Method and apparatus for generating information

Abstract

The disclosed embodiments disclose a method and apparatus for generating information. A specific implementation of the method includes: obtaining historical user information from at least one terminal device used by a user, wherein the historical user information includes basic user information, historical scenario information, and historical behavior information corresponding to the historical scenario information; establishing a behavior information generation model for the user based on the historical user information, wherein the behavior information generation model is used to generate the user's behavior information based on scenario information of the scenario in which the user is located; and generating behavior information based on the scenario information of the scenario in which the user is located and the behavior information generation model. This implementation implements the generation of behavior information.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer technology, and more particularly, to a method and apparatus for generating information. Background Art

[0002] The rapid development of internet technology has made people's lives more convenient. For example, the emergence of web browsers, instant messaging tools, shopping apps, search apps, and mapping apps has opened up a vast world of possibilities for people. People's internet activities leave behind a wide range of data, and how to effectively utilize this data has long been a hot topic of research in the industry. For example, based on the data left by a user's internet activity, a user profile can be constructed, allowing for further decision-making based on this profile. Furthermore, various other information can be generated based on the data left by users' internet activities. Summary of the Invention

[0003] The embodiments of the present disclosure provide a method and apparatus for generating information.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for generating information, the method comprising: obtaining historical user information from at least one terminal device used by a user, wherein the historical user information comprises basic user information, historical scenario information, and historical behavior information corresponding to the historical scenario information; establishing a behavior information generation model for the user based on the historical user information, wherein the behavior information generation model is used to generate the behavior information of the user based on scenario information of the scenario in which the user is located; and generating behavior information based on the scenario information of the scenario in which the user is located and the behavior information generation model.

[0005] In some embodiments, the above method also includes: sending preset specific scenario information to the above user, and receiving feedback behavior information input by the above user for the above specific scenario information; obtaining the generated behavior information generated by the above behavior information generation model for the above specific scenario information; in response to determining that the above feedback behavior information does not match the above generated behavior information, training the above behavior information generation model based on the above specific scenario information and the above feedback behavior information.

[0006] In some embodiments, the above method also includes: receiving a viewing request sent by the above user, wherein the above viewing request is used to view the behavior information generated by the above behavior information generation model within a preset time period; and presenting the behavior information generated by the above behavior information generation model within the preset time period to the above user according to the above viewing request.

[0007] In some embodiments, the above method also includes: receiving modification information sent by the above user regarding the presented behavior information, wherein the above modification information is used to modify the presented behavior information; and training the above behavior information generation model based on the above modification information.

[0008] In some embodiments, the above-mentioned generating behavior information based on the scene information of the scene in which the above-mentioned user is located and the above-mentioned behavior information generation model includes: receiving associated behavior information generated by at least one associated behavior information generation model, wherein the above-mentioned at least one associated behavior information generation model is established based on user information of at least one associated user who has an associated relationship with the above-mentioned user; using the received associated behavior information as scene information, and generating behavior information based on the above-mentioned behavior information generation model.

[0009] In some embodiments, the above-mentioned behavior information generation model includes at least one sub-model, wherein the sub-model in the above-mentioned at least one sub-model is used to generate predetermined category behavior information; and the above-mentioned establishment of a behavior information generation model for the above-mentioned user based on the above-mentioned historical user information includes: based on a machine learning algorithm, using the above-mentioned user basic information, historical scene information and historical behavior information corresponding to the historical scene information to train the sub-model in the above-mentioned at least one sub-model to obtain a behavior information generation model for the above-mentioned user.

[0010] In some embodiments, the above-mentioned establishment of a behavior information generation model for the above-mentioned user based on the above-mentioned historical user information includes: receiving adjustment data input by the above-mentioned user for the above-mentioned historical user information, wherein the above-mentioned adjustment data is used to adjust the above-mentioned historical user information; and using the adjusted historical user information to establish a behavior information generation model for the above-mentioned user.

[0011] In some embodiments, the above method further includes: acquiring behavior information generated by multiple models including the above behavior information generation model; performing statistical analysis on the acquired behavior information, and displaying the statistical analysis results.

