CN111310049B - Information interaction method and related equipment - Google Patents
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- CN111310049B CN111310049B CN202010118840.3A CN202010118840A CN111310049B CN 111310049 B CN111310049 B CN 111310049B CN 202010118840 A CN202010118840 A CN 202010118840A CN 111310049 B CN111310049 B CN 111310049B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/70—Game security or game management aspects
- A63F13/79—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/50—Lighting effects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T19/00—Manipulating 3D models or images for computer graphics
- G06T19/006—Mixed reality
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The embodiment of the invention provides an information interaction method and related equipment, wherein the method comprises the following steps: the method comprises the steps of obtaining real-time position information of a first user, and obtaining use characteristic information of the first user for a first application; determining target application virtual asset data recommended for the first user according to the real-time position information and the use characteristic information; acquiring a real-time scene image through a camera device, rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image to obtain a data recommendation real-time image for the first user; and displaying the data recommendation real-time image. The invention can improve the diversity of information recommendation modes and the effectiveness of information recommendation.
Description
Technical Field
The present invention relates to the field of information interaction, and in particular, to an information interaction method and related device.
Background
With the development of internet technology, more and more applications based on the internet are integrated into the aspects of people's life. In some application scenarios, the application needs to recommend some application information to the user, for example, in some game applications, the game application may recommend some game equipment, game props, and the like to the user, in some e-commerce applications, the e-commerce application may recommend some clothing, decorations, and the like to the user, when recommending, the same information is generally recommended for all users, and the recommendation adopts a mode that the related information of the items to be recommended is displayed to the user in a list form, for example, pictures, parameters, and the like of the recommended items, and then the user selects the items according to the information in the display list. The recommendation method for recommending the unified information to the user through the list is relatively single, so that the information recommendation effectiveness is relatively low.
Disclosure of Invention
The invention provides an information interaction method and related equipment, and the diversity of information recommendation modes and the effectiveness of information recommendation can be improved through the information interaction method and the related equipment.
An embodiment of the present invention provides an information interaction method, including:
the method comprises the steps of obtaining real-time position information of a first user, and obtaining use characteristic information of the first user for a first application;
determining target application virtual asset data recommended for the first user according to the real-time position information and the use characteristic information;
acquiring a real-time scene image through a camera device, rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image to obtain a data recommendation real-time image for the first user;
and displaying the data recommendation real-time image.
Wherein the user data comprises an application operation record; the obtaining of the usage characteristic information of the first user for the first application comprises:
determining a first quantity of request response data which is the most front in the first cache queue of the first application as the application operation record; and the request response data in the first cache queue is response data corresponding to the application operation request of the first user to the first application.
Before the obtaining of the use characteristic information of the first user for the first application, the method further includes:
receiving a first application operation request of the first user for the first application;
judging whether first request response data corresponding to the first application operation request exists in the first cache queue or not;
if the first request response data exists, determining the first request response data as the request response data which is ranked the top in the first cache queue;
and under the condition of no storage, acquiring the first request response data from a first application server corresponding to the first application, and storing the first request response data into the first cache queue as the request response data which is ranked most at the front in the first cache queue.
Wherein the determining target application virtual asset data recommended for the first user according to the real-time location information and the usage characteristic information comprises:
extracting first feature data under at least one preset user feature label from the use feature information;
determining at least one peripheral sample user of the first user based on the real-time location information, the peripheral sample user having at least one target favorite asset data for a respective first application;
acquiring sample characteristic data of the peripheral sample users under the at least one preset user characteristic label;
determining feature similarity of the peripheral sample user and the first user according to the sample feature data and the first feature data;
and determining a second number of first peripheral sample users with the highest feature similarity, and determining the target application virtual asset data according to first target favorite asset data corresponding to the first peripheral sample users.
Wherein the determining the target application virtual asset data according to the first target favorite asset data corresponding to the first peripheral sample user comprises:
determining asset characteristic data of each first target favorite asset data under different asset characteristic tags;
determining a high-frequency asset feature set according to the asset feature data under different asset feature labels, wherein the high-frequency asset feature set comprises at least one high-frequency asset feature data, and the frequency of the at least one high-frequency asset feature data appearing in the asset feature data of the same first target favorite asset data is greater than a first frequency threshold;
and determining target application virtual asset data matched with the high-frequency asset feature set from a candidate application virtual asset database.
Rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image to obtain a data recommendation real-time image for the first user comprises:
determining a fusion position of a data icon corresponding to the target application virtual asset data in the real-time scene image;
determining a first affine transformation according to the position information of the camera device and the position information corresponding to the fusion position;
and drawing a data icon corresponding to the target application virtual asset data at the fusion position in the real-time scene image according to the first affine transformation.
