CN112165639A - Content distribution method, content distribution device, electronic equipment and storage medium - Google Patents

Content distribution method, content distribution device, electronic equipment and storage medium Download PDF

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Publication number
CN112165639A
CN112165639A CN202011009503.7A CN202011009503A CN112165639A CN 112165639 A CN112165639 A CN 112165639A CN 202011009503 A CN202011009503 A CN 202011009503A CN 112165639 A CN112165639 A CN 112165639A
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content
distributed
interaction
timeliness
information
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CN112165639B (en
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朱朝悦
衡阵
马连洋
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Tencent Technology Shenzhen Co Ltd
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Tencent Technology Shenzhen Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
    • H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
    • H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
    • H04N21/26208—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists the scheduling operation being performed under constraints
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
    • H04L65/60—Network streaming of media packets
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • H04L67/1004—Server selection for load balancing
    • H04L67/1014—Server selection for load balancing based on the content of a request
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4667—Processing of monitored end-user data, e.g. trend analysis based on the log file of viewer selections
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
    • H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
    • H04N21/84—Generation or processing of descriptive data, e.g. content descriptors

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Databases & Information Systems (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

The application discloses a content distribution method, a content distribution device, an electronic device and a storage medium, wherein the content distribution method comprises the following steps: acquiring associated information of contents to be distributed, wherein the associated information of the contents to be distributed comprises text description information corresponding to a plurality of contents to be distributed and content interaction information under a distribution account corresponding to the contents to be distributed; extracting the characteristics of the acquired content interaction information to obtain interaction characteristics corresponding to the acquired content interaction information; predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information; the content to be distributed is distributed based on the predicted content timeliness, the scheme can accurately push the content to be distributed to the target user within the validity period of the content timeliness, and the content distribution accuracy is improved.

Description

Content distribution method, content distribution device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a content distribution method and apparatus, an electronic device, and a storage medium.
Background
With the development of modern technologies, the way of media publishing information is more and more convenient. These media may register with an account on the network platform and then publish information, such as text information, audio information, and video information, based on the account. These media also include self-media, which refers to the way the general public publishes their own facts and news through the internet, etc. In recent years, the content creation is performed, all large internet companies actively enter a content market, various self-media are gushed out like bamboo shoots in spring after rain, and everyone can create the self-media by writing. A huge amount of articles are created every day by the media, but some content released by the media account may be copied from a media platform or the original content of the media account may be reprocessed and pieced together, so that the content distributed by the media account needs to be checked.
At present, a manual auditing scheme is adopted to audit the content issued by the self-media account, however, because the number of the self-media account is large and limited by manpower and auditing time, part of the content with high timeliness is not audited and may pass the time period of the audit, the content to be distributed cannot be accurately pushed to the target user in the validity period of the timeliness of the content in the existing content distribution scheme.
Disclosure of Invention
The application provides a content distribution method, a content distribution device, an electronic device and a storage medium, which can accurately push content to be distributed to a target user in the validity period of content timeliness, and improve the accuracy of content distribution.
The application provides a content distribution method, which comprises the following steps:
acquiring associated information of contents to be distributed, wherein the associated information of the contents to be distributed comprises text description information corresponding to a plurality of contents to be distributed and content interaction information under a distribution account corresponding to the contents to be distributed;
extracting the characteristics of the acquired content interaction information to obtain interaction characteristics corresponding to the acquired content interaction information;
predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information;
and performing content distribution on the content to be distributed based on the predicted content timeliness.
Correspondingly, the application also provides a content distribution device, which comprises:
the system comprises an acquisition module, a distribution module and a processing module, wherein the acquisition module is used for acquiring the associated information of the content to be distributed, and the associated information of the content to be distributed comprises text description information corresponding to a plurality of contents to be distributed and content interaction information under a distribution account corresponding to the content to be distributed;
the extraction module is used for extracting the characteristics of the acquired content interaction information to obtain interaction characteristics corresponding to the acquired content interaction information;
the prediction module is used for predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information;
and the distribution module is used for carrying out content distribution on the content to be distributed based on the predicted content timeliness.
