CN112241486A - Multimedia information acquisition method and device - Google Patents
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Abstract
The disclosure discloses a multimedia information acquisition method, a multimedia information acquisition device, an electronic device and a computer-readable storage medium, wherein the method comprises the following steps: acquiring a user behavior list according to a history log of a current user account; the user behavior list comprises an identifier; acquiring a first embedded vector according to the identifier; calculating the similarity between the first embedded vector and a second embedded vector in a multimedia information base; and selecting preset number of multimedia information as a multimedia information base to be recommended according to the sorting result. The method comprises the steps of obtaining a user behavior list according to a historical log of a current user account, wherein the user behavior list comprises an identifier; acquiring a first embedded vector according to the identifier; calculating the similarity between the first embedded vector and a second embedded vector in a multimedia information base, and sequencing the second embedded vector according to the similarity; and selecting a preset number of multimedia information as a multimedia information base to be recommended according to the sorting result, so that the recommendation speed can be increased, and personalized recommendation can be realized for different user accounts.
Description
Technical Field
The present disclosure relates to the field of multimedia information processing technologies, and in particular, to a method and an apparatus for acquiring multimedia information, an electronic device, and a computer-readable storage medium.
Background
With the rapid development of internet technology, people increasingly use personalized push applications (apps) to acquire multimedia information, including news, videos, and the like. Besides pushing interesting multimedia information to the user when the user uses the apps, the apps can also actively push customized multimedia information to the user when the user does not use the apps through a multimedia information pushing mechanism.
In the prior art, when a video is recommended to a user, a multimedia information recommendation model is often used for scoring and predicting all multimedia information in a video library, and the multimedia information with high score is recommended to the user. However, due to the large amount of data in the video library, the speed is slow when the user refreshes the recommendation, and so on.
Disclosure of Invention
The disclosure provides a multimedia information acquisition method, a multimedia information acquisition device, an electronic device and a computer-readable storage medium, which at least solve the problem that in the related art, due to the huge data amount in a video library, the speed is slower when a user refreshes recommendations. The technical scheme of the disclosure is as follows:
according to a first aspect of the embodiments of the present disclosure, there is provided a multimedia information obtaining method, including:
acquiring a user behavior list according to a history log of a current user account; the user behavior list comprises at least one identification which is associated with the multimedia information and corresponds to the user behavior, and the identification is used for marking the characteristics of at least one dimension of the multimedia information;
obtaining at least one first embedding vector according to the identification associated with the at least one multimedia message; wherein one identifier corresponds to one first embedded vector;
calculating the similarity between the first embedded vector and a second embedded vector stored in a user behavior information base, and sequencing the multimedia information associated with the second embedded vector according to the similarity;
and selecting a preset number of multimedia information as the multimedia information to be recommended corresponding to the current user account according to the sorting result.
Further, the calculating the similarity between the first embedding vector and a second embedding vector stored in a user behavior information base, and ordering the multimedia information associated with the second embedding vector according to the similarity includes:
for a multimedia information identifier of each multimedia information corresponding to each user behavior, acquiring a first embedded vector of the corresponding multimedia information according to the multimedia information identifier, and acquiring a first embedded vector of an author according to an author identifier of the multimedia information corresponding to the multimedia information identifier, wherein the author identifier is an identifier corresponding to the author of the multimedia information corresponding to the multimedia information identifier;
splicing the first embedded vector of the multimedia information and the first embedded vector of the author.
Furthermore, the second embedding vector is formed by splicing the embedding vector of the multimedia information stored in the user behavior information base and the embedding vector of the author of the stored multimedia information.
Further, the user behavior is a behavior of triggering a preset button, wherein the preset button is at least one of a button for clicking multimedia information, a button for liking the multimedia information, a button for browsing the multimedia information, and a button for paying attention to an author of the multimedia information.
According to a second aspect of the embodiments of the present disclosure, there is also provided a multimedia information acquiring apparatus including:
the identification acquisition module is used for acquiring a user behavior list according to a historical log of a current user account; the user behavior list comprises at least one identification which is associated with the multimedia information and corresponds to the user behavior, and the identification is used for marking the characteristics of at least one dimension of the multimedia information;
the vector acquisition module is used for acquiring at least one first embedded vector according to the identifier associated with the at least one piece of multimedia information; wherein one identifier corresponds to one first embedded vector;
the similarity calculation module is used for calculating the similarity between the first embedded vector and a second embedded vector stored in a user behavior information base and sequencing the multimedia information associated with the second embedded vector according to the similarity;
and the multimedia information base determining module is used for selecting a preset number of multimedia information as the multimedia information to be recommended corresponding to the current user account according to the sorting result.
