CN114021016A

CN114021016A - Data recommendation method, device, equipment and storage medium

Info

Publication number: CN114021016A
Application number: CN202111307983.XA
Authority: CN
Inventors: 王旭; 郑伟; 朱静茹
Original assignee: Beijing Qury Technology Co ltd; Shandong Kurui Technology Co ltd
Current assignee: Beijing Qury Technology Co ltd; Shandong Kurui Technology Co ltd
Priority date: 2021-11-05
Filing date: 2021-11-05
Publication date: 2022-02-08

Abstract

The disclosure provides a data recommendation method, device, equipment and storage medium. The data recommendation method comprises the following steps: acquiring historical use data of a target user in a plurality of terminal devices; determining a user representation of a target user based on the historical usage data; acquiring recommended data matched with the user portrait of the target user in cloud data; and displaying the recommended data to the target user. Because the user portrait is constructed based on historical use data of a plurality of terminal devices, the content of the user portrait is rich and comprehensive, so that the recommendation data determined based on the user portrait is more matched with the requirements of target users, and the user experience is improved.

Description

Data recommendation method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of internet technologies, and in particular, to a data recommendation method, apparatus, device, and storage medium.

Background

The intelligent home is used for connecting the life of the user with the Internet, and the user can provide data search and recommendation services through the intelligent home. However, when the current smart home provides data search and recommendation services for users, recommendation data is determined based on historical usage data of a single terminal device, and user data between devices cannot be shared, so that the recommendation data is not accurate enough.

Disclosure of Invention

In order to solve the technical problem, the present disclosure provides a data recommendation method, apparatus, device and storage medium.

In a first aspect, an embodiment of the present disclosure provides a data recommendation method, including:

acquiring historical use data of a target user in a plurality of terminal devices;

determining a user representation of a target user based on the historical usage data;

acquiring recommended data matched with the user portrait of the target user in cloud data;

and displaying the recommended data to the target user.

Optionally, before obtaining recommendation data in cloud data that matches the user profile of the target user, the method further includes:

determining similar users in the cloud users based on the user portrait of the target user;

the acquiring of the recommendation data matched with the user portrait of the target user in the cloud data comprises:

and acquiring data liked by the similar users in the cloud data to serve as the recommended data matched with the user portrait of the target user.

Optionally, the determining similar users among cloud-end users based on the user representation of the target user includes:

and determining the similar users in the cloud end users based on the user portrait of the target user and the user portrait of the cloud end users by adopting a pre-trained similarity model.

Optionally, the user representation of the target user includes favorite features of the target user;

and acquiring the recommendation data matched with the favorite features of the target user in the cloud data.

Optionally, the recommendation data comprises a feature tag; the user representation further includes features other than the preferred features;

the presenting the recommended data to the target user includes:

based on the feature tag and the other features of the recommended data, screening and/or sorting the recommended data to obtain processed data;

and displaying the processed data to the target user.

Optionally, the determining a user representation of a target user based on the historical usage data comprises:

according to the corresponding relation between the scenes and the terminal equipment, dividing the historical use data into different data groups, wherein each data group comprises the historical use data of all the terminal equipment in a single scene;

and determining the user image of the target user in each scene according to the data group corresponding to each scene.

determining the current scene of the target user;

acquiring recommended data which is matched with a user portrait corresponding to the target user in the current scene from the cloud data;

the presenting the recommended data to the target user includes: and displaying the recommended data to the target user by adopting the terminal equipment corresponding to the current scene.

In a second aspect, an embodiment of the present disclosure provides a data recommendation apparatus, including:

a historical usage data acquisition unit for acquiring historical usage data of a target user in a plurality of terminal devices;

a portrait determination unit to determine a user portrait of a target user based on the historical usage data;

the recommendation data acquisition unit is used for acquiring recommendation data matched with the user portrait of the target user in the cloud data;

and the display unit is used for displaying the recommended data to the target user.

In a third aspect, the present disclosure provides an electronic device, comprising: a processor for executing a computer program stored in a memory, the computer program, when executed by the processor, performing the steps of the method of the first aspect.