[0012] In a second aspect, an embodiment of the present disclosure provides a device for generating information, the device comprising: an acquisition unit, configured to acquire historical user information from at least one terminal device used by a user, wherein the historical user information comprises basic user information, historical scene information and historical behavior information corresponding to the historical scene information; an establishment unit, configured to establish a behavior information generation model for the user based on the historical user information, wherein the behavior information generation model is used to generate the behavior information of the user based on scene information of the scene in which the user is located; a generation unit, configured to generate behavior information based on the scene information of the scene in which the user is located and the behavior information generation model.

[0013] In some embodiments, the above-mentioned device also includes: a receiving unit, configured to send preset specific scenario information to the above-mentioned user, and receive feedback behavior information input by the above-mentioned user for the above-mentioned specific scenario information; a feedback unit, configured to obtain the generated behavior information generated by the above-mentioned behavior information generation model for the above-mentioned specific scenario information; a first training unit, configured to train the above-mentioned behavior information generation model based on the above-mentioned specific scenario information and the above-mentioned feedback behavior information in response to determining that the above-mentioned feedback behavior information does not match the above-mentioned generated behavior information.

[0014] In some embodiments, the above-mentioned device also includes: a request receiving unit, configured to receive a viewing request sent by the above-mentioned user, wherein the above-mentioned viewing request is used to view the behavior information generated by the above-mentioned behavior information generation model within a preset time period; a presentation unit, configured to present the behavior information generated by the above-mentioned behavior information generation model within the preset time period to the above-mentioned user according to the above-mentioned viewing request.

[0015] In some embodiments, the above-mentioned device also includes: an information receiving unit, configured to receive modification information sent by the above-mentioned user regarding the presented behavior information, wherein the above-mentioned modification information is used to modify the presented behavior information; a second training unit, configured to train the above-mentioned behavior information generation model based on the above-mentioned modification information.

[0016] In some embodiments, the above-mentioned generation unit is further configured to: receive associated behavior information generated by at least one associated behavior information generation model, wherein the above-mentioned at least one associated behavior information generation model is established based on user information of at least one associated user who has an associated relationship with the above-mentioned user; use the received associated behavior information as scene information, and generate behavior information based on the above-mentioned behavior information generation model.

[0017] In some embodiments, the above-mentioned behavior information generation model includes at least one sub-model, wherein the sub-model in the above-mentioned at least one sub-model is used to generate predetermined category behavior information; and the above-mentioned establishment unit is further configured to: based on a machine learning algorithm, use the above-mentioned user basic information, historical scene information and historical behavior information corresponding to the historical scene information to train the sub-model in the above-mentioned at least one sub-model to obtain a behavior information generation model for the above-mentioned user.

[0018] In some embodiments, the establishment unit is further configured to: receive adjustment data input by the user for the historical user information, wherein the adjustment data is used to adjust the historical user information; and use the adjusted historical user information to establish a behavior information generation model for the user.

[0019] In some embodiments, the above-mentioned device also includes: an information acquisition unit, configured to acquire behavior information generated by multiple models including the above-mentioned behavior information generation model; a statistical unit, configured to perform statistical analysis on the acquired behavior information and display the statistical analysis results.

[0020] In a third aspect, an embodiment of the present disclosure provides a server comprising: one or more processors; a storage device on which one or more programs are stored, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0021] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any implementation manner in the first aspect.

[0022] The method and apparatus for generating information provided by the embodiments of the present disclosure obtain historical user information from at least one terminal device used by the user, then establish a behavior information generation model for the user based on the historical user information, and finally generate the user's behavior information based on the scenario information of the user's scenario and the behavior information generation model, thereby realizing the generation of user behavior information. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present disclosure will become more apparent from a reading of the detailed description of non-limiting embodiments made with reference to the following drawings:

[0024] Figure 1 is an exemplary system architecture diagram in which an embodiment of the present disclosure may be applied;

[0025] Figure 2 is a flow chart of one embodiment of a method for generating information according to the present disclosure;

[0026] Figure 3 is a schematic diagram of an application scenario of the method for generating information according to the present disclosure;

[0027] Figure 4 is a schematic structural diagram of an embodiment of an apparatus for generating information according to the present disclosure;

[0028] Figure 5 It is a structural diagram of a computer system suitable for implementing a server of an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The present disclosure will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

[0030] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in the present disclosure can be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0031] Figure 1 An exemplary system architecture 100 is shown to which the method for generating information or the apparatus for generating information according to the embodiments of the present disclosure can be applied.

