CN113010784A - Method, apparatus, electronic device, and medium for generating prediction information - Google Patents
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Abstract
Embodiments of the present disclosure disclose methods, apparatuses, electronic devices, and media for generating prediction information. One embodiment of the method comprises: acquiring user related information of a user; determining an emotion label of the user-related information based on the user-related information; receiving personal information input by the user aiming at a target information acquisition page; generating prediction information based on the emotion label and the personal information. The embodiment can judge the user on the basis of the interaction record of the user and the personal information which is subjectively filled in. Therefore, the generated prediction information has high accuracy, and can provide accurate prompt for service personnel so that the service personnel can provide insurance service for the user better and the user experience is improved laterally.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a method, an apparatus, an electronic device, and a medium for generating prediction information.
Background
With the development of the internet and the enhancement of the public insurance awareness, it is difficult to judge whether the user intends to meet the need of insurance in the absence of subjective information submission and interactive records of the user. The insurance requirements of the user cannot be made clear, and thus the insurance service cannot be provided better.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose methods, apparatuses, electronic devices, and media for generating prediction information to solve the technical problems mentioned in the background section above.
In a first aspect, some embodiments of the present disclosure provide a method for generating prediction information, the method comprising: acquiring user related information of a user; determining an emotion label of the user-related information based on the user-related information; receiving personal information input by the user aiming at a target information acquisition page; generating prediction information based on the emotion label and the personal information.
In a second aspect, some embodiments of the present disclosure provide an apparatus for generating prediction information, the apparatus comprising: an acquisition unit configured to acquire user-related information of a user; a determining unit configured to determine an emotion tag of the user-related information based on the user-related information; a receiving unit configured to receive personal information input by the user for a target information collection page; a generating unit configured to generate prediction information based on the emotion label and the personal information.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement the method as described in the first aspect.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method as described in the first aspect.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: the emotion label of the user-related information is determined by acquiring the user-related information of the user. Then, personal information subjectively input by the user is received, and prediction information for judging the insurance demand intention of the user is generated. In addition, the user-related information includes browsing record information of the user, and thus, the method provided by the embodiment can judge the user on the basis of the interaction record of the user and the personal information which is subjectively filled in. Therefore, the generated prediction information has high accuracy, and can provide accurate prompt for service personnel so that the service personnel can provide insurance service for the user better and the user experience is improved laterally.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic illustration of one application scenario of a method for generating prediction information, in accordance with some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of a method for generating prediction information according to the present disclosure;
FIG. 3 is a schematic block diagram of some embodiments of an apparatus for generating prediction information according to the present disclosure;
FIG. 4 is a schematic block diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of one application scenario of a method for generating prediction information according to some embodiments of the present disclosure.
In the application scenario of fig. 1, first, the computing device 101 may obtain user-related information 102 of a user. Computing device 101 may then determine emotion label 103 for the user-related information 102 based on the user-related information 102. Thereafter, the computing device 101 may receive personal information 104 entered by the user for the target information collection page. Finally, computing device 101 may generate predictive information 105 based on the emotion tags 103 and the personal information 104.
The computing device 101 may be hardware or software. When the computing device is hardware, it may be implemented as a distributed cluster composed of multiple servers or terminal devices, or may be implemented as a single server or a single terminal device. When the computing device is embodied as software, it may be installed in the hardware devices enumerated above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of computing devices in FIG. 1 is merely illustrative. There may be any number of computing devices, as implementation needs dictate.
With continued reference to fig. 2, a flow 200 of some embodiments of a method for generating prediction information in accordance with the present disclosure is shown. The method may be performed by the computing device 101 of fig. 1. The method for generating the prediction information comprises the following steps:
In some embodiments, the executing entity (e.g., computing device 101 shown in fig. 1) of the method for generating predictive information may obtain user-related information for a user via a wireless connection. Here, the user-related information may include, but is not limited to, at least one of: browsing record information of the browsing content of the user and emotion related information input by the user, wherein the browsing record information comprises emotion label information of the browsing content, browsing duration of the user and clicking behavior of the user. Specifically, the emotion tag information of the browsing content may be set in advance.