Wherein the determining of the fusion position of the data icon corresponding to the target application virtual asset data in the real-time scene image comprises:
determining an asset data type of the target application virtual asset data;
and determining the fusion position of the target application virtual asset data in the real-time scene image according to the asset data type of the target application virtual asset data.
Another aspect of the embodiments of the present application provides an information interaction apparatus, including:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring real-time position information of a first user and acquiring use characteristic information of the first user for a first application;
the determining module is used for determining target application virtual asset data recommended for the first user according to the real-time position information and the use characteristic information;
the fusion module is used for acquiring a real-time scene image through a camera device, rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image, and obtaining a data recommendation real-time image for the first user;
and the display module is used for displaying the data recommendation real-time image.
In another aspect, an embodiment of the present invention provides an information interaction apparatus, including a processor, a memory, and a communication interface, where the processor, the memory, and the communication interface are connected to each other, where the communication interface is configured to receive and send data, the memory is configured to store program codes, and the processor is configured to call the program codes to perform the method in the above aspect of the embodiment of the present invention.
Yet another aspect of the embodiments of the present application provides a computer-readable storage medium storing a computer program, the computer program comprising program instructions that, when executed by a processor, perform a method as in one aspect of an embodiment of the present invention.
In the embodiment of the application, the real-time position information of a first user and the use characteristic information of the first user aiming at a first application are obtained, and more than two target application virtual asset numbers recommended for the first user are determined according to the real-time position information and the use characteristic information; the method comprises the steps of obtaining a real-time scene image through a camera device, rendering and fusing a data icon corresponding to target application virtual asset data to the real-time scene image to obtain a data recommendation real-time image for a first user, displaying the data recommendation real-time image, and receiving an icon confirmation instruction of the first user for the target data icon in the data recommendation real-time image. By determining the target application virtual asset data personalized by the user and displaying the target application virtual asset data to the user through data recommendation real-time image recommendation, the diversity of information recommendation modes is improved, and further the effectiveness of information recommendation is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of an information interaction method provided in an embodiment of the present application;
fig. 2 is a schematic diagram of a first buffer queue according to an embodiment of the present disclosure;
fig. 3 is a schematic diagram of another first buffer queue according to an embodiment of the present application;
fig. 4 is a schematic diagram of a real-time scene image according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a data recommendation real-time image provided by an embodiment of the present application;
fig. 6 is a schematic drawing diagram of a three-dimensional data icon provided in an embodiment of the present application;
FIG. 7 is a flowchart illustrating another information interaction method according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an information interaction apparatus according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of another information interaction device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The information interaction method is based on an Augmented Reality (AR) method, augmented Reality is also called as Augmented Reality and is a technology for promoting integration of real world information and virtual world information content, and the method implements simulation processing on entity information which is difficult to experience in a space range of the real world originally on the basis of scientific technologies such as computers and the like, performs superposition processing on virtual information content in the real world, and can be perceived by human senses in the process, so that sensory experience beyond Reality is realized.
According to the information interaction method, the data icon corresponding to the data when the target application virtual asset recommended to the first user is applied can be overlapped with the real-time scene image through the augmented reality technology to generate the data recommendation real-time image, the immersion feeling of the target application virtual asset data recommended by the first user is enhanced, the diversity of recommendation modes is improved, and the recommendation effectiveness is further improved.
The information interaction method in the embodiment of the application can be applied to scenes of online data recommendation in terminal application, for example, scenes of recommendation of game gifts, such as recommendation of game equipment, game props, game pets and the like; the method can be used in the recommendation scene of E-commerce commodities, such as recommendation of clothes, hairstyles, decorations and the like. The information interaction method in this application may be executed by a terminal installed with the application, further may be executed by the application, or may be executed by a service server operating the application and the terminal installed with the application at the same time.
Referring to fig. 1, fig. 1 is a schematic flowchart of an information interaction method provided in an embodiment of the present application, and as shown in the drawing, the method may include the following steps:
s101, acquiring real-time position information of a first user, and acquiring use characteristic information of the first user for a first application.
Wherein the first application is an application recommending information to the first user; the information interaction device may acquire the real-time location information of the first user from a terminal in which the first application is installed; or the real-time location information reported by the first application may be obtained from a service server operating the first application. In one implementation, the usage characteristic information of the first user for the first application may include user identity information of the first user, and the user identity information of the first user may include identity data of the first user under different identity tags, such as gender, age, occupation, hobbies, and the like. Further, in another optional implementation manner, the usage characteristic information of the first user further includes an application operation record of the first user, where the application operation record of the first user includes operation data of the first user on different operation tags for the first application within a certain time period.