[0032] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0033] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0034] Terminal devices 101, 102, 103 can be hardware or software. When terminal devices 101, 102, 103 are hardware, they can be various electronic devices with display screens and supporting information interaction, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, Moving Picture Experts Group Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) players, laptop computers and desktop computers, etc. When terminal devices 101, 102, 103 are software, they can be installed in the electronic devices listed above. It can be implemented as multiple software or software modules (for example, to provide distributed services), or it can be implemented as a single software or software module. No specific limitation is made here.

[0035] The server 105 may be a server that provides various services, such as a backend server that processes user information generated by the terminal devices 101, 102, and 103. The backend server may analyze and process the received user information and other data, and feed back the processing results (e.g., behavior information) to the terminal devices 101, 102, and 103.

[0036] It should be noted that the server 105 can be hardware or software. When the server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server 105 is software, it can be implemented as multiple software or software modules (for example, to provide distributed services), or it can be implemented as a single software or software module. No specific limitations are given here.

[0037] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0038] It should be noted that the method for generating information provided in the embodiments of the present disclosure is generally executed by the server 105 , and accordingly, the device for generating information is generally set in the server 105 .

[0039] Continue to refer Figure 2 , shows a process 200 of an embodiment of a method for generating information according to the present disclosure. The method for generating information includes the following steps:

[0040] Step 201: Acquire historical user information from at least one terminal device used by the user.

[0041] In this embodiment, the execution subject of the method for generating information (eg Figure 1 The server 105 shown) can obtain historical user information from at least one terminal device used by the user through a wired connection or a wireless connection. Here, the historical user information may include basic user information, historical scene information, and historical behavior information corresponding to the historical scene information. Here, basic user information may refer to basic attribute information of the user, and the basic user information may include but is not limited to age, gender, occupation, place of residence, native place, education, marital status, income, hobbies, height, weight, etc. Historical scene information can be used to describe historical scenes, and the historical scene information may include but is not limited to time, place, weather, related people, etc. The historical behavior information corresponding to the historical scene information may refer to information on historical behavior generated by the above-mentioned user in the above-mentioned historical scene.

[0042] Typically, users generate a large amount of behavioral information when using terminal devices. For example, when users shop using devices such as smartphones and tablets, they generate shopping-related behavioral information. Users also generate a large amount of behavioral information when using smart wearable devices (such as smart bracelets, smart watches, and smart glasses). For example, smart bracelets can generate movement information. In practice, the execution entity can obtain historical user information from at least one terminal device used by the user through various methods. For example, after the user's authorization, the execution entity can obtain historical behavioral information generated by the user while using the application by accessing the application's API (Application Programming Interface). For another example, with some social networking software, the execution entity can obtain user behavior information by adding friends and obtaining information posted by users through the social networking software. This user behavior information can include the time, location, content, and other aspects of the information posted by the user. In some scenarios, users can also send their own information directly to the execution entity via the terminal device as historical user information. For example, users can send their behavior information on clothing, food, housing, transportation, entertainment, and other scenarios to the execution entity. For example, a user eats eggs, milk and bread for breakfast one day. At this time, the user can send information such as the breakfast time, dining location and type of food eaten to the execution entity through the terminal device used (for example, a mobile phone).

[0043] Step 202: Establish a user behavior information generation model based on historical user information.

[0044] In this embodiment, the execution entity can establish a behavior information generation model for the user based on the historical user information obtained in step 201. The above-mentioned behavior information generation model can be used to generate the user's behavior information based on the scene information of the scene in which the user is located. The above-mentioned behavior information generation model can be used to characterize the corresponding relationship between scene information and behavior information. The above-mentioned behavior information generation model can be a model obtained by various means. For example, a model obtained by training based on a machine learning algorithm. Here, the above-mentioned behavior information generation model may include an information input side and an information output side, wherein the information input side is used to input scene information, and the information output side is used to output behavior information.