Further, the identifier is a multimedia information identifier, and the vector acquisition module is specifically configured to: for a multimedia information identifier of each piece of multimedia information corresponding to a user behavior, acquiring a first embedded vector of the corresponding multimedia information according to the multimedia information identifier, and acquiring a first embedded vector of an author according to an author identifier of the multimedia information corresponding to the multimedia information identifier, wherein the author identifier is an identifier corresponding to the author of the multimedia information corresponding to the multimedia information identifier; splicing the first embedded vector of the multimedia information and the first embedded vector of the author.
Furthermore, the second embedding vector is formed by splicing the embedding vector of the multimedia information stored in the user behavior information base and the embedding vector of the author of the stored multimedia information.
Further, the user behavior is a behavior of triggering a preset button, wherein the preset button is at least one of a button for clicking multimedia information, a button for liking the multimedia information, a button for browsing the multimedia information, and a button for paying attention to an author of the multimedia information.
According to a third aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory for storing processor-executable instructions; wherein the processor is configured to: the multimedia information acquisition method according to any one of the first aspect is implemented by executing instructions.
According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer-readable storage medium, wherein instructions of the storage medium, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the multimedia information acquisition method according to any one of the first aspect.
According to a fifth aspect of embodiments of the present disclosure, a computer product is provided, which includes the multimedia information acquiring method according to any one of the first aspect.
The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects: acquiring a user behavior list according to a history log of a current user account, wherein the user behavior list comprises an identifier; acquiring a first embedded vector according to the identifier; calculating the similarity between the first embedded vector and a second embedded vector in a multimedia information base, and sequencing the second embedded vector according to the similarity; and selecting a preset number of multimedia information as a multimedia information base to be recommended according to the sorting result, so that the recommendation speed can be increased, and personalized recommendation can be realized for different user accounts.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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.
Fig. 1 is a flowchart of a multimedia information obtaining method according to an embodiment of the disclosure.
Fig. 2 is a block diagram of a multimedia information acquiring apparatus according to a second embodiment of the disclosure.
Fig. 3 is a block diagram of an electronic device according to a third embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
Example one
Fig. 1 is a flowchart of a multimedia information obtaining method according to an embodiment of the present disclosure, and an execution main body of the multimedia information obtaining method according to the embodiment of the present disclosure may be a multimedia information obtaining apparatus provided by the embodiment of the present disclosure, and the apparatus may be integrated in a mobile terminal device (e.g., a smart phone, a tablet computer, etc.), a notebook computer, or a fixed terminal (a desktop computer), and the multimedia information obtaining apparatus may be implemented by hardware or software. As shown in fig. 1, the method comprises the following steps:
in step S11, a user behavior list is obtained according to the history log of the current user account; the user behavior list comprises an identifier associated with at least one piece of multimedia information corresponding to user behaviors, and the identifier is used for marking the characteristics of at least one dimension of the multimedia information.
Wherein the multimedia information includes, but is not limited to, short video. The present disclosure may be applied in short video applications for recommending short videos.
The user behavior list may include one or more user behaviors in the recent period (for example, one month), and may be a behavior for triggering a preset button, where the preset button is at least one of a button for clicking multimedia information, a button for liking multimedia information, a button for browsing multimedia information, and a button for an author focusing on multimedia information. Namely, the behavior includes the behavior of clicking multimedia information, the behavior of liking multimedia information, the behavior of browsing multimedia information and the behavior of paying attention to the author of multimedia information. Moreover, there may be conditional dependency among various user behaviors, for example, for a multimedia message, only if the user clicks the behavior first, there will be subsequent behaviors of like, browsing, and paying attention to the multimedia message. For example, in the case of short videos, operation buttons such as click buttons, like buttons, and focus buttons may be provided on a display interface of a short video application. For the video in the video, the user can play the video content by triggering and clicking a button; further, if the video is played completely or the video is played for more than a predetermined time (e.g., 1 minute), the user may be considered to have browsed the video; further, if the user likes the video after browsing the video, the user likes the video content by triggering a like button mark; further, if the user likes the video and can focus on the author of the video, the user can focus on the author of the video by triggering the focus button.