When the usage characteristic information of the first user includes the application operation record of the first user, the application operation record may be acquired in the following manner. During the use of the first application by the first user, the first application caches some data, which may be responded to quickly when the first application requests it. Here, the data cached by the first application may be cached by an LRU (Least Recently Used) algorithm, specifically, first response data corresponding to an application operation request of the first application from the user is stored in the first cache queue, and after receiving the first application operation request of the first application from the first user, it is determined whether the first request response data corresponding to the first application operation request exists in the first cache queue. If the first request response data exists, the first request response data is determined as the request response data which is ranked the most front in the first cache queue, and the request response data which is originally ranked before the first request response data is sequentially adjusted backwards; and under the condition that the first request response data do not exist, acquiring the first request response data from a first application server corresponding to the first application, storing the first request response data into a first cache queue as the request response data which is ranked most ahead in the first cache queue, and sequentially backwards adjusting the originally stored request response data in the first cache queue. And determining a first quantity of request response data which is ranked most at the top in the first cache queue as the application operation record.
For example, referring to fig. 2, fig. 2 is a schematic diagram of a first buffer queue provided in this embodiment, as shown in the figure, state 1 is an original state of the first buffer queue, and request response data 5 is response data of a first application operation request for a first user, then the request response data 5 is arranged in a first position in the first buffer queue through state 2, and request response data 2 and request response data 4 are sequentially adjusted backward by one position to obtain a first buffer queue of state 3, and if the first number is 4, then the request response data 5, the request response data 1, the request response data 2, and the request response data 3 are determined as application operation records of the first user. Referring to fig. 3, fig. 3 is another schematic diagram of a first cache queue provided in this embodiment, as shown in the drawing, a state 1 is an original state of the first cache queue, and request response data 9 is request response data corresponding to a first application operation request acquired from a first application server, then through the state 2, the request response data 9 is used as request response data of a first position in the first cache queue, and an original last request response data 8 in the first cache queue is moved out of the first cache queue to obtain the first cache queue of the state 3, and if the first quantity is 4, the request response data 9, the request response data 1, the request response data 2, and the request response data 3 are determined as application operation records of a first user. It should be noted that the above state can be realized by adjusting the position pointer corresponding to each request response data in the queue.
S102, determining target application virtual asset data recommended for the first user according to the real-time position information and the use characteristic information.
The information interaction device can acquire position information of each user and identity information (including identity data under different identity labels) of each user, each user has corresponding target favorite asset data, and target application virtual asset data can be determined according to the target favorite asset data of each user, wherein the target favorite asset data can be interesting virtual asset data marked by the user, and the marking mode can be a consumed mode, a collection mode, a shopping cart adding mode, a sharing mode and the like.
In one implementation, in a case that the usage characteristic information of the first user only includes the user identity information of the first user, a plurality of reference users whose location information matches the real-time location information of the first user may be determined from the users of the first application according to the real-time location information of the first user, where the reference users may be users whose real-time distance from the first user does not exceed a preset distance threshold, and the reference users may also be users located in the same geographical area as the first user, such as users located in the same mall as the first user, or users located in the same residential district as the first user. And further, according to the user identity information of the first user and the identity information of the reference user, determining a plurality of target reference users with identity information matched with the user identity information of the first user from the reference user, wherein the number of target identity data of the target reference users is larger than a first data threshold, and the target identity data is the same identity data of the first user under the same identity label in the identity data of the target reference users. Therefore, the target application virtual asset data is determined according to the application virtual asset data of the target reference user.
In another implementation, the first feature data under at least one preset user feature tag may be extracted from the usage feature information, and at least one peripheral sample user of the first user may be determined from the usage users of the first application according to the real-time location information of the first user, where a determination manner of the peripheral sample user may refer to the determination manner of the usage user in the above implementation manner. And further acquiring sample feature data of the peripheral sample users under at least one preset user feature tag, determining the feature similarity between the peripheral sample users and the first users according to the first feature data and the sample feature data, determining the first peripheral sample users with the feature similarity higher than that of a second number, and determining target application virtual asset data according to first target favorite asset data corresponding to the first peripheral sample users. Optionally, the usage characteristic information may include user identity information and/or application operation records, where a characteristic tag corresponding to the user identity information is an identity tag, and a characteristic tag corresponding to the application operation records is an operation tag.
S103, acquiring a real-time scene image through a camera device, rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image, and obtaining a data recommendation real-time image for the first user.
Each target application virtual asset data has a preset corresponding data icon, and the data icon can be a plane icon or a three-dimensional icon. In the fusion process, a plane in a three-dimensional scene to which a data icon is attached is determined in the real-time scene image, then the plane in the three-dimensional scene is mapped onto a two-dimensional screen, namely the fusion position of the data icon in the real-time scene image, and the data icon is rendered and drawn on the mapped plane.