[0045] In some optional implementations of this embodiment, the above-mentioned behavior information generation model may include at least one sub-model. Each of the at least one sub-model may be used to generate predetermined category behavior information. In practice, user behavior may be divided into a variety of predetermined categories, such as learning, work, entertainment, sports, socializing, dining, and so on. A corresponding sub-model may be established for each predetermined category. Step 202 may be specifically performed as follows:

[0046] Based on a machine learning algorithm, a sub-model in at least one sub-model is trained using the user's basic information, historical scene information, and historical behavior information corresponding to the historical scene information to obtain a behavior information generation model for the user.

[0047] In this implementation, the execution entity can train each of the at least one sub-model based on a machine learning algorithm using the user's basic information, historical scene information, and historical behavior information corresponding to the historical scene information, thereby obtaining a behavior information generation model for the above-mentioned user.

[0048] In practice, the context information used for training different sub-models can vary. The machine learning algorithms used for training different sub-models can be the same or different. For example, for a particular sub-model, the context information used for training that sub-model can be determined based on the predetermined category of behavior corresponding to the generated behavior information.

[0049] In some optional implementations of this embodiment, step 202 may be performed as follows:

[0050] First, adjustment data input by the user for historical user information is received.

[0051] In this implementation, the execution entity can receive adjustment data entered by the user regarding historical user information, where the adjustment data can be used to adjust the historical user information. For example, the user can adjust all or part of the historical user information as needed. For example, if the historical user information includes the information "Failed the postgraduate entrance examination in January 2012," and the user wants to know what would have happened if they had passed the examination, the user can adjust the information to "Succeeded the postgraduate entrance examination in January 2012 and was admitted to XX University, majoring in XX."

[0052] Then, the adjusted historical user information is used to build a user behavior information generation model.

[0053] In this implementation, the execution entity can use the adjusted historical user information to establish a behavior information generation model for the user. In this embodiment, the user can adjust the historical user information used to establish the behavior information generation model by inputting adjustment data, so that the established behavior information generation model better meets the user's needs.

[0054] Step 203: Generate a model based on the scene information and behavior information of the user's scene to generate behavior information.

[0055] In this embodiment, the execution subject can generate the user's behavior information based on the scene information of the scene in which the user is currently located and the behavior information generation model. As an example, the execution subject can input the scene information of the scene in which the user is currently located from the information input side of the behavior information generation model, and can obtain the behavior information on the information output side of the behavior information generation model. In some application scenarios, even if the user is incapable of behavior (for example, dead, comatose, etc.), the execution subject can generate behavior information based on the scene information of the current scene (for example, time, place, weather, etc.). In this scenario, some scene information can be determined based on the user's historical information. For example, the location can be the location where the user was last located, or the location where the user has stayed the longest before (for example, residence).

[0056] In some optional implementations of this embodiment, step 203 may specifically include the following:

[0057] First, associated behavior information generated by at least one associated behavior information generation model is received.

[0058] In this implementation, the execution entity can receive associated behavior information generated by at least one associated behavior information generation model. The at least one associated behavior information generation model is established based on user information of at least one associated user associated with the user. Here, an associated user can refer to a user with whom the user has a real-world association, such as the user's parents, spouse, children, siblings, colleagues, friends, neighbors, and so on. In practice, a behavior information generation model can be established for each associated user as an associated behavior information generation model. This associated behavior information generation model can be established based on the associated user's historical user information. Furthermore, for each associated behavior information generation model, an associated behavior information generation model can be established for that associated behavior information generation model. In this way, a large number of behavior information generation models can be obtained, and these large numbers of behavior information generation models can form a virtual community. Each behavior information generation model in the virtual community can be considered a corresponding person in the real world. Behavior information generation models in the virtual community can receive and send information to each other, just like communication between two people in the real world.

[0059] Then, the received associated behavior information is used as scene information, and the behavior information is generated based on the behavior information generation model.