In some optional implementation manners of some embodiments, the executing entity may obtain the user-related information by: the method comprises the steps that in response to the fact that clicking operation of a user on an entry control used for representing a target activity page is detected, the target activity page is displayed, wherein the target activity page comprises a content display area and a content composition area; secondly, in response to detecting user operations (for example, sliding operations, clicking operations and dragging operations) of the user on the content in the content display area, acquiring browsing record related information, and determining the browsing record related information as browsing record information in the user related information; a third step of receiving information input by the user in response to the detection of the input operation of the user for the content composition area, and determining the information input by the user as emotion-related information in the user-related information; and fourthly, combining the browsing record related information and the information input by the user to obtain the user related information.
It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
In some embodiments, the executing entity may determine the emotion label of the user-related information by: the execution main body can calculate and determine the acceptance of the user to the browsing content based on the browsing record information of the browsing content of the user; secondly, the executive body can determine the emotion attribute of the user based on the emotion related information input by the user; and thirdly, the executive body can determine the emotion label of the user related information based on the receptivity and the emotion attribute. Here, the emotion label may be description information for characterizing the current emotion of the user. As an example, the emotional tag may be "happy," may be "self-confident," or may be "curious.
In some optional implementation manners of some embodiments, the executing body may calculate, according to a preset browsing weight, emotion tag information of browsing content in the browsing record information, browsing duration of the user, and a click behavior of the user, to obtain a score for representing an acceptance of the browsing content.
In some optional implementation manners of some embodiments, the executing body may input the emotion related information input by the user to a pre-trained emotion attribute determination model to obtain emotion attribute information. Here, the emotion attribute determination model may be a word vector Language model based on training, for example, a Natural Language Processing model (NLP).
In some optional implementation manners of some embodiments, the executing entity may calculate the emotion attribute information and the receptivity according to a preset emotion weight, so as to obtain a calculation result. Then, the execution main body may select an emotion tag from a preset emotion tag library as an emotion tag of the user-related information based on the calculation result.
In some embodiments, the execution main body may receive, through a wireless connection, personal information input by the user with respect to the target information collection page. Here, the personal information includes user personal attribute tag information and user family attribute tag information. Specifically, the user personal attribute tag information includes, but is not limited to, at least one of the following: age, gender, occupation, personal income, academic history, residence, social insurance, whether or not they have been refused to be insured, diseases, body quality index, habits such as smoking and drinking, and purchase budget. The user family attribute tag information includes, but is not limited to, at least one of the following: family income, family structure (marital status, number of children, aged support), and family liability status.
And step 204, generating prediction information based on the emotion label and the personal information.
In some embodiments, the executing entity may input the emotion label and the personal information to a prediction information generation model of a preselected training, so as to obtain prediction information. Here, the prediction information generation model is obtained by training a training sample set. The prediction information may be a score for representing the insurance demand of the user, or may be text information for representing the insurance demand of the user. As an example, the prediction information may be "85.9 points", and the prediction information may also be "the user has an insurance need 85.9% probability".
In some optional implementations of some embodiments, the prediction information generation model may be obtained by training: the method comprises the steps of firstly, obtaining a training sample set, wherein training samples comprise sample emotion labels, sample personal information and sample prediction information; secondly, selecting training samples from the training sample set; thirdly, inputting sample emotion labels and sample personal information in training samples of the training sample set into the initial model; fourthly, comparing the output prediction information with the sample prediction information to obtain an information loss value; fifthly, comparing the information loss value with a preset threshold value to obtain a comparison result; sixthly, determining whether the initial model is trained or not according to the comparison result; and seventhly, determining the initial model as a prediction information generation model in response to determining that the training of the initial model is completed.