In particular implementations, reality augmentation may include Marker-Based reality augmentation (Marker-Based AR) and Marker-Less (or unmarked) Based reality augmentation (Marker-Less AR). In the display enhancement mode based on the marker, a marker (marker) for augmented reality, such as a template picture or a picture of a two-dimensional code with a certain specification shape of a renderer, can be preset, then the marker is placed in a real scene, the position of the marker in a real-time scene image is the fusion position of a data icon in the real-time scene image, then the marker is identified and posture evaluation is carried out through a camera device, the position of the marker and a template coordinate system with the marker center as an origin are determined, then a first mapping relation from the template coordinate system to the camera coordinate system of the camera device is determined, a second mapping relation from the camera coordinate system and a screen coordinate system corresponding to a screen is determined, further a first affine transformation from the template coordinate system to the screen coordinate system is determined according to the first mapping relation and the second mapping relation, further, the data icon corresponding to target application virtual asset data can be drawn at the fusion position in the real-time scene image according to the first affine transformation, and the fusion effect of the data icon attached to the marker is achieved.
In a reality augmentation mode based on a small amount of marks (or no marks), the fusion position of the data icon can be determined not based on the marks, the feature points in the real-time scene image can be extracted by a feature extraction algorithm and compared with the feature points of the template object recorded in advance, when the matching number of the feature points in the real-time scene image and the template feature points exceeds a threshold value, the template object is determined to be scanned and is used as the marks, and then the icon data can be further drawn based on the template object in a mode subsequent to the reality augmentation mode based on the marks.
Optionally, the asset data type of the target application virtual asset data may be determined, and then the fusion position of the target application virtual asset data in the real-time scene image may be determined according to the asset data type of the target application virtual asset data. For example, if the target application virtual asset data includes a unicorn ride, a coupon treasure box, and a virtual puppy pet, the unicorn ride belongs to a ride asset data type, the corresponding fusion position is the ground, the coupon treasure box belongs to a treasure box asset data type, the corresponding fusion position is an object placing surface, the virtual puppy pet belongs to a pet asset data type, and the corresponding fusion position is a grassland, referring to fig. 4, fig. 4 is a real-time scene image schematic diagram provided in this embodiment of the present application, where position 1, position 2, and position 3 are fusion positions of the unicorn ride, the coupon treasure box, and the virtual puppy pet, respectively. Referring to fig. 5, fig. 5 is a schematic diagram of a data recommendation real-time image provided in an embodiment of the present application, and fig. 5 shows a data recommendation real-time image obtained by fusing a unicorn seat, a coupon treasure box, and a virtual puppy pet in a corresponding fusion position in the real-time scene image shown in fig. 4.
If the data icon is a three-dimensional icon, the data icon may be drawn based on a WebGL (Web Graphics Library) protocol. Referring to fig. 6 and 6, which are schematic diagrams illustrating drawing of a three-dimensional data icon according to an embodiment of the present disclosure, as shown in the figures, a vertex array of the data icon is drawn into a primitive by a vertex shader, the primitive is assembled and rasterized, and then the primitive is transmitted to a fragment shader, a data recommendation real-time image is obtained after various fragment operations, and the data recommendation real-time image is stored in a frame buffer, so that the data recommendation real-time image in the frame buffer is displayed on a screen.
And S104, displaying the data recommendation real-time image.
The data recommendation real-time image is displayed on a terminal display screen, a first user can select a data icon in the data recommendation real-time image, the information interaction device receives an icon confirmation instruction of the first user for a target data icon in a data recommendation real-time object, and then target application recommendation asset data corresponding to the target data icon is transferred to a virtual asset account of the first user for the first application.
In the embodiment of the application, the real-time position information of a first user and the use characteristic information of the first user aiming at a first application are obtained, and more than two target application virtual asset numbers recommended for the first user are determined according to the real-time position information and the use characteristic information; the method comprises the steps of obtaining a real-time scene image through a camera device, rendering and fusing a data icon corresponding to target application virtual asset data to the real-time scene image, obtaining a data recommendation real-time image for a first user, displaying the data recommendation real-time image, and receiving an icon confirmation instruction of the first user for the target data icon in the data recommendation real-time image. By determining the target application virtual asset data personalized by the user and displaying the target application virtual asset data to the user through data recommendation real-time image recommendation, the diversity of information recommendation modes is improved, and further the effectiveness of information recommendation is improved.
Referring to fig. 7, fig. 7 is a flowchart illustrating another information interaction method provided in an embodiment of the present application, where as shown in the figure, the method may include the following steps:
s701, acquiring real-time position information of a first user, and acquiring use characteristic information of the first user for a first application.
The use characteristic information of the first user comprises user identity information and/or application operation records of the first user, wherein the user identity information comprises identity data under different identity labels, and the application operation records comprise operation data under different operation labels.
S702, extracting first characteristic data under at least one preset user characteristic label from the use characteristic information.