[0060] In this implementation, the execution entity can use the received associated behavior information as scene information and generate behavior information based on the behavior information generation model. It is understood that the scene information here can include other information such as time, location, and weather in addition to the associated behavior information.

[0061] In some optional implementations of this embodiment, the above method for generating information may also include: Figure 2 The following steps are not shown:

[0062] Step S1: Send preset specific scene information to the user, and receive feedback behavior information input by the user in response to the specific scene information.

[0063] In this implementation, the execution entity can send preset specific scenario information to the terminal device used by the user. Here, the above-mentioned specific scenario information can be set according to actual needs. For example, the above-mentioned specific scenario information can be used to describe the dining scene, which may include time, place, optional dishes, people dining together, etc. After the user receives the specific scenario information, he can input feedback behavior information for the specific scenario information, such as which dishes to order, whether he will pay the bill, etc. Since the feedback behavior information is the behavior information entered by the user himself, it can be used as the user's real behavior information.

[0064] Step S2: Obtain generated behavior information generated by the behavior information generation model for specific scenario information.

[0065] In this implementation, the execution entity may also input the specific scenario information into the behavior information generation model from the information input side, thereby obtaining generated behavior information generated by the behavior information generation model for the specific scenario information. Since the generated behavior information is generated by the behavior information generation model, it may be different from the feedback behavior information.

[0066] Step S3: In response to determining that the feedback behavior information does not match the generated behavior information, a behavior information generation model is trained based on the specific scenario information and the feedback behavior information.

[0067] In this implementation, the execution entity may match the feedback behavior information received in step S1 with the generated behavior information obtained in step S2, thereby determining whether the feedback behavior information and the generated behavior information match. As an example, the matching of the feedback behavior information and the generated behavior information may mean that the feedback behavior information is the same as the generated behavior information. If the feedback behavior information matches the generated behavior information, it indicates that the generated behavior information generated by the above-mentioned behavior information generation model for the above-mentioned specific scenario information is correct. If the feedback behavior information does not match the generated behavior information, it indicates that the generated behavior information generated by the above-mentioned behavior information generation model for the above-mentioned specific scenario information is incorrect. At this point, it is necessary to train the behavior information generation model based on the specific scenario information and the feedback behavior information. As an example, the execution entity may use the specific scenario information and the feedback behavior information as sample data to train the behavior information generation model. Through this implementation, the execution entity may obtain sample data for training the behavior information generation model, thereby making the behavior information generated by the behavior information generation model more accurate.

[0068] In some optional implementations of this embodiment, the above method for generating information may also include: Figure 2 The following steps are not shown:

[0069] Step 1: Receive a viewing request sent by a user.

[0070] In this implementation, the execution entity can receive a viewing request sent by the user through the terminal device used. The above-mentioned viewing request can be used to view the behavior information generated by the behavior information generation model within a preset time period. In practice, the behavior information generation model can generate behavior information in real time based on the scene information of the user's scene. In this way, the user can send a viewing request to the execution entity at any time to view the behavior information generated by the behavior information generation model within a preset time period. As an example, the above-mentioned preset time period can be a time period selected by the user. For example, if the user wants to view the behavior information generated by the behavior information generation model on the previous day, the user can select the previous day as the preset time period.

[0071] Step 2: Based on the viewing request, the behavior information generated by the behavior information generation model within a preset time period is presented to the user.

[0072] In this implementation, the execution entity may send the behavior information generated by the behavior information generation model within the above-mentioned preset time period to the terminal device used by the user according to the above-mentioned viewing request, so that the terminal device used by the user can present it to the user.

[0073] In some optional implementations, the above method for generating information may further include: Figure 2 The following steps are not shown:

[0074] Step three: receiving modification information sent by the user in response to the presented behavior information.

[0075] In this implementation, after the behavior information generated by the behavior information generation model within a preset time period is presented to the user in step 2, the user can view the presented behavior information. If the user believes that the presented behavior information does not conform to his or her actual behavior, the user can send modification information to the execution entity through the terminal device. The modification information can be used to modify the presented behavior information. In this way, the execution entity can receive the modification information sent by the user for the presented behavior information. As an example, the above-mentioned modification information can be text information, voice information, picture information, etc. When the above-mentioned modification information is voice information or picture information, the above-mentioned execution entity can identify the voice information or picture information to obtain the text corresponding to the voice information or picture information.