In a fourth aspect, the present disclosure provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of the first aspect.

According to the technical scheme, historical use data of a target user in a plurality of terminal devices are obtained, a user portrait of the target user is determined based on the historical use data of the terminal devices, recommendation data matched with the user portrait is determined based on the user portrait, and the recommendation data are displayed to the target user. Because the user portrait is constructed based on historical use data of a plurality of terminal devices, the content of the user portrait is rich and comprehensive, so that the recommendation data determined based on the user portrait is more matched with the requirements of target users, and the user experience is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.

In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.

Fig. 1 is a schematic flowchart of a data recommendation method according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of another data recommendation method provided by an embodiment of the present disclosure;

FIG. 3 is a flow chart of yet another data recommendation method provided by an embodiment of the present disclosure;

fig. 4 is a flowchart of another data recommendation method provided by an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of a data recommendation device provided in the present disclosure;

fig. 6 is a schematic structural diagram of an electronic device provided in 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 present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather 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 understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.

Fig. 1 is a schematic flow chart of a data recommendation method provided in an embodiment of the present disclosure, and as shown in fig. 1, the data recommendation method provided in the embodiment of the present disclosure includes steps S101 to S104.

It should be noted that the data recommendation method provided by the embodiment of the present disclosure may be executed by a terminal device; the following describes steps of a data recommendation method according to an embodiment of the present disclosure, with a first terminal device as an execution subject.

Step S101: historical use data of a target user in a plurality of terminal devices is acquired.

The target user is a user who uses at least one of the plurality of terminal devices, and the terminal device may determine the target user based on information such as account login information of the user, a voiceprint of the user, or an image of the user.

In the embodiment of the present disclosure, the plurality of terminal devices may be at least two of a mobile phone, a tablet, a smart television, a smart watch, a smart refrigerator, a stereo, an in-vehicle central control, a camera, and the like.

The historical usage data of the terminal device is historical data generated by the target user using the terminal device. The historical usage data may be at least one of content data browsed by the user, voice interaction data of the user, image interaction data of the user, scene data of the user, time data of the user interaction, and sensing data of the device.

It should be noted that the historical usage data in the terminal device may be usage data generated by one application in the terminal device, or may be usage data generated by different applications in the terminal device.

In some embodiments of the present disclosure, historical usage data for a plurality of terminal devices may be stored in a local memory of each terminal device. When the first terminal equipment is used by a user, the first terminal equipment sends a historical use data acquisition request to each terminal equipment to obtain the historical use data of each terminal equipment.

In other embodiments of the present disclosure, historical usage data for a plurality of terminal devices may be stored in a particular memory device. When the first terminal equipment is used by a user, the first terminal equipment sends a historical use data acquisition request to the storage equipment to obtain the historical use data of each terminal equipment.

Step S102: based on the historical usage data, a user representation of the target user is determined.

The first terminal device determines a user representation of the target user based on the historical usage data after acquiring the historical usage data.

For example, the historical usage data of the target user a in the terminal device 1 is d1, the historical usage data of the target user a in the terminal device 2 is d2, and the third data of the target user in the terminal device 3 is d 3. After obtaining the historical usage data d1-d3 of the target user in the three terminal devices, the first terminal device may perform analysis based on the historical usage data d1-d3 to obtain an analysis feature value, and construct a user representation of the target user based on the analysis feature value.

The user profile is determined based on historical usage data of the plurality of terminal devices, which reflects information such as behavioral characteristics, preferences, and habits of the target user among the different terminal devices, so that much information is available in the user profile.

In some embodiments of the present disclosure, determining the user representation of the target user from the historical usage data in step S102 may specifically include steps S1021-S1022.

Step S1021: and determining the behavior sequence of the target user according to the time of generation of all data in the historical use data.

In the embodiment of the present disclosure, each historical usage data may have a corresponding generation time stamp, and the generation time stamp is used to indicate the generation time of the historical usage data. The terminal device may arrange all the historical usage data according to the generation time stamps thereof to obtain a data sequence. Since each generation of historical usage data corresponds to a behavior of the target user, the sequence of behaviors of the target user may be determined from the data sequence.