The method further comprises the following steps: and responding to the condition that the initial model is not trained completely, adjusting relevant parameters in the initial model, reselecting samples from the training sample set, and continuing to execute the training step by using the adjusted initial model as the initial model.
The information loss value stated above may be a value obtained by inputting the above-mentioned output prediction information and corresponding sample prediction information as parameters into an executed loss function. Here, the loss function (e.g., a square loss function, an exponential loss function, etc.) is generally used to measure the degree of disagreement between the predicted value (e.g., the sample emotion label and the sample prediction information corresponding to the sample personal information) and the true value (e.g., the prediction information obtained through the above steps) of the model. It is a non-negative real-valued function. In general, the smaller the loss function, the better the robustness of the model. The loss function may be set according to actual requirements. As an example, the loss function may be a Cross Entropy loss function (Cross entry).
Here, the initial model may be a model that is not trained or does not reach a preset condition after training. The initial model may be a model having a deep neural network structure. The neural network model may have various existing neural network structures. For example, the Neural Network structure may be a Convolutional Neural Network (CNN). The storage location of the initial model is likewise not limiting in this disclosure.
Optionally, the execution subject may update the training sample set of the prediction information generation model according to a preset period, so as to improve accuracy of generating the prediction information.
In some optional implementations of some embodiments, the method further comprises: transmitting the prediction information to a target device having a display function, and controlling the device to display the prediction information.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: the emotion label of the user-related information is determined by acquiring the user-related information of the user. Then, personal information subjectively input by the user is received, and prediction information for judging the insurance demand intention of the user is generated. In addition, the user-related information includes browsing record information of the user, and thus, the method provided by the embodiment can judge the user on the basis of the interaction record of the user and the personal information which is subjectively filled in. Therefore, the generated prediction information has high accuracy, and can provide accurate prompt for service personnel so that the service personnel can provide insurance service for the user better and the user experience is improved laterally.
With further reference to fig. 3, as an implementation of the above-described method for the above-described figures, the present disclosure provides some embodiments of an apparatus for generating prediction information, which correspond to those of the method embodiments described above for fig. 2, and which may be applied in particular to various electronic devices.
As shown in fig. 3, an apparatus 300 for generating prediction information of some embodiments includes: an acquisition unit 301, a determination unit 302, a reception unit 303, and a generation unit 304. Wherein, the obtaining unit 301 is configured to obtain user-related information of a user; a determining unit 302 configured to determine an emotion label of the user-related information based on the user-related information; a receiving unit 303 configured to receive personal information input by the user for a target information collection page; a generating unit 304 configured to generate prediction information based on the emotion label and the personal information.
In some optional implementations of some embodiments, the user-related information includes: browsing record information of the browsing content of the user and emotion related information input by the user, wherein the browsing record information comprises emotion label information of the browsing content, browsing duration of the user and clicking behavior of the user.
In some optional implementations of some embodiments, the determining unit 302 of the apparatus 300 for generating prediction information is further configured to: calculating and determining the acceptance of the user to the browsed content based on the browsing record information of the browsed content of the user; determining an emotion attribute of the user based on the emotion-related information input by the user; determining an emotion label for the user-related information based on the receptivity and the emotion attribute.
In some optional implementations of some embodiments, the determining the emotion attribute of the user based on the emotion related information input by the user includes: and inputting the emotion related information input by the user into a pre-trained emotion attribute judgment model to obtain emotion attribute information.
In some optional implementations of some embodiments, the personal information includes user personal attribute tag information and user family attribute tag information.
In some optional implementations of some embodiments, the generating unit 304 of the apparatus 300 for generating prediction information is further configured to: and inputting the emotion label and the personal information into a pre-trained prediction information generation model to obtain prediction information.
In some optional implementations of some embodiments, the means 300 for generating prediction information is further configured to: transmitting the prediction information to a target device having a display function, and controlling the target device to display the prediction information.
It will be understood that the units described in the apparatus 300 correspond to the various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 300 and the units included therein, and are not described herein again.