Optionally, in some embodiments of the present application, the prediction module includes:
the first extraction submodule block is used for extracting the content label of the content to be distributed from the text description information;
the second extraction submodule is used for extracting the characteristics of the content tags to obtain tag characteristics corresponding to the content tags;
and the prediction submodule is used for predicting the content timeliness of the content to be distributed based on the semantics of the content label, the label characteristic and the interaction characteristic corresponding to the acquired content interaction information.
Optionally, in some embodiments of the present application, the summon prediction sub-module includes:
the word segmentation unit is used for segmenting the label text corresponding to the content label based on the semantic meaning of the content label;
the segmentation unit is used for segmenting the label features according to word segmentation results to obtain element features corresponding to each text element in the label text;
and the predicting unit is used for predicting the content timeliness of the content to be distributed based on the element characteristics corresponding to each text element in the label text and the interaction characteristics corresponding to the acquired content interaction information.
Optionally, in some embodiments of the present application, the prediction unit includes:
the obtaining subunit is used for obtaining a preset timeliness prediction model;
and the predicting subunit is used for predicting the content timeliness of the content to be distributed through the timeliness prediction model based on the element characteristics corresponding to each text element in the label text and the interaction characteristics corresponding to the acquired content interaction information.
Optionally, in some embodiments of the present application, the prediction subunit is specifically configured to:
performing feature combination on interaction features corresponding to the acquired content interaction information to obtain combination features;
predicting the user attention degree of the content to be distributed in different preset time periods according to the combined characteristics to obtain a first attention degree corresponding to each preset time period;
predicting the user attention degree of the content to be distributed in different preset time periods according to the element characteristics corresponding to each text element in the label text to obtain a second attention degree corresponding to each preset time period;
and fusing the first attention and the second attention corresponding to each preset time period to obtain the content timeliness of the content to be distributed in each preset time period.
Optionally, in some embodiments of the present application, the apparatus further includes an identification module, where the identification module is specifically configured to:
identifying an intent of the collected content interaction information;
determining the interaction information corresponding to the intention as the forward intention as forward interaction information according to the intention identification result;
determining the interactive information corresponding to the intention as the negative interactive information;
reserving the forward interactive information;
the prediction module is specifically configured to: and predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the forward interaction information.
Optionally, in some embodiments of the present application, the distribution module is specifically configured to:
sequencing the contents to be distributed in the associated information of the contents to be distributed based on the predicted content timeliness to obtain a content list to be distributed;
and distributing the contents to be distributed in the content list to be distributed according to the sequence of the contents to be distributed in the content list to be distributed.
After the relevant information of the content to be distributed is collected, the relevant information of the content to be distributed comprises a plurality of text description information corresponding to the content to be distributed, the content interaction information under a distribution account corresponding to the content to be distributed is subjected to feature extraction, interaction features corresponding to the collected content interaction information are obtained, then, according to the semantics of the text description information and the interaction features corresponding to the collected content interaction information, the content timeliness of the content to be distributed is predicted, and finally, the content to be distributed is subjected to content distribution based on the predicted content timeliness. Therefore, the scheme can accurately push the content to be distributed to the target user in the validity period of the content timeliness, and the content distribution accuracy is improved.
Drawings
In order to more clearly illustrate the technical solutions in the present application, the drawings needed to be used in the description of the embodiments will be briefly introduced 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 to obtain other drawings based on these drawings without creative efforts.
Fig. 1a is a scene schematic diagram of a content distribution method provided in the present application;
FIG. 1b is a schematic flow chart of a content distribution method provided herein;
FIG. 1c is a schematic diagram of a temporal prediction model provided herein;
FIG. 2a is another schematic flow chart of a content distribution method provided herein;
FIG. 2b is another schematic diagram of a time-dependent predictive model provided herein;
fig. 3a is a schematic structural diagram of a content distribution apparatus provided in the present application;
fig. 3b is another schematic structural diagram of a content distribution apparatus provided in the present application;
fig. 4 is a schematic structural diagram of an electronic device provided in the present application.
Detailed Description
The technical solutions in the present application will be described clearly and completely with reference to the accompanying drawings in the present application, 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.
Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.