Here, the preset user feature tag includes a preset user identity tag and/or a preset user operation tag corresponding to the usage feature information, and the first feature data includes identity data of the first user in the preset identity tag and operation data of the first user in the preset user operation tag. Optionally, the first feature data of some text classes may be quantized, for example, a preset identity tag for gender, where the identity data under the tag may be quantized to 1 if the identity data is female, and may be quantized to 2 if the identity data is male.
S703, determining at least one peripheral sample user of the first user according to the real-time position information, wherein the peripheral sample user has at least one target favorite asset data aiming at the first application.
The surrounding sample users may be users of the first application within a preset distance threshold range from the first user, and may also be users of the first application within the same geographical range as the first user, such as a same mall, a same residential area, and the like. Each peripheral sample user has respective target favorite asset data for the first application, which may be target favorite asset data collected, purchased, or shared by each peripheral sample user.
S704, obtaining sample characteristic data of the peripheral sample users under the at least one preset user characteristic label.
The acquired sample feature data of the peripheral sample users are feature data of the first user under the same preset user feature label. Optionally, the sample feature data of the text class may be quantized in the same manner as the first feature data.
S705, according to the sample feature data and the first feature data, determining feature similarity of the peripheral sample user and the first user.
Here, a sample feature vector may be determined from the sample feature data, a first feature vector may be determined from the first feature data, and feature similarity between the peripheral sample user and the first user may be calculated from the sample feature vector and the first feature vector. Alternatively, the feature Similarity may be one of Cosine Similarity (Cosine Similarity), modified Cosine Similarity (Adjusted Cosine Similarity), and pearson correlation Similarity (pearson correlation Similarity).
S706, determining a second number of first peripheral sample users with the highest feature similarity, and determining the target application virtual asset data according to first target favorite asset data corresponding to the first peripheral sample users.
Here, after the feature similarities of the respective peripheral sample users and the first user are ranked from high to low, the first peripheral sample users of the second number with the highest feature similarity are determined. For example, there are four first peripheral sample users, which are user a, user b, user c, and user d, respectively, and the corresponding first target favorite asset data is asset data 1 and asset data 2 corresponding to user a, asset data 2 and asset data 3 corresponding to user b, asset data 2 and asset data 1 corresponding to user c, and asset data 3 corresponding to user d, and asset data 2 and asset data 3 may be determined as target application virtual asset data.
In another implementation, each first sample peripheral user may perform quantitative scoring on each first target favorite asset data according to a labeling mode of each first target favorite asset data, for example, the purchase labeling mode is quantized to 5 points, the share labeling mode is quantized to 4 points, and the collection labeling mode is quantized to 3 points, so that an estimated evaluation score of the first user for each target application virtual asset data may be calculated according to the quantitative scoring of the first target favorite asset data by the first peripheral sample user and the feature similarity of each first peripheral sample user and the first user, and a plurality of first target favorite asset data with the highest estimated evaluation score may be determined as the target application virtual asset data.
In another implementation, asset feature data of each first target favorite asset data under different asset feature tags may be determined, for example, if the first target favorite asset data is game dress, a style, a hue, a gender, and the like of a corresponding asset feature tag may be determined. Determining a high-frequency asset feature set according to asset feature data under different asset feature labels, wherein the high-frequency asset feature set comprises at least one high-frequency asset feature data, and the frequency of the at least one high-frequency asset feature data appearing in the asset feature data of the same first target favorite asset data is greater than a first frequency threshold; and determining target application virtual asset data matched with the high-frequency asset feature set from the candidate application virtual asset database. Specifically, the high-frequency asset feature set may be determined through an association rule algorithm, where if the high-frequency asset feature set only includes one high-frequency asset feature data, the frequency of occurrence of the high-frequency asset feature data in the asset feature data of the first target favorite asset data is greater than a first frequency threshold, and if the high-frequency asset feature set includes a plurality of high-frequency asset feature data, the frequency of occurrence of the plurality of high-frequency asset feature data in the asset feature data of the same first target favorite asset data is greater than the first frequency threshold.
In a specific implementation, the asset feature data of the discrete numerical type of the first target favorite asset data may be preprocessed first to be preprocessed into asset feature data of a continuous numerical segment type. For some asset feature tags of numerical classes, segmentation can be performed in advance for possible values of the asset feature tag to obtain different segmented continuous data under the asset feature tag, and then the data under the asset feature tag is replaced by the corresponding segmented continuous data. For example, 3 pieces of corresponding piece-sequential data, which are 0 to 10 medals, 10 to 50 medals, and 51 medals or more, may be divided in advance for asset characteristic data, which is a game-decorating price, and if a certain game-decorating price is 8 medals, it may be preprocessed into 0 to 10 medals.