For example, when 7:00 am, the mobile phone alarm of the target user rings, that is, the generation timestamp corresponding to the mobile phone alarm data is 7: 00; at noon of 12:00, the target user takes out through the APP of the mobile phone, namely, the generation timestamp corresponding to the mobile phone service content data can be 12: 00; at 20:00 pm, the target user watches the movie through the smart television, i.e., the generation timestamp corresponding to the television content data may be 20: 00. The target user behavior corresponding to the mobile phone alarm data can be getting up, the user behavior corresponding to the mobile phone service content data can be eating, and the user behavior corresponding to the television content data can be leisure entertainment, so that the behavior sequence of the target user can be obtained as follows: getting up, eating, leisure and entertainment.

Step S1022: a user representation is determined based on the sequence of behaviors.

In the embodiment of the disclosure, the behavior sequence may be divided by using a specific sampling period to obtain a user portrait of a target user within the sampling period. Because the portrait is constructed based on the behavior sequence in the later sampling period, the user portrait can comprehensively and accurately reflect the behavior characteristics of the user in the sampling period, and the accuracy of the user portrait is improved.

Step S103: and acquiring recommended data matched with the user portrait of the target user in the cloud data.

Step S104: and displaying the recommended data to the target user.

In the embodiment of the disclosure, after the first terminal device determines the user portrait of the target user, the user portrait of the target user is sent to the cloud server, so that the cloud server determines the recommendation data matched with the user portrait of the target user based on the user portrait of the target user, and returns the recommendation data to the first terminal device.

After obtaining the recommendation data, the first terminal device may use a specific output device to display the recommendation data to the target user. For example, the recommended data is picture data, and the first terminal device displays and outputs the recommended data by using a display screen. For another example, the recommended data is audio data, and the first terminal device outputs the recommended data using a speaker.

Based on the foregoing steps, in the embodiment of the present disclosure, historical usage data of a target user in a plurality of terminal devices is obtained, a user portrait of the target user is determined based on the historical usage data of the plurality of terminal devices, recommendation data matched with the user portrait is determined based on the user portrait, and the recommendation data is displayed to the target user. Because the user portrait is constructed based on historical use data of a plurality of terminal devices, the content of the user portrait is rich and comprehensive, so that the recommendation data determined based on the user portrait is more matched with the requirements of target users, and the user experience is improved.

Fig. 2 is a flowchart of another data recommendation method provided in an embodiment of the present disclosure. As shown in FIG. 2, in some further embodiments of the present disclosure, data recommendation method steps S201-S205.

Step S201: historical use data of a target user in a plurality of terminal devices is acquired.

Step S202: based on the historical usage data, a user representation of the target user is determined.

Steps S201 to S202 are the same as those in the previous embodiment, and reference may be made to the previous embodiment specifically, and the description is not repeated here.

Step S203: based on the user representation of the target user, similar users in the cloud end users are determined.

In some embodiments of the disclosure, after determining the user portrait of the target user, the first terminal device may send an acquisition request of the cloud user portrait to the cloud server, so as to trigger the cloud server to return the plurality of cloud user portraits to the first terminal device.

After receiving the user portrait of the cloud user, the first terminal device may compare the user portraits of the plurality of cloud users with the user portrait of the target user, and determine a similar user of the cloud user to the target user. Specifically, in some embodiments of the present disclosure, the first terminal device may process the user portrait of the target user and the portraits of the multiple cloud users by using a pre-trained similarity model, so as to determine similar users.

Step S204: and acquiring data similar to the user's favorite data in the cloud data as recommended data matched with the user portrait of the target user.

After the first terminal device determines the similar user, the identifier of the similar user is sent to the cloud server, so that the cloud server obtains data liked by the similar user based on the identifier of the similar user and returns the data liked by the similar user to the first terminal device, and the first terminal device takes the data liked by the similar user as recommended data matched with the user portrait of the target user.