Referring now to FIG. 4, a block diagram of an electronic device (e.g., computing device 101 of FIG. 1)400 suitable for use in implementing some embodiments of the present disclosure is shown. The server shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, electronic device 400 may include a processing device (e.g., central processing unit, graphics processor, etc.) 401 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage device 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for the operation of the electronic apparatus 400 are also stored. The processing device 401, the ROM 402, and the RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate wirelessly or by wire with other devices to exchange data. While fig. 4 illustrates an electronic device 400 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 4 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through the communication device 409, or from the storage device 408, or from the ROM 402. The computer program, when executed by the processing apparatus 401, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the apparatus; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring user related information of a user; determining an emotion label of the user-related information based on the user-related information; receiving personal information input by the user aiming at a target information acquisition page; generating prediction information based on the emotion label and the personal information.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes an acquisition unit, a determination unit, a reception unit, and a generation unit. The names of these units do not in some cases constitute a limitation to the unit itself, and for example, the acquiring unit may also be described as a "unit that acquires user-related information of a user".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A method for generating prediction information, comprising:
acquiring user related information of a user;
determining an emotion label of the user-related information based on the user-related information;
receiving personal information input by the user aiming at a target information acquisition page;
generating prediction information based on the emotion label and the personal information.
2. The method of claim 1, wherein the user-related information comprises: browsing record information of the browsing content of the user and emotion related information input by the user, wherein the browsing record information comprises emotion label information of the browsing content, browsing duration of the user and clicking behavior of the user.
3. The method of claim 2, wherein determining the emotion label for the user-related information based on the user-related information comprises:
calculating and determining the acceptance of the user to the browsed content based on the browsing record information of the browsed content of the user;
determining an emotion attribute of the user based on the emotion-related information input by the user;
determining an emotion label for the user-related information based on the receptivity and the emotion attribute.
4. The method of claim 3, wherein determining the emotional attribute of the user based on the emotion-related information input by the user comprises:
and inputting the emotion related information input by the user into a pre-trained emotion attribute judgment model to obtain emotion attribute information.
5. The method of claim 1, wherein the personal information includes user personal attribute tag information and user family attribute tag information.
6. The method of claim 5, wherein generating predictive information based on the emotion tag and the personal information comprises:
and inputting the emotion label and the personal information into a pre-trained prediction information generation model to obtain prediction information.
7. The method according to one of claims 1 to 6, characterized in that the method further comprises:
transmitting the prediction information to a target device having a display function, and controlling the target device to display the prediction information.