And, each kind of user behavior corresponds to one or more multimedia information, for example, the user behavior is a behavior that the user clicks on the multimedia information, and the user may click on a plurality of multimedia information in the near future. The user behavior list includes an identifier associated with the multimedia information or the multimedia information corresponding to each user behavior, and the identifier may be a multimedia information identifier and/or an author identifier.
In step S12, at least one first embedding vector is obtained according to the identifier associated with the at least one multimedia message; wherein one identifier corresponds to one first embedded vector.
The embedded vector embedding is a vector constructed according to a mapping relationship, and the mapping relationship may be a linear vector space from which multimedia information in a space is projected, so that the distance between the multimedia information and the linear vector space can be calculated and measured in the vector space.
Wherein the first embedding vector is generated according to multimedia information characteristics and/or user attributes of the multimedia information, and the first embedding vector comprises the multimedia information characteristics and/or the user attributes. The user attributes include a name or a nickname, an age, a gender, a household type and the like input by the user.
Specifically, the embedded vector generation model may be obtained in advance based on neural network or deep learning neural network training, and in this embodiment, the embedded vector may be obtained in real time according to the embedded vector generation model, or the embedded vector of the model may be generated in advance according to the embedded vector, and the corresponding relationship between the identifier and the embedded vector is stored.
In step S13, a similarity between the first embedding vector and a second embedding vector stored in the user behavior information base is calculated, and the multimedia information associated with the second embedding vector is sorted according to the similarity.
The user behavior information base corresponds to the user behavior types. For example, the behavior of clicking multimedia information corresponds to a clicked multimedia information library, the behavior of liking multimedia information corresponds to a liking multimedia information library, the behavior of browsing multimedia information corresponds to a browsing multimedia information library, the behavior of an author focusing on multimedia information corresponds to a focusing multimedia information library, and the like.
For each user behavior, the identification is a multimedia information identification, and at least one pair of the multimedia information identification and a second embedded vector is stored in a corresponding user behavior information base; and/or if the identification is an author identification, storing at least one pair of the author identification and the second embedded vector in the corresponding user behavior information base.
Wherein the second embedding vector corresponds to the first embedding vector. For example, if the first embedding vector is an embedding vector obtained according to the multimedia information characteristics, the second embedding vector is also an embedding vector obtained according to the multimedia information characteristics, and the corresponding relation between the second embedding vector and the multimedia information identifier is stored in the multimedia information base; and if the first embedding vector is the embedding vector obtained according to the author multimedia information, the second embedding vector is also the embedding vector obtained according to the author multimedia information, and the corresponding relation between the second embedding vector and the author identifier is stored in the multimedia information base.
The similarity may be a cosine value, a euclidean distance, etc. of the first embedded vector and the second embedded vector, so as to measure the approximation degree of the first embedded vector and the second embedded vector.
Specifically, each user behavior corresponds to at least one first embedded vector, and the similarity between each first embedded vector and a second embedded vector is calculated respectively, wherein the number of the second embedded vectors is multiple. For example, if the user behavior is a behavior of clicking multimedia information, the corresponding first embedded vectors are a1, a2 and a3, respectively, and 10000 second embedded vectors in the multimedia information base are clicked, then the similarities of a1 and 10000 second embedded vectors are calculated and sorted according to the similarity, the similarities of a2 and 10000 second embedded vectors are sorted according to the similarity, and the similarities of a3 and 10000 second embedded vectors are sorted according to the similarity. If the user behavior is a behavior liking multimedia information, the corresponding first embedded vectors are b1, b2, b3 and b4 respectively, 1000 corresponding second embedded vectors in the multimedia information liking behavior multimedia information library are calculated, the similarity of b1 and 1000 second embedded vectors is calculated respectively and sorted according to the size of the similarity, the similarity of b2 and 1000 second embedded vectors is sorted according to the size of the similarity, the similarity of b3 and 1000 second embedded vectors is sorted according to the size of the similarity, and the similarity of b4 and 1000 second embedded vectors is sorted according to the size of the similarity. Similar to other user behaviors, the description is omitted here.