For example, the historical usage data for target user a includes movies starring at actor X, movie tickets and comedy tickets to purchase movies starring at actor X. The analysis results in the user representation of user a including a like to watch a movie, a like to a drama, and a like to actor X. The cloud user B also has the image characteristics of the user A, and the cloud user pays attention to a lot of happy works, but the user A does not pay attention to the happy works. By adopting the similarity model, the similarity of the cloud user B of the user A can be determined, so that the happy fried dough twist concerned by the user B can be recommended to the target user A as recommendation data.

Step S205: and displaying the recommended data to the target user.

By adopting the method provided by the embodiment of the disclosure, the first terminal device does not send the user portrait of the target user to the cloud terminal device after determining the user portrait of the target user, but requests the user portrait of the cloud terminal user from the cloud terminal server, and determines the similar user based on the user portrait of the target user and the user portrait of the cloud terminal user locally, so that data liked by the similar user in the cloud terminal data can be obtained as recommended data. By adopting the method provided by the embodiment of the disclosure, the first terminal device can avoid the specific information of the user portrait of the target user from being leaked, and the privacy of the user privacy is improved.

In other implementations of the present disclosure, the steps S201 to S204 may also be executed by the cloud server, and after the cloud server determines the recommendation data, the recommendation data is issued to the first terminal device, so that the first terminal device executes the step S205. In other embodiments of the present disclosure, the first terminal device may execute the aforementioned steps S201 to S202 and S205, and the cloud server may execute the steps S203 and S204.

Fig. 3 is a flowchart of still another data recommendation method provided by an embodiment of the present disclosure. As shown in FIG. 3, in still other embodiments of the present disclosure, a data recommendation method includes steps S301-S304.

Step S301: historical use data of a target user in a plurality of terminal devices is acquired.

Step S302: based on the historical usage data, a user profile of the target user is determined, the user profile of the target user including favorite features of the target user.

Steps S301 to S302 are the same as those in the previous embodiment, and reference may be made to the previous embodiment specifically, and the description is not repeated here. It should be noted, however, that in embodiments of the present disclosure, the user representation of the target user includes favorite features of the target user.

Step S303: and acquiring recommendation data matched with the favorite features of the target user from the cloud data.

For example, the user's smart television watched the movie "former girl friend X", and played the trailer of the movie twice. Determining the user representation using the user representation construction model analyzes the user's favorite features including listening to songs similar to the aforementioned trailer. When a target user listens to music by using a first electronic device such as a smart sound, the first electronic device determines that the recommended data includes "former girlfriend X" and similar songs based on the favorite features of the target user, and displays the data of the songs to the target user.

Step S304: and displaying the recommended data to the target user.

In the embodiment of the disclosure, after determining the user portrait of the target user, the first terminal device does not send all contents of the user portrait to the cloud device, but only sends the favorite features of the target user to the cloud device, so that the cloud device determines the matched recommended data only based on the favorite features of the user.

Because the first terminal device only sends the favorite feature data of the user to the cloud server, and does not send all the contents of the user portrait to the cloud server, the method provided by the embodiment of the disclosure can avoid the leakage of other information except the favorite feature in the user portrait of the target user, and ensure the confidentiality of the other information of the target user.

In some embodiments of the present disclosure, the recommendation data includes a feature tag for characterizing an attribute feature of the recommendation data. The user representation may include other features besides the preferred features described above. Correspondingly, step S304 may include steps S3041-S3042.

Step S3041: and screening and/or sorting the recommended data based on the feature tags and other features of the recommended data to obtain the processed data.

Step S3042: and displaying the processed data to a target user.

By adopting the steps S3041 and S3042 provided by the embodiment of the present disclosure, the recommended data is screened or sorted by comparing the feature tag of the recommended data with other features except the favorite feature in the user portrait, so that the processed data more conforming to the target user can be obtained, and then the content recommended to the user more conforms to the requirement of the target user, thereby improving the user experience.

Fig. 4 is a flowchart of another data recommendation method provided in an embodiment of the present disclosure. As shown in fig. 4, in some embodiments of the present disclosure, a data recommendation method includes steps S401-S406.

Step S401: historical use data of a target user in a plurality of terminal devices is acquired.

Step S402: and dividing the historical use data into different data groups according to the corresponding relation between the scene and the equipment.