8. An apparatus for generating prediction information, comprising:
an acquisition unit configured to acquire user-related information of a user;
a determining unit configured to determine an emotion tag of the user-related information based on the user-related information;
a receiving unit configured to receive personal information input by the user for a target information collection page;
a generating unit configured to generate prediction information based on the emotion label and the personal information.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
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| CN202110285219.0A CN113010784B (en) | 2021-03-17 | 2021-03-17 | Method, apparatus, electronic device and medium for generating prediction information |
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Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090234718A1 (en) * | 2000-09-05 | 2009-09-17 | Novell, Inc. | Predictive service systems using emotion detection |
| CN103678304A (en) * | 2012-08-31 | 2014-03-26 | 国际商业机器公司 | Method and device for pushing specific content for predetermined webpage |
| CN104781837A (en) * | 2012-08-15 | 2015-07-15 | 汤姆森路透社全球资源公司 | System and method for forming predictions using event-based sentiment analysis |
| CN104836720A (en) * | 2014-02-12 | 2015-08-12 | 北京三星通信技术研究有限公司 | Method for performing information recommendation in interactive communication, and device |
| CN105843922A (en) * | 2016-03-25 | 2016-08-10 | 乐视控股（北京）有限公司 | Multimedia classification recommendation method, apparatus and system |
| CN106844646A (en) * | 2017-01-22 | 2017-06-13 | 竹间智能科技（上海）有限公司 | Artificial intelligence reply method and device based on emotional prediction |
| WO2017209482A1 (en) * | 2016-05-31 | 2017-12-07 | (주)잼투고 | System and method for providing video content |
| CN107562816A (en) * | 2017-08-16 | 2018-01-09 | 深圳狗尾草智能科技有限公司 | User view automatic identifying method and device |
| CN107730389A (en) * | 2017-09-30 | 2018-02-23 | 平安科技（深圳）有限公司 | Electronic installation, insurance products recommend method and computer-readable recording medium |
| CN108073640A (en) * | 2016-11-17 | 2018-05-25 | 广州市动景计算机科技有限公司 | Page push method and system |
| CN108427722A (en) * | 2018-02-09 | 2018-08-21 | 卫盈联信息技术（深圳）有限公司 | intelligent interactive method, electronic device and storage medium |
| CN108876541A (en) * | 2018-06-20 | 2018-11-23 | 郑州村村联网络技术有限公司 | Method of Commodity Recommendation and device |
| CN108961072A (en) * | 2018-06-07 | 2018-12-07 | 平安科技（深圳）有限公司 | Push method, apparatus, computer equipment and the storage medium of insurance products |
| CN109033257A (en) * | 2018-07-06 | 2018-12-18 | 中国平安人寿保险股份有限公司 | Talk about art recommended method, device, computer equipment and storage medium |
| CN109165982A (en) * | 2018-08-28 | 2019-01-08 | 百度在线网络技术（北京）有限公司 | The determination method and apparatus of user's purchase information |
| CN109767261A (en) * | 2018-12-18 | 2019-05-17 | 深圳壹账通智能科技有限公司 | Products Show method, apparatus, computer equipment and storage medium |
| CN109816141A (en) * | 2018-12-13 | 2019-05-28 | 平安科技（深圳）有限公司 | Product data prediction technique, device, computer equipment based on data analysis |
| CN109933782A (en) * | 2018-12-03 | 2019-06-25 | 阿里巴巴集团控股有限公司 | User emotion prediction technique and device |
| CN110046540A (en) * | 2018-12-29 | 2019-07-23 | 广州二元科技有限公司 | A method of user emotion is judged according to browsing history and Expression Recognition |
| CN110162602A (en) * | 2019-05-31 | 2019-08-23 | 浙江核新同花顺网络信息股份有限公司 | A kind of intelligent interactive method and system |
| CN110263326A (en) * | 2019-05-21 | 2019-09-20 | 平安科技（深圳）有限公司 | A kind of user's behavior prediction method, prediction meanss, storage medium and terminal device |
| CN110399548A (en) * | 2018-04-20 | 2019-11-01 | 北京搜狗科技发展有限公司 | A kind of search processing method, device, electronic equipment and storage medium |
| CN111291184A (en) * | 2020-01-20 | 2020-06-16 | 百度在线网络技术（北京）有限公司 | Expression recommendation method, device, equipment and storage medium |