In step S14, a preset number of multimedia information is selected as the to-be-recommended multimedia information base corresponding to the current user account according to the sorting result, and the preset number of multimedia information in the to-be-recommended multimedia information base is sorted according to the similarity.
The preset number of multimedia information can be set by user.
Specifically, referring to the example in the step S13, if the user behavior is a behavior of clicking the multimedia information and a behavior of liking the multimedia information, for the behavior of clicking the multimedia information, according to the similarity between the first embedded vector a1 and 10000 second embedded vectors, the identifiers corresponding to the first 100 second embedded vectors are selected as recommended multimedia information and stored in the multimedia information library to be recommended; according to the similarity between the first embedding vector a2 and 10000 second embedding vectors, selecting identifiers corresponding to the first 100 second embedding vectors as recommended multimedia information and storing the recommended multimedia information into a to-be-recommended multimedia information base; according to the similarity between the first embedding vector a3 and 10000 second embedding vectors, selecting identifiers corresponding to the first 100 second embedding vectors as recommended multimedia information and storing the recommended multimedia information into a to-be-recommended multimedia information base; then a total of 100 x 3 identifiers for the behavior of clicking the multimedia information are stored as the recommended multimedia information in the multimedia information base to be recommended. Similarly, for the behavior like the multimedia information, according to the similarity between the first embedding vector b1 and 1000 second embedding vectors, the identifiers corresponding to the first 50 second embedding vectors in the sequence are selected as recommended multimedia information and stored in the multimedia information base to be recommended; according to the similarity between the first embedding vector b2 and 1000 second embedding vectors, selecting the identifiers corresponding to the first 50 second embedding vectors as recommended multimedia information and storing the recommended multimedia information into a to-be-recommended multimedia information base; according to the similarity between the first embedding vector b3 and 1000 second embedding vectors, selecting the identifiers corresponding to the first 50 second embedding vectors as recommended multimedia information and storing the recommended multimedia information into a to-be-recommended multimedia information base; according to the similarity between the first embedding vector b4 and 1000 second embedding vectors, selecting the identifiers corresponding to the first 50 second embedding vectors as recommended multimedia information and storing the recommended multimedia information into a to-be-recommended multimedia information base; then a total of 50 × 4 identifiers for the behavior like multimedia information are stored as the recommended multimedia information in the multimedia information library to be recommended. In summary, if the user behavior is a behavior of clicking the multimedia information and a behavior of liking the multimedia information, a total of 100 × 3+50 × 4-500 identifiers are stored as the recommended multimedia information in the multimedia information library to be recommended.
In the embodiment, 10000+ 1000-11000 pieces of selectable recommended multimedia information are reduced to 500 pieces of recommended multimedia information during recommendation, so that the data volume of a multimedia information base during recommendation can be greatly reduced, the recommendation speed is increased, the recommended multimedia information in the multimedia information base to be recommended is determined according to user behaviors obtained from a history log of a user account, and personalized recommendation can be realized for different user accounts.
In an optional embodiment, the identifier is a multimedia information identifier, and step S12 specifically includes:
step S121: for a multimedia information identifier of each piece of multimedia information corresponding to a user behavior, acquiring a first embedded vector of the corresponding multimedia information according to the multimedia information identifier, and acquiring a first embedded vector of an author according to an author identifier of the multimedia information corresponding to the multimedia information identifier, wherein the author identifier is an identifier corresponding to an author of the multimedia information corresponding to the multimedia information identifier.
Step S122: splicing the first embedded vector of the multimedia information and the first embedded vector of the author.
Wherein, the first embedded vector of the multimedia information may be spliced after or before the first embedded vector of the author at the time of splicing, which is not limited herein. For example, the first embedded vector of the multimedia information is [ c1, c2, c3, c4, c5, c6], the first embedded vector of the author is [ d1, d2, d3, d4, d5], and the spliced embedded vector thereof may be: [ c1, c2, c3, c4, c5, c6, d1, d2, d3, d4, d5], or [ d1, d2, d3, d4, d5, c1, c2, c3, c4, c5, c6 ]. Specifically, when the first embedded vector is a spliced embedded vector, the second embedded vector in the category user behavior information base corresponding to the first embedded vector is also a spliced embedded vector, and the splicing form of the second embedded vector is the same as that of the first embedded vector.