In the embodiment of the present disclosure, each data group includes historical usage data of all terminal devices in a single scene.

Multiple terminal devices can be used in the same scene, and accordingly the corresponding relation between the scene and the terminal devices can be obtained. For example, the terminal device 1 is a television, the terminal device 2 is a stereo, and the terminal device 3 is a refrigerator. In a home theater scene, a television and a sound are used, and obviously, the television and the sound have a corresponding relationship with the home theater scene.

In the specific embodiment of the present disclosure, there are multiple scenarios for using the terminal device, and each scenario corresponds to at least one device. According to the above correspondence, the historical usage data of all the terminal devices can be grouped, and the historical usage data of all the devices corresponding to the same scene can be divided into one data group, so that a plurality of data groups can be obtained based on a plurality of scenes.

In an embodiment of the present disclosure, the corresponding relationship may be determined according to content correlation and/or time correlation among a plurality of historical usage data.

For example, in a home theater scene, a picture displayed by a television and an audio output audio have a correlation in content, and thus the correspondence between the television and the audio and the home theater scene can be determined. In the home theater scene, the time when the television displays the picture has a correlation with the time when the audio is output at the sound, and therefore, the correspondence of the television and the sound with the home theater scene can be determined. It should be noted that, in this embodiment, the correspondence between the determined scene and the device is exemplarily described only by taking the home theater scene as an example, and in other scenes, the correspondence between other scenes may be determined based on the content correlation and/or the time correlation between other historical usages

Step S403: and determining the user image of the target user in each scene according to the data group corresponding to each scene.

According to the data in the plurality of scenes and the data groups corresponding to the scenes, the user portrait of the target user in different scenes can be carved, so that the user portrait of the target user can be subdivided, the recommendation data under the corresponding scenes can be recommended according to the current scene of the user, the accuracy of the recommendation data is improved, and the user experience is further improved.

Step S404: and determining the current scene of the target user.

In the embodiment of the present disclosure, the determining of the current scene of the target user may be that the current scene of the user is obtained through a trigger operation of the user or an automatic detection of the terminal device.

Step S405: and acquiring recommended data which is matched with the user portrait corresponding to the current scene of the target user in the cloud data.

In the embodiment of the disclosure, after the scene where the target user is located is determined, the terminal device determines the corresponding user portrait based on the scene where the target user is located, and sends the user portrait of the scene where the target user is located to the cloud server, so that the cloud server recommends the recommendation data matched with the current scene.

Step S406: and displaying the recommended data to the target user by adopting the terminal equipment corresponding to the current scene.

The user portrait acquired in the embodiment of the present disclosure is a scenized user portrait, that is, different user portraits are depicted for different scenes, and the current scene of the user may be acquired through a trigger operation of the user or an automatic detection of the device. And recommending contents suitable for the current scene to the user based on the scene user portrait according to the current scene of the user. For example, when the user is currently in a home theater scene, the sound and the television recommend corresponding contents respectively, and the sound and the television respectively present the recommended contents to the user in a suitable presentation form.

In the embodiment, the recommendation data are acquired according to the current scene of the target user and the user picture, so that the corresponding recommendation data can be displayed to the user according to different scenes, the requirements of the user are refined based on the scenes, and the accuracy of data recommendation can be improved.

Fig. 5 is a schematic structural diagram of a data recommendation device according to the present disclosure, and as shown in fig. 5, the data recommendation device 500 includes a historical usage data acquisition unit 501, a portrait determination unit 502, a recommendation data acquisition unit 503, and a presentation unit 504.

The historical usage data acquisition unit 501 is configured to acquire historical usage data of a target user in a plurality of terminal devices. A representation determination unit 502 is used to determine a user representation of a target user based on historical usage data. The recommendation data obtaining unit 503 is configured to obtain recommendation data that matches the user portrait of the target user in the cloud data. The presentation unit 504 is used for presenting the recommendation data to the target user.