[0076] Step 4: Based on the modification information, the behavior information generation model is trained.

[0077] In this implementation, the execution entity can train the behavior information generation model based on the modification information received in step 3. Specifically, the execution entity can use the scenario information and modification information corresponding to the presented behavior information as sample data to train the behavior information generation model. Through this implementation, the execution entity can use the modification information sent by the user to update the behavior information generation model, thereby making the behavior information generated by the behavior information generation model more accurate.

[0078] In some optional implementations of this embodiment, the above method for generating information may also include: Figure 2 The following are not shown:

[0079] First, behavior information generated by multiple models including a behavior information generation model is obtained.

[0080] In this implementation, the execution subject can obtain the behavior information generated by multiple models including the above-mentioned behavior information generation model. As an example, the above-mentioned multiple models can be established based on the historical user information of multiple people in the real world, and these multiple people may or may not have a relationship in the real world. Multiple people can refer to a group. As an example, all people on the earth can be regarded as a group, and the behavior information generated by each corresponding model in this group can be obtained. As an example, information exchange can be carried out between multiple models. For example, the behavior information generated by a certain model can be sent to another model as the scene information of the other model. The virtual space formed between multiple models can be regarded as a space parallel to the real world.

[0081] Then, the obtained behavior information is statistically analyzed, and the statistical analysis results are displayed.

[0082] In this implementation, the obtained behavior information can be statistically analyzed and the statistical analysis results can be displayed. Here, the statistical analysis can be various forms of statistical analysis, for example, statistical analysis of a certain behavior, such as analyzing the number of models that generate the behavior, the time distribution of the behavior, etc.

[0083] Continue to see Figure 3 , Figure 3 FIG. 1 is a schematic diagram of an application scenario of the method for generating information according to this embodiment. Figure 3 In an application scenario, server 301 can obtain historical user information from at least one terminal device 302 used by the user. This historical user information may include basic user information, historical scenario information, and historical behavior information corresponding to the historical scenario information. Server 301 can then establish a behavior information generation model for the user based on the historical user information. The behavior information generation model is used to generate user behavior information based on scenario information of the user's scenario. Finally, server 301 can generate behavior information based on the scenario information of the user's scenario and the behavior information generation model.

[0084] The method provided by the above-mentioned embodiment of the present disclosure can establish a behavior information generation model based on the user's historical user information, and generate the user's behavior information based on the scene information of the user and the behavior information generation model, thereby realizing the generation of user behavior information.

[0085] Further references Figure 4 As an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for generating information. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0086] like Figure 4 As shown, the apparatus 400 for generating information in this embodiment includes: an acquisition unit 401, an establishment unit 402, and a generation unit 403. The acquisition unit 401 is configured to acquire historical user information from at least one terminal device used by a user, wherein the historical user information includes basic user information, historical scenario information, and historical behavior information corresponding to the historical scenario information; the establishment unit 402 is configured to establish a behavior information generation model for the user based on the historical user information, wherein the behavior information generation model is used to generate the behavior information of the user based on scenario information of the scenario in which the user is located; and the generation unit 403 is configured to generate behavior information based on the scenario information of the scenario in which the user is located and the behavior information generation model.

[0087] In this embodiment, the specific processing of the acquisition unit 401, the establishment unit 402 and the generation unit 403 of the device 400 for generating information and the technical effects thereof can be referred to respectively. Figure 2 The relevant descriptions of step 201, step 202 and step 203 in the corresponding embodiment are not repeated here.

[0088] In some optional implementations of this embodiment, the above-mentioned device 400 also includes: a receiving unit (not shown in the figure), configured to send preset specific scenario information to the above-mentioned user, and receive feedback behavior information input by the above-mentioned user for the above-mentioned specific scenario information; a feedback unit (not shown in the figure), configured to obtain the generated behavior information generated by the above-mentioned behavior information generation model for the above-mentioned specific scenario information; a first training unit (not shown in the figure), configured to train the above-mentioned behavior information generation model based on the above-mentioned specific scenario information and the above-mentioned feedback behavior information in response to determining that the above-mentioned feedback behavior information does not match the above-mentioned generated behavior information.