Furthermore, acquiring each preprocessed first target favorite asset data corresponding to each asset feature data possibly appearing under each asset feature tag to form a plurality of candidate item sets, and determining the candidate item sets of which the appearance times of the asset feature data contained in the candidate item sets are not less than a preset appearance time threshold value as frequent item sets by comparing the appearance times of the asset feature data contained in each preprocessed first target favorite asset data with the preset appearance time threshold value;
if only one frequent item set exists, determining the frequent item set as a high-frequency asset feature set; if the frequent item set comprises a plurality of frequent item sets, combining the asset characteristic data contained in any frequent item set with the asset characteristic data in other frequent item sets to form a candidate two-item set, comparing the frequency of the asset characteristic data contained in each candidate two-item set in each preprocessed first target favorite asset data with a preset frequency threshold, and determining the candidate two-item set with the frequency not less than the preset frequency threshold as the frequent two-item set; if the candidate two-item set with the occurrence frequency not smaller than a preset occurrence frequency threshold does not exist, determining a plurality of frequent one-item sets as high-frequency asset feature sets;
if only one frequent binomial set exists, determining the frequent binomial set as a high-frequency asset feature set; if the frequent binomial sets comprise a plurality of frequent binomial sets, combining every two of the frequent binomial sets with only one asset feature data different from the asset feature data in the frequent binomial sets to form a candidate three-item set, and comparing the frequency of the asset feature data contained in the candidate three-item set in each first target favorite asset data with a preset frequency threshold; and in the same way, when only one frequent L item set is determined, determining the frequent L item set as a high-frequency asset feature set, or determining a plurality of frequent M-1 item sets as the high-frequency asset feature set until a candidate M item set is determined, wherein the candidate M item set does not contain asset feature data, and the frequency of the asset feature data appearing in each first target favorite asset data is not less than a preset appearance frequency threshold. It is understood that, in the above process, only one asset feature data under the same asset feature tag in the candidate sets is generated by combination.
And S707, acquiring a real-time scene image through a camera device, rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image, and obtaining a data recommendation real-time image for the first user.
S708, displaying the data recommendation real-time image, and receiving an icon confirmation instruction of the first user for a target data icon in the data recommendation real-time image.
The implementation manner corresponding to steps S707 and S708 may refer to the corresponding description in the embodiment corresponding to fig. 1, and is not described herein again.
In the embodiment of the application, the real-time position information of a first user and the use characteristic information of the first user for a first application are obtained, first characteristic data under at least one preset user characteristic label are extracted from the use characteristic information, at least one peripheral sample user of the first user is determined according to the real-time position information, and sample characteristic data of the peripheral sample user under the at least one preset user characteristic label are obtained. And determining the feature similarity of the peripheral sample user and the first user according to the sample feature data and the first feature data, and determining the feature similarity of the peripheral sample user and the first user according to the sample feature data and the first feature data. And determining a second number of first peripheral sample users with highest feature similarity, further determining target application virtual asset data according to first target favorite asset data corresponding to the first peripheral sample users, and displaying the target asset application virtual asset data to the users for selection through a reality augmentation technology. The method and the device realize that the target application virtual asset data is recommended to the user in an individualized way through a reality augmentation way, improve the diversity of information recommendation ways, and further improve the effectiveness of information recommendation.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an information interaction apparatus according to an embodiment of the present invention, as shown in the figure, the information interaction apparatus 80 at least includes an obtaining module 801, a determining module 802, a fusing module 803, and a presenting module 804, where:
an obtaining module 801, configured to obtain real-time location information of a first user, and obtain usage characteristic information of the first user for a first application;
a determining module 802, configured to determine, according to the real-time location information and the usage characteristic information, target application virtual asset data recommended for the first user;
a fusion module 803, configured to obtain a real-time scene image through a camera device, render and fuse a data icon corresponding to the target application virtual asset data to the real-time scene image, and obtain a data recommendation real-time image for the first user;
a display module 804, configured to display the data recommendation real-time image.
Optionally, the user data includes an application operation record; the obtaining module 801 is specifically configured to:
determining a first quantity of request response data which is ranked most front in a first cache queue of the first application as the application operation record; and the request response data in the first cache queue is response data corresponding to the application operation request of the first user to the first application.
Optionally, the obtaining module 801 is further configured to:
receiving a first application operation request of the first user for the first application;
judging whether first request response data corresponding to the first application operation request exists in the first cache queue or not;
if the first request response data exists, determining the first request response data as the request response data which is ranked the top in the first cache queue;
and under the condition of no storage, acquiring the first request response data from a first application server corresponding to the first application, and storing the first request response data into the first cache queue as the request response data which is ranked most at the front in the first cache queue.
Optionally, the determining module 802 is specifically configured to:
extracting first feature data under at least one preset user feature label from the use feature information;
determining at least one peripheral sample user of the first user based on the real-time location information, the peripheral sample user having at least one target favorite asset data for a first application, respectively;
acquiring sample characteristic data of the peripheral sample users under the at least one preset user characteristic label;
determining feature similarity of the peripheral sample user and the first user according to the sample feature data and the first feature data;
and determining a second number of first peripheral sample users with the highest feature similarity, and determining the target application virtual asset data according to first target favorite asset data corresponding to the first peripheral sample users.