In some embodiments of the present disclosure, the data recommendation device 500 further comprises a similar user determination unit. The similar user determination unit is used for determining similar users in the cloud users based on the user portrait of the target user. Correspondingly, the recommendation data acquisition unit 503 includes a first recommendation data acquisition sub-unit. The first recommended data acquiring subunit is used for acquiring data liked by the similar user in the cloud data as recommended data matched with the user portrait of the target user.

In some embodiments of the present disclosure, the similar user determining unit determines the similar user in the cloud user based on the user portrait of the target user and the user portrait of the cloud user by using a pre-trained similarity model.

In some embodiments of the present disclosure, the user representation of the target user includes favorite features of the target user. Correspondingly, the recommendation data acquisition unit 503 includes a second recommendation data acquisition sub-unit. And the second recommendation data acquisition subunit acquires recommendation data matched with the favorite features of the target user in the cloud data.

In some embodiments of the present disclosure, the recommendation data includes a feature tag; the user representation may include features other than preferred features. Correspondingly, the presentation unit 504 includes a data processing subunit and a presentation subunit. And the output processing subunit is used for screening and/or sorting the recommended data based on the feature tags and other features of the recommended data to obtain processed data. And the display subunit displays the processed data to the target user.

In some embodiments of the present disclosure, portrait determination unit 502 includes a grouping subunit and a user portrait determination subunit. The grouping subunit is configured to divide the historical usage data into different data groups according to a correspondence between the scenes and the terminal devices, where each data group includes historical usage data of all terminal devices in a single scene. And the user portrait determining subunit is used for determining the user portrait of the target user in each scene according to the data group corresponding to each scene.

In some embodiments of the present disclosure, the data recommendation device 500 further includes a current scene determination unit. The current scene determining unit is used for determining the current scene of the target user. Correspondingly, the recommended data acquiring unit 503 acquires recommended data, which is matched with the user portrait corresponding to the target user in the current scene, in the cloud data. The presentation unit 504 presents the recommended data to the target user by using the terminal device corresponding to the current scene.

The apparatus of this embodiment may be configured to perform the steps of the method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.

The embodiment of the present disclosure further provides an electronic device, which includes a processor and a memory, where the memory stores a computer program, and when the computer program is executed by the processor, the method of any one of the above-mentioned fig. 1 to fig. 4 may be implemented.

Fig. 6 is a schematic structural diagram of an electronic device provided in some embodiments of the present disclosure. As shown in fig. 6, electronic device 600 may include a processing means (e.g., central processing unit, graphics processor, etc.) 601 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage means 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. While fig. 6 illustrates an electronic device 600 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.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.

It should be noted that the computer readable medium in the present disclosure can 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 the present 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 contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either 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.

The computer readable medium may be embodied in the electronic device; 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 historical use data of a target user in a plurality of terminal devices; determining a user representation of a target user based on the historical usage data; acquiring recommended data matched with the user portrait of the target user in cloud data; and displaying the recommended data to the target user.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to 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 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 the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.

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.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.

The embodiments of the present disclosure further provide a computer-readable storage medium, where a computer program is stored in the storage medium, and when the computer program is executed by a processor, the method of any one of the embodiments in fig. 1 to fig. 4 may be implemented, where the execution manner and the beneficial effects are similar, and are not described herein again.

The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for recommending data, comprising:

and displaying the recommended data to the target user.

2. The method of claim 1, wherein prior to obtaining recommendation data in cloud data that matches the user representation of the target user, the method further comprises:

3. The method of claim 1, wherein determining similar ones of cloud-based users based on the user representation of the target user comprises:

4. The method of claim 1, wherein the user representation of the target user includes favorite features of the target user;

5. The method of claim 4, wherein the recommendation data comprises a feature tag; the user representation further includes features other than the preferred features;

the presenting the recommended data to the target user includes:

and displaying the processed data to the target user.

6. The method of any of claims 1-5, wherein determining a user representation of a target user based on the historical usage data comprises:

7. The method of claim 6,

before obtaining recommendation data in cloud data that matches the user representation of the target user, the method further includes:

determining the current scene of the target user;

8. A data recommendation device, comprising:

9. An electronic device, comprising: a processor for executing a computer program stored in a memory, the computer program, when executed by the processor, implementing the steps of the method of any of claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.