系统技术有限公司 | Shopping mall store recommendation method, device and system |
| CN109920174A (en) * | 2019-01-23 | 2019-06-21 | 平安科技（深圳）有限公司 | Book borrowing and reading method, apparatus, electronic equipment and storage medium |
| CN111222053A (en) * | 2019-11-27 | 2020-06-02 | 腾讯音乐娱乐科技（深圳）有限公司 | Object recommendation method and device and related equipment |
| CN111144283A (en) * | 2019-12-25 | 2020-05-12 | 辽宁工程技术大学 | Intelligent supermarket commodity recommendation system based on face recognition |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113051144A (en) * | 2021-03-26 | 2021-06-29 | 中山大学 | Intelligent contract recommendation method and device |
| CN113051144B (en) * | 2021-03-26 | 2022-02-08 | 中山大学 | Intelligent contract recommendation method and device |
| CN114022937A (en) * | 2021-11-10 | 2022-02-08 | 广州图创计算机软件开发有限公司 | Intelligent perception multifunctional intelligent terminal management system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106776619B (en) | Method and device for determining attribute information of target object | |
| US11922674B2 (en) | Systems, methods, and storage media for evaluating images | |
| Vittayakorn et al. | Runway to realway: Visual analysis of fashion | |
| Segalin et al. | The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits | |
| Corcoran et al. | Segmentation performance evaluation for object-based remotely sensed image analysis | |
| CN109558535B (en) | Personalized article pushing method and system based on face recognition | |
| US20140172643A1 (en) | System and method for categorizing an image | |
| CN108734184B (en) | Method and device for analyzing sensitive image | |
| Jaiswal et al. | An intelligent recommendation system using gaze and emotion detection | |
| Wu et al. | Optimizing mean reciprocal rank for person re-identification | |
| WO2014068567A1 (en) | Method and system for predicting personality traits, capabilities and suggested interactions from images of a person | |
| Zhang et al. | Image retrieval of wool fabric. Part II: based on low-level color features | |
| Kuo et al. | Automatic machine embroidery image color analysis system. Part I: Using Gustafson-Kessel clustering algorithm in embroidery fabric color separation | |
| AU2017231602A1 (en) | Method and system for visitor tracking at a POS area | |
| Zhang et al. | Image retrieval of wool fabric. Part I: Based on low-level texture features | |
| CN111784372A (en) | Store commodity recommendation method and device | |
| Carranza-Rojas et al. | Combining leaf shape and texture for Costa Rican plant species identification | |
| CN112131477A (en) | Library book recommendation system and method based on user portrait | |
| CN114692593B (en) | Network information safety monitoring and early warning method | |
| Jayanthi et al. | Leaf disease segmentation from agricultural images via hybridization of active contour model and OFA | |
| CN114971294A (en) | Data acquisition method, device, equipment and storage medium | |
| Yang et al. | Visual saliency detection with center shift | |
| CN112837108A (en) | Information processing method and device and electronic equipment | |
| Suksangaram et al. | Automated image recognition for consumer behavior analysis: Histogram of orientation gradient | |
| US20220172271A1 (en) | Method, device and system for recommending information, 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 |