The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Natural Language Processing (NLP) is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that enable efficient communication between humans and computers using natural language. Natural language processing is a science integrating linguistics, computer science and mathematics. Therefore, the research in this field will involve natural language, i.e. the language that people use everyday, so it is closely related to the research of linguistics. Natural language processing techniques typically include text processing, semantic understanding, machine translation, robotic question and answer, knowledge mapping, and the like.
Machine Learning (ML) is a multi-domain cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach for computers to have intelligence, and is applied to all fields of artificial intelligence. Machine learning and deep learning generally include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and formal education learning.
With the research and progress of artificial intelligence technology, the artificial intelligence technology is developed and applied in a plurality of fields, such as common smart homes, smart wearable devices, virtual assistants, smart speakers, smart marketing, unmanned driving, automatic driving, unmanned aerial vehicles, robots, smart medical care, smart customer service, and the like.
The scheme provided by the embodiment of the application relates to technologies such as artificial intelligence natural language processing and machine learning, and is specifically explained by the following embodiment.
The application provides a content distribution method, a content distribution device, an electronic device and a storage medium.
The content delivery device may be specifically integrated in a server, the server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, middleware service, a domain name service, a security service, a CDN, a big data and artificial intelligence platform, and the like. The terminal may be, but is not limited to, a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.
For example, referring to fig. 1a, the content distribution apparatus is integrated on a server, and the server collects associated information of content to be distributed, where the associated information of the content to be distributed includes text description information corresponding to a plurality of content to be distributed and content interaction information under a distribution account corresponding to the content to be distributed, the text description information is text information that can be used to describe a type of the content to be distributed, such as a title of the content to be distributed, and the like, the content interaction information may be information generated after a user performs an interaction with the distribution account within a historical period, such as a approval of the content distributed under the distribution account, and then the server may perform feature extraction on the collected content interaction information to obtain an interaction feature corresponding to the collected content interaction information, and then, according to semantics of the text description information and the interaction feature corresponding to the collected content interaction information, and predicting the content timeliness of the content to be distributed, and finally, the server distributes the content to be distributed based on the predicted content timeliness.
The content distribution method can predict the content timeliness of the content to be distributed according to the content interaction information and the text description information, predict the content timeliness of the content to be distributed through different dimensions, and avoid the problem that the predicted content timeliness is inaccurate due to the fact that the content timeliness of the content to be distributed is predicted by using information of a single dimension.
The following are detailed below. It should be noted that the description sequence of the following embodiments is not intended to limit the priority sequence of the embodiments.
A content distribution method, comprising: acquiring the associated information of the content to be distributed, performing feature extraction on the acquired content interaction information to obtain the interaction features corresponding to the acquired content interaction information, predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction features corresponding to the acquired content interaction information, and performing content distribution on the content to be distributed based on the predicted content timeliness.
Referring to fig. 1b, fig. 1b is a schematic flow chart of a content distribution method provided in the present application. The specific flow of the content distribution method may be as follows:
101. and collecting the associated information of the content to be distributed.
The association information of the content to be distributed comprises text description information corresponding to a plurality of contents to be distributed and content interaction information under a distribution account corresponding to the content to be distributed. It is understood that each content to be distributed has corresponding text description information, and the text description information may include a content text corresponding to the content to be distributed, a content title of the content to be distributed, and a classification tag of the content to be distributed. In the present application, content titles of content to be distributed and classification labels of the content to be distributed are collectively referred to as content labels, a distribution account refers to an account authenticated by a content distribution system (also referred to as a content distribution platform), the distribution account is an account having a content distribution function, and the distribution account may be a self-media account. The textual description information may be obtained in a direct or indirect manner. Specifically, the method includes directly obtaining direct text presentation content such as a title, a label and the like of the content to be distributed; for example, the text corresponding to the caption and the background voice in the video or audio is indirectly acquired through picture recognition, voice recognition and the like.
It can be understood that the self Media (We Media) is a generic term of a new Media that is personalized and independent, and delivers normative and non-normative information to an unspecified majority or a specific individual by means of modernization and electronization, the self Media account may be an account (such as a microblog account) that is registered in an independent content distribution platform and can issue content autonomously, or an account that is registered in a content distribution platform integrated in a social platform and can issue content autonomously, the content interaction information is information generated after a user interacts with a distribution account within a historical period, for example, the user approves content issued under the distribution account, or for example, the user forwards content issued under the distribution account, and the content interaction information records the time when the user interacts with the distribution account, Operation and the targeted distribution account number and other information.