[0089] In some optional implementations of this embodiment, the above-mentioned device 400 also includes: a request receiving unit (not shown in the figure), configured to receive a viewing request sent by the above-mentioned user, wherein the above-mentioned viewing request is used to view the behavior information generated by the above-mentioned behavior information generation model within a preset time period; a presentation unit (not shown in the figure), configured to present the behavior information generated by the above-mentioned behavior information generation model within the preset time period to the above-mentioned user according to the above-mentioned viewing request.

[0090] In some optional implementations of this embodiment, the above-mentioned device 400 also includes: an information receiving unit (not shown in the figure), configured to receive modification information sent by the above-mentioned user regarding the presented behavior information, wherein the above-mentioned modification information is used to modify the presented behavior information; a second training unit (not shown in the figure), configured to train the above-mentioned behavior information generation model based on the above-mentioned modification information.

[0091] In some optional implementations of this embodiment, the above-mentioned generation unit 403 is further configured to: receive associated behavior information generated by at least one associated behavior information generation model, wherein the above-mentioned at least one associated behavior information generation model is established based on user information of at least one associated user who has an associated relationship with the above-mentioned user; use the received associated behavior information as scene information, and generate behavior information based on the above-mentioned behavior information generation model.

[0092] In some optional implementations of this embodiment, the above-mentioned behavior information generation model includes at least one sub-model, wherein the sub-model in the above-mentioned at least one sub-model is used to generate predetermined category behavior information; and the above-mentioned establishment unit 402 is further configured to: based on a machine learning algorithm, use the above-mentioned user basic information, historical scene information and historical behavior information corresponding to the historical scene information to train the sub-model in the above-mentioned at least one sub-model to obtain a behavior information generation model for the above-mentioned user.

[0093] In some optional implementations of this embodiment, the establishment unit 402 is further configured to: receive adjustment data input by the user for the historical user information, wherein the adjustment data is used to adjust the historical user information; and use the adjusted historical user information to establish a behavior information generation model for the user.

[0094] In some optional implementations of this embodiment, the above method also includes: an information acquisition unit (not shown in the figure), configured to acquire behavior information generated by multiple models including the above-mentioned behavior information generation model; a statistical unit (not shown in the figure), configured to perform statistical analysis on the acquired behavior information, and display the statistical analysis results.

[0095] Reference below Figure 5 , which shows a structural diagram of a computer system 500 suitable for implementing a server of an embodiment of the present disclosure. Figure 5 The server shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0096] like Figure 5 As shown, the computer system 500 includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage unit 508 into a random access memory (RAM) 503. Various programs and data required for the operation of the system 500 are also stored in the RAM 503. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0097] The following components are connected to the I / O interface 505: an input section 506 including a keyboard, a mouse, and the like; an output section 507 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 508 including a hard disk; and a communication section 509 including a network interface card such as a LAN card or a modem. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 510 as needed, so that computer programs read therefrom can be installed into the storage section 508 as needed.

[0098] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 509, and / or installed from a removable medium 511. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the method of the present disclosure are performed.

[0099] It should be noted that the computer-readable medium described in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0100] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0102] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. The units described may also be provided in a processor. For example, they may be described as: a processor including an acquisition unit, an establishment unit, and a generation unit. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the acquisition unit may also be described as a "unit for acquiring historical user information from at least one terminal device used by a user."

[0103] As another aspect, the present disclosure further provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the device, the device: obtains historical user information from at least one terminal device used by the user, wherein the above historical user information includes basic user information, historical scenario information, and historical behavior information corresponding to the historical scenario information; establishes a behavior information generation model for the above user based on the above historical user information, wherein the above behavior information generation model is used to generate the behavior information of the above user based on the scenario information of the scenario in which the above user is located; generates behavior information based on the scenario information of the scenario in which the above user is located and the above behavior information generation model.