Optionally, the determining module 802 is specifically configured to:
determining asset characteristic data of each first target favorite asset data under different asset characteristic tags;
determining a high-frequency asset feature set according to the asset feature data under different asset feature labels, wherein the high-frequency asset feature set comprises at least one high-frequency asset feature data, and the frequency of the at least one high-frequency asset feature data appearing in the asset feature data of the same first target favorite asset data is greater than a first frequency threshold;
and determining target application virtual asset data matched with the high-frequency asset feature set from a candidate application virtual asset database.
Optionally, the fusion module 803 is specifically configured to:
determining a fusion position of a data icon corresponding to the target application virtual asset data in the real-time scene image;
determining a first affine transformation according to the position information of the camera device and the position information corresponding to the fusion position;
and drawing a data icon corresponding to the target application virtual asset data at the fusion position in the real-time scene image according to the first affine transformation.
Optionally, the fusion module 803 is specifically configured to:
determining an asset data type of the target application virtual asset data;
and determining the fusion position of the target application virtual asset data in the real-time scene image according to the asset data type of the target application virtual asset data.
In a specific implementation, the information interaction device may execute, through each built-in functional module, each step in the information interaction method shown in fig. 1 and fig. 7, and details of the specific implementation may refer to details of implementation of each step in the embodiment corresponding to fig. 1 and fig. 7, which are not described herein again.
In the embodiment of the application, an acquisition module acquires real-time position information of a first user and use characteristic information of the first user aiming at a first application, and a determination module determines more than two target application virtual asset numbers recommended for the first user according to the real-time position information and the use characteristic information; the fusion module obtains a real-time scene image through the camera device, renders and fuses a data icon corresponding to the target application virtual asset data to the real-time scene image to obtain a data recommendation real-time image for the first user, and then the display module displays the data recommendation real-time image and receives an icon confirmation instruction of the first user for the target data icon in the data recommendation real-time image. By determining the target application virtual asset data personalized by the user and displaying the target application virtual asset data to the user through data recommendation real-time image recommendation, the diversity of information recommendation modes is improved, and further the effectiveness of information recommendation is improved.
Referring to fig. 9, fig. 9 is a schematic structural diagram of another information interaction apparatus according to an embodiment of the present invention, and as shown in the figure, the information interaction apparatus 90 includes a processor 901, a memory 902, and a communication interface 903. The processor 901 is connected to the memory 902 and the communication interface 903, for example, the processor 901 may be connected to the memory 902 and the communication interface 903 through a bus.
The processor 901 is configured to support the information interaction apparatus to perform corresponding functions in the information interaction methods described in fig. 1 and fig. 7. The Processor 901 may be a Central Processing Unit (CPU), a Network Processor (NP), a hardware chip, or any combination thereof. The hardware chip may be an Application-Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof. The PLD may be a Complex Programmable Logic Device (CPLD), a Field-Programmable Gate Array (FPGA), general Array Logic (GAL), or any combination thereof.
The memory 902 is used to store program codes and the like. The memory 902 includes an internal memory that may include at least one of: volatile memory (e.g., dynamic Random Access Memory (DRAM), static RAM (SRAM), synchronous Dynamic RAM (SDRAM), etc.) and non-volatile memory (e.g., one Time Programmable Read Only Memory (OTPROM), programmable ROM (PROM), erasable Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM). Memory 902 may also include external memory that may include at least one of a Hard Disk (Hard Disk Drive, HDD) or Solid State Drive (SSD), a flash Drive, such as high density flash (CF), secure Digital (SD), micro SD, mini SD, extreme digital (xD), memory stick, etc.
The communication interface 903 is used for receiving or transmitting data.
the method comprises the steps of obtaining real-time position information of a first user, and obtaining use characteristic information of the first user for a first application;
determining target application virtual asset data recommended for the first user according to the real-time position information and the use characteristic information;
acquiring a real-time scene image through a camera device, rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image to obtain a data recommendation real-time image for the first user;
and displaying the data recommendation real-time image.
It should be noted that, the implementation of each operation may also correspond to the corresponding description of the method embodiments shown in fig. 1 and fig. 7; the processor 901 may also be configured to perform other operations in the above method embodiments.