| CN111339284A (en) * | 2020-02-13 | 2020-06-26 | 深圳壹账通智能科技有限公司 | Product intelligent matching method, device, equipment and readable storage medium |
| WO2020221103A1 (en) * | 2019-04-30 | 2020-11-05 | 上海掌门科技有限公司 | Method for displaying user emotion, and device |
| CN112330358A (en) * | 2020-10-22 | 2021-02-05 | 北京明略昭辉科技有限公司 | Method and apparatus for product sales prediction, storage medium, and electronic device |
-
2021
- 2021-03-17 CN CN202110285219.0A patent/CN113010784B/en active Active
Patent Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090234718A1 (en) * | 2000-09-05 | 2009-09-17 | Novell, Inc. | Predictive service systems using emotion detection |
| CN104781837A (en) * | 2012-08-15 | 2015-07-15 | 汤姆森路透社全球资源公司 | System and method for forming predictions using event-based sentiment analysis |
| CN103678304A (en) * | 2012-08-31 | 2014-03-26 | 国际商业机器公司 | Method and device for pushing specific content for predetermined webpage |
| CN104836720A (en) * | 2014-02-12 | 2015-08-12 | 北京三星通信技术研究有限公司 | Method for performing information recommendation in interactive communication, and device |
| CN105843922A (en) * | 2016-03-25 | 2016-08-10 | 乐视控股（北京）有限公司 | Multimedia classification recommendation method, apparatus and system |
| WO2017209482A1 (en) * | 2016-05-31 | 2017-12-07 | (주)잼투고 | System and method for providing video content |
| CN108073640A (en) * | 2016-11-17 | 2018-05-25 | 广州市动景计算机科技有限公司 | Page push method and system |
| CN106844646A (en) * | 2017-01-22 | 2017-06-13 | 竹间智能科技（上海）有限公司 | Artificial intelligence reply method and device based on emotional prediction |
| CN107562816A (en) * | 2017-08-16 | 2018-01-09 | 深圳狗尾草智能科技有限公司 | User view automatic identifying method and device |
| CN107730389A (en) * | 2017-09-30 | 2018-02-23 | 平安科技（深圳）有限公司 | Electronic installation, insurance products recommend method and computer-readable recording medium |
| CN108427722A (en) * | 2018-02-09 | 2018-08-21 | 卫盈联信息技术（深圳）有限公司 | intelligent interactive method, electronic device and storage medium |
| CN110399548A (en) * | 2018-04-20 | 2019-11-01 | 北京搜狗科技发展有限公司 | A kind of search processing method, device, electronic equipment and storage medium |
| CN108961072A (en) * | 2018-06-07 | 2018-12-07 | 平安科技（深圳）有限公司 | Push method, apparatus, computer equipment and the storage medium of insurance products |
| CN108876541A (en) * | 2018-06-20 | 2018-11-23 | 郑州村村联网络技术有限公司 | Method of Commodity Recommendation and device |
| CN109033257A (en) * | 2018-07-06 | 2018-12-18 | 中国平安人寿保险股份有限公司 | Talk about art recommended method, device, computer equipment and storage medium |
| CN109165982A (en) * | 2018-08-28 | 2019-01-08 | 百度在线网络技术（北京）有限公司 | The determination method and apparatus of user's purchase information |
| CN109933782A (en) * | 2018-12-03 | 2019-06-25 | 阿里巴巴集团控股有限公司 | User emotion prediction technique and device |
| CN109816141A (en) * | 2018-12-13 | 2019-05-28 | 平安科技（深圳）有限公司 | Product data prediction technique, device, computer equipment based on data analysis |
| CN109767261A (en) * | 2018-12-18 | 2019-05-17 | 深圳壹账通智能科技有限公司 | Products Show method, apparatus, computer equipment and storage medium |
| CN110046540A (en) * | 2018-12-29 | 2019-07-23 | 广州二元科技有限公司 | A method of user emotion is judged according to browsing history and Expression Recognition |
| WO2020221103A1 (en) * | 2019-04-30 | 2020-11-05 | 上海掌门科技有限公司 | Method for displaying user emotion, and device |
| CN110263326A (en) * | 2019-05-21 | 2019-09-20 | 平安科技（深圳）有限公司 | A kind of user's behavior prediction method, prediction meanss, storage medium and terminal device |
| CN110162602A (en) * | 2019-05-31 | 2019-08-23 | 浙江核新同花顺网络信息股份有限公司 | A kind of intelligent interactive method and system |
| CN111291184A (en) * | 2020-01-20 | 2020-06-16 | 百度在线网络技术（北京）有限公司 | Expression recommendation method, device, equipment and storage medium |
| CN111339284A (en) * | 2020-02-13 | 2020-06-26 | 深圳壹账通智能科技有限公司 | Product intelligent matching method, device, equipment and readable storage medium |
| CN112330358A (en) * | 2020-10-22 | 2021-02-05 | 北京明略昭辉科技有限公司 | Method and apparatus for product sales prediction, storage medium, and electronic device |
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