Example two
Fig. 2 is a block diagram of a multimedia information obtaining apparatus according to a second embodiment of the disclosure. The device can be integrated in a mobile terminal device (e.g., a smart phone, a tablet computer, etc.), a notebook computer or a fixed terminal (desktop computer), and the multimedia information acquisition device can be implemented by hardware or software. Referring to fig. 2, the apparatus includes an identification obtaining module 21, a vector obtaining module 22, a similarity calculation module 23, and a multimedia information base determination module 24; wherein the content of the first and second substances,
the identification obtaining module 21 is configured to obtain a user behavior list according to a history log of a current user account; the user behavior list comprises at least one identification which is associated with the multimedia information and corresponds to the user behavior, and the identification is used for marking the characteristics of at least one dimension of the multimedia information;
the vector obtaining module 22 is configured to obtain at least one first embedding vector according to the identifier associated with the at least one multimedia message; wherein one identifier corresponds to one first embedded vector;
the similarity calculation module 23 is configured to calculate a similarity between the first embedded vector and a second embedded vector stored in the user behavior information base, and rank the multimedia information associated with the second embedded vector according to the similarity;
the multimedia information base determining module 24 is configured to select a preset number of multimedia information as the to-be-recommended multimedia information corresponding to the current user account according to the sorting result.
Further, the identifier is a multimedia information identifier, and the vector obtaining module 22 is specifically configured to: for a multimedia information identifier of each piece of multimedia information corresponding to a user behavior, acquiring a first embedded vector of the corresponding multimedia information according to the multimedia information identifier, and acquiring a first embedded vector of an author according to an author identifier of the multimedia information corresponding to the multimedia information identifier, wherein the author identifier is an identifier corresponding to the author of the multimedia information corresponding to the multimedia information identifier; splicing the first embedded vector of the multimedia information and the first embedded vector of the author.
Furthermore, the second embedding vector is formed by splicing the embedding vector of the multimedia information stored in the user behavior information base and the embedding vector of the author of the stored multimedia information.
Further, the user behavior is a behavior of triggering a preset button, wherein the preset button is at least one of a button for clicking multimedia information, a button for liking the multimedia information, a button for browsing the multimedia information, and a button for paying attention to an author of the multimedia information.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
EXAMPLE III
Fig. 3 is a block diagram illustrating an apparatus 300 for multimedia information acquisition, according to an example embodiment. For example, the apparatus 300 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 3, the apparatus 300 may include one or more of the following components: a processing component 302, a memory 304, a power component 306, a multimedia component 308, an audio component 310, an input/output (I/O) interface 312, a sensor component 314, and a communication component 316.
The processing component 302 generally controls overall operation of the device 300, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 302 may include one or more processors 320 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 302 can include one or more modules that facilitate interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate interaction between the multimedia component 308 and the processing component 302.
The memory 304 is configured to store various types of data to support operations at the device 300. Examples of such data include instructions for any application or method operating on device 300, contact data, phonebook data, messages, pictures, multimedia information, and so forth. The memory 304 may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 306 provides power to the various components of the device 300. The power components 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 300.
The multimedia component 308 includes a screen that provides an output interface between the device 300 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the device 300 is in an operating mode, such as a photographing mode or a multimedia information mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 310 is configured to output and/or input audio signals. For example, audio component 310 includes a Microphone (MIC) configured to receive external audio signals when apparatus 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 304 or transmitted via the communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.