Embodiments of the present invention also provide a computer storage medium storing a computer program, the computer program comprising program instructions, which when executed by a computer, cause the computer to execute the method according to the foregoing embodiments, wherein the computer may be a part of the above-mentioned information interaction apparatus.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. An information interaction method, comprising:
acquiring real-time position information of a first user, and acquiring use characteristic information of the first user for a first application;
determining target application virtual asset data recommended for the first user according to the real-time position information and the use characteristic information, wherein the target application virtual asset data is determined according to target favorite asset data of users, and the users use the first application;
acquiring a real-time scene image through a camera device, rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image, and obtaining a data recommendation real-time image for the first user; in the process of rendering fusion, the fusion position of the target application virtual asset data in the real-time scene image is obtained according to the determined asset data type of the target application virtual asset data;
and displaying the data recommendation real-time image.
2. The method of claim 1, wherein the usage characteristic information of the first user for the first application comprises an application operation record; the obtaining of the usage characteristic information of the first user for the first application comprises:
determining a first quantity of request response data which is the most front in the first cache queue of the first application as the application operation record; the request response data in the first cache queue is response data corresponding to the application operation request of the first user to the first application.
3. The method of claim 2, wherein before the obtaining the usage characteristic information of the first user for the first application, further comprising:
receiving a first application operation request of the first user for the first application;
judging whether first request response data corresponding to the first application operation request exists in the first cache queue or not;
if the first request response data exists, determining the first request response data as the request response data which is ranked the top in the first cache queue;
and under the condition of no storage, acquiring the first request response data from a first application server corresponding to the first application, and storing the first request response data into the first cache queue as the request response data which is ranked most at the front in the first cache queue.
4. The method of claim 1, wherein determining target application virtual asset data to recommend to the first user based on the real-time location information and the usage characteristic information comprises:
extracting first feature data under at least one preset user feature tag from the use feature information;
determining at least one peripheral sample user of the first user based on the real-time location information, the peripheral sample user having at least one target favorite asset data for a first application, respectively;
acquiring sample characteristic data of the peripheral sample users under the at least one preset user characteristic label;
determining feature similarity of the peripheral sample user and the first user according to the sample feature data and the first feature data;
and determining a second number of first peripheral sample users with the highest feature similarity, and determining the target application virtual asset data according to first target favorite asset data corresponding to the first peripheral sample users.
5. The method of claim 4, wherein determining the target application virtual asset data based on the first target favorite asset data corresponding to the first peripheral sample user comprises:
determining asset characteristic data of each first target favorite asset data under different asset characteristic tags;
determining a high-frequency asset feature set according to the asset feature data under different asset feature labels, wherein the high-frequency asset feature set comprises at least one high-frequency asset feature data, and the frequency of the at least one high-frequency asset feature data appearing in the asset feature data of the same first target favorite asset data is greater than a first frequency threshold;
and determining target application virtual asset data matched with the high-frequency asset feature set from a candidate application virtual asset database.
6. The method of claim 1, wherein the rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image to obtain a data recommendation real-time image for the first user comprises:
determining a fusion position of a data icon corresponding to the target application virtual asset data in the real-time scene image;
determining a first affine transformation according to the position information of the camera device and the position information corresponding to the fusion position;
and drawing a data icon corresponding to the target application virtual asset data at the fusion position in the real-time scene image according to the first affine transformation.
7. The method of claim 6, wherein the determining a fusion position of a data icon corresponding to the target application virtual asset data in the real-time scene image comprises:
determining an asset data type of the target application virtual asset data;
and determining the fusion position of the target application virtual asset data in the real-time scene image according to the asset data type of the target application virtual asset data.
8. An information interaction apparatus, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring real-time position information of a first user and acquiring use characteristic information of the first user for a first application;
a determining module, configured to determine target application virtual asset data recommended for the first user according to the real-time location information and the usage characteristic information, where the target application virtual asset data is determined according to target favorite asset data of users, and the users include users using the first application;
the fusion module is used for acquiring a real-time scene image through a camera device, rendering and fusing a data icon corresponding to the target application virtual asset data to the real-time scene image, and obtaining a data recommendation real-time image for the first user; in the process of rendering fusion, the fusion position of the target application virtual asset data in the real-time scene image is obtained according to the determined asset data type of the target application virtual asset data;
and the display module is used for displaying the data recommendation real-time image.
9. An information interaction device, comprising a processor, a memory and a communication interface, wherein the processor, the memory and the communication interface are connected with each other, the communication interface is used for receiving and sending data, the memory is used for storing program codes, and the processor is used for calling the program codes and executing the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which is executed by a processor to implement the method of any one of claims 1 to 7.
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| CN112221159B (en) * | 2020-10-21 | 2022-04-08 | 腾讯科技（深圳）有限公司 | Virtual item recommendation method and device and computer readable storage medium |
| CN113704605A (en) * | 2021-08-24 | 2021-11-26 | 山东库睿科技有限公司 | Service information recommendation method and device, electronic equipment and medium |
| CN114693414B (en) * | 2022-06-02 | 2022-11-04 | 深圳星坊科技有限公司 | Store item recommendation method and device, computer equipment and storage medium |
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