[0104] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

Claims

1. A method for generating information, comprising: Acquiring historical user information from at least one terminal device used by the user, wherein the historical user information includes basic user information, historical scenario information, and historical behavior information corresponding to the historical scenario information; Based on the historical user information, a behavior information generation model for the user is established, wherein the behavior information generation model is used to generate the user's behavior information based on the scenario information of the scenario in which the user is located. The user's behavior information includes multiple categories such as learning, work, entertainment, sports, socializing, and dining. Multiple behavior information generation models form a virtual community, and the behavior information generation models in the virtual community can receive or send information to each other; Behavior information is generated based on scenario information of the scenario in which the user is located and the behavior information generation model, wherein the behavior information generation models in the virtual community receive or send information to each other, including: receiving associated behavior information generated by at least one associated behavior information generation model, wherein the at least one associated behavior information generation model is established based on user information of at least one associated user who has an associated relationship with the user; using the received associated behavior information as scenario information, and generating behavior information based on the behavior information generation model.

2. The method according to claim 1, wherein The method further comprises: Sending preset specific scenario information to the user, and receiving feedback behavior information input by the user in response to the specific scenario information; Obtaining generated behavior information generated by the behavior information generation model for the specific scenario information; In response to determining that the feedback behavior information does not match the generated behavior information, the behavior information generation model is trained based on the specific scenario information and the feedback behavior information.

3. The method according to claim 1, wherein The method further comprises: receiving a viewing request sent by the user, wherein the viewing request is for viewing behavior information generated by the behavior information generation model within a preset time period; According to the viewing request, the behavior information generated by the behavior information generation model within a preset time period is presented to the user.

4. The method according to claim 3, wherein: The method further comprises: receiving modification information sent by the user with respect to the presented behavior information, wherein the modification information is used to modify the presented behavior information; The behavior information generation model is trained based on the modification information.

5. The method according to claim 1, wherein The behavior information generation model includes at least one sub-model, wherein a sub-model in the at least one sub-model is used to generate predetermined category behavior information; and The step of establishing a behavior information generation model for the user based on the historical user information includes: Based on a machine learning algorithm, the user basic information, historical scene information, and historical behavior information corresponding to the historical scene information are used to train a sub-model in the at least one sub-model to obtain a behavior information generation model for the user.

6. The method according to claim 1, wherein The step of establishing a behavior information generation model for the user based on the historical user information includes: receiving adjustment data input by the user for the historical user information, wherein the adjustment data is used to adjust the historical user information; Using the adjusted historical user information, a behavioral information generation model for the user is established.

7. The method according to claim 1, wherein The method further comprises: Acquiring behavior information generated by multiple models including the behavior information generation model; Perform statistical analysis on the acquired behavioral information and display the statistical analysis results.

8. A device for generating information, comprising: an acquiring unit configured to acquire historical user information from at least one terminal device used by the user, wherein the historical user information includes basic user information, historical scenario information, and historical behavior information corresponding to the historical scenario information; an establishing unit configured to establish a behavior information generation model for the user based on the historical user information, wherein the behavior information generation model is used to generate the user's behavior information based on scenario information of a scenario in which the user is located, and the user's behavior information includes multiple categories such as learning, work, entertainment, sports, socializing, and dining. A plurality of the behavior information generation models form a virtual community, and the behavior information generation models in the virtual community can receive or send information to each other; A generation unit is configured to generate behavior information based on scenario information of a scenario in which the user is located and the behavior information generation model, wherein the behavior information generation models in the virtual community receive or send information to each other, including: receiving associated behavior information generated by at least one associated behavior information generation model, wherein the at least one associated behavior information generation model is established based on user information of at least one associated user who has an associated relationship with the user; using the received associated behavior information as scenario information, and generating behavior information based on the behavior information generation model.

9. The device according to claim 8, wherein The device further comprises: a receiving unit configured to send preset specific scenario information to the user, and receive feedback behavior information input by the user in response to the specific scenario information; A feedback unit is configured to obtain generated behavior information generated by the behavior information generation model for the specific scenario information; The first training unit is configured to, in response to determining that the feedback behavior information does not match the generated behavior information, train the behavior information generation model based on the specific scenario information and the feedback behavior information.

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