The I/O interface 312 provides an interface between the processing component 302 and peripheral interface modules, which may be keyboards, action wheels for clicking on information, buttons, and the like. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the device 300. For example, sensor assembly 314 may detect an open/closed state of device 300, the relative positioning of components, such as a display and keypad of apparatus 300, the change in position of apparatus 300 or a component of apparatus 300, the presence or absence of user contact with apparatus 300, the orientation or acceleration/deceleration of apparatus 300, and the change in temperature of apparatus 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 316 is configured to facilitate wired or wireless communication between the apparatus 300 and other devices. The apparatus 300 may access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G, or 5G), or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 300 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a storage medium comprising instructions, such as the memory 304 comprising instructions, executable by the processor 320 of the apparatus 300 to perform the method described above is also provided. Alternatively, the storage medium may be a non-transitory computer readable storage medium, which may be, for example, a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
In this scheme, all the user information (e.g., user personal information, user operation behavior information, user device information, etc.) involved is collected and subjected to subsequent processing or analysis by user authorization.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A multimedia information acquisition method is characterized by comprising the following steps:
acquiring a user behavior list according to a history log of a current user account; the user behavior list comprises an identifier associated with at least one piece of multimedia information corresponding to user behaviors, and the identifier is used for marking the characteristics of at least one dimension of the multimedia information;
obtaining at least one first embedding vector according to the identification associated with the at least one multimedia message; wherein one identifier corresponds to one first embedded vector;
calculating the similarity between the first embedded vector and a second embedded vector stored in a user behavior information base, and sequencing the multimedia information associated with the second embedded vector according to the similarity;
and selecting a preset number of multimedia information as the multimedia information to be recommended corresponding to the current user account according to the sorting result.
2. The method of claim 1, wherein the identifier is a multimedia information identifier, and wherein calculating the similarity between the first embedding vector and a second embedding vector stored in a user behavior information base and sorting multimedia information associated with the second embedding vector according to the similarity comprises:
for a multimedia information identifier of each piece of multimedia information corresponding to the user behavior, acquiring a first embedded vector of the corresponding multimedia information according to the multimedia information identifier, and acquiring a first embedded vector of an author according to an author identifier of the multimedia information corresponding to the multimedia information identifier, wherein the author identifier is an identifier corresponding to an author of the multimedia information corresponding to the multimedia information identifier;
splicing the first embedded vector of the multimedia information and the first embedded vector of the author.
3. The method of claim 2, wherein the second embedding vector is formed by splicing an embedding vector of the multimedia information stored in the user behavior information base and an embedding vector of an author of the stored multimedia information.
4. The method according to any one of claims 1 to 3, wherein the user behavior is a behavior of triggering a preset button, wherein the preset button is at least one of a button for clicking multimedia information, a button for liking multimedia information, a button for browsing multimedia information, and a button for focusing on an author of multimedia information.
5. A multimedia information acquisition apparatus, comprising:
the identification acquisition module is used for acquiring a user behavior list according to a historical log of a current user account; the user behavior list comprises an identifier associated with at least one piece of multimedia information corresponding to user behaviors, and the identifier is used for marking the characteristics of at least one dimension of the multimedia information;
the vector acquisition module is used for acquiring at least one first embedded vector according to the identifier associated with the at least one piece of multimedia information; wherein one identifier corresponds to one first embedded vector;
the similarity calculation module is used for calculating the similarity between the first embedded vector and a second embedded vector stored in a user behavior information base and sequencing the multimedia information associated with the second embedded vector according to the similarity;
and the multimedia information base determining module is used for selecting a preset number of multimedia information as the multimedia information to be recommended corresponding to the current user account according to the sorting result.
6. The apparatus of claim 5, wherein the identifier is a multimedia information identifier, and the vector obtaining module is specifically configured to: for a multimedia information identifier of each piece of multimedia information corresponding to the user behavior, acquiring a first embedded vector of the corresponding multimedia information according to the multimedia information identifier, and acquiring a first embedded vector of an author according to an author identifier of the multimedia information corresponding to the multimedia information identifier, wherein the author identifier is an identifier corresponding to an author of the multimedia information corresponding to the multimedia information identifier; splicing the first embedded vector of the multimedia information and the first embedded vector of the author.
7. The apparatus of claim 6, wherein the second embedding vector is formed by splicing an embedding vector of multimedia information stored in a user behavior information base and an embedding vector of an author of the stored multimedia information.
8. The apparatus according to any one of claims 5-7, wherein the user behavior is a behavior of triggering a preset button, wherein the preset button is at least one of a button for clicking multimedia information, a button for liking multimedia information, a button for browsing multimedia information, and a button for focusing on an author of multimedia information.
9. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions; wherein the processor is configured to: the multimedia information acquisition method according to any one of claims 1 to 4 is realized by executing instructions.
10. A non-transitory computer-readable storage medium having instructions therein, which when executed by a processor of a mobile terminal, enable the mobile terminal to perform the multimedia information acquisition method of any one of claims 1 to 4.
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