Here, the definition of the timeliness of the content is explained, and the distribution content has a certain effect in a period of time, and the effect is measured according to the interest degree of the user in the distribution content. Generally, timeliness refers to that videos are pushed to a user within a time range and are not overdue, user retention, clicking and clicking passing rates on end lines are all important in timeliness, content is pushed to the user within the content timeliness validity range to play a forward role, and otherwise user discomfort can be caused
102. And extracting the characteristics of the acquired content interaction information to obtain interaction characteristics corresponding to the acquired content interaction information.
For a computer, the information needs to be converted into information that can be identified and processed by a structured computer, that is, the information is scientifically abstracted, and a mathematical model of the information is established to describe and replace the information.
For different types of behavior information, the adopted feature extraction modes may be different, but the concepts are consistent, for example, comment behavior information is taken as an example, currently, comments of users for distribution content are generally embodied in a text form, in the application, a vector space model can be used for describing text vectors, but if feature items obtained by a word segmentation algorithm and a word frequency statistical method are directly used for representing each dimension in the text vectors, the dimension of the vectors is very large. The unprocessed text vector not only brings huge calculation cost to subsequent work, so that the efficiency of the whole processing process is very low, but also can damage the accuracy of a classification and clustering algorithm, so that the obtained result is difficult to satisfy. Therefore, the text vector must be further purified, and the most representative text features of the text feature categories are found on the basis of ensuring the meaning of the original text. To solve this problem, the most effective approach is to reduce the dimension by feature selection. There are 4 common:
(1) the original features are transformed into fewer new features by a mapping or transformation method.
(2) Some of the most representative features are selected from the original features.
(3) And selecting the most influential features according to the prior knowledge.
(4) The method is an accurate method, interference of human factors is less, the method is particularly suitable for application of a text automatic classification mining system, selection is carried out according to actual conditions, and the method is not limited.
103. And predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information.
The content to be distributed may be one or more of an image, a text, a voice, and a video, and therefore, in order to enable the user to know the domain or the type of the distributed content, the distribution account generally adds a corresponding content tag to the distributed content, where the content tag includes a content title of the content to be distributed and a classification tag of the content to be distributed.
For the content to be distributed with more characters, such as a text, the content text of the content to be distributed can be extracted, and the content timeliness of the content to be distributed is predicted by combining the semantics of text description information and the interaction characteristics corresponding to the acquired content interaction information; for contents to be distributed with less text information such as images, voice and videos, taking videos as an example, semantic features available for video titles and video labels are not rich, and it is generally difficult to accurately judge video timeliness through the video titles and video labels, so accuracy of subsequent directional delivery is affected. Therefore, in the present application, the content timeliness of the content to be distributed is predicted by using the semantics of the text description information and the interaction feature corresponding to the collected content interaction information, and optionally, in some embodiments, the step "predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction feature corresponding to the collected content interaction information" may specifically include:
(11) extracting a content label of the content to be distributed from the text description information;
(12) extracting the characteristics of the content label to obtain the label characteristics corresponding to the content label;
(13) and predicting the content timeliness of the content to be distributed based on the semantics and the label characteristics of the content label and the interaction characteristics corresponding to the acquired content interaction information.
Wherein, the elements in the label text can be single words or phrases, the label features can be regarded as the features of the label text corresponding to the content label, which is more concerned about the whole label text itself, for the content delivery system, in order to improve the accuracy of the subsequent content delivery, each element of the label text is also required to be generalized, on one hand, the limitation of human thinking dimension caused by artificial feature extraction can be made up, on the other hand, the high latitude dense vector can be converted into the low latitude dense vector, thereby reducing the calculation cost and further improving the calculation rate, specifically, the label features can be segmented according to the semantics of the label text, then, the timeliness of the content to be distributed is predicted based on the separated features and the interactive features corresponding to the collected content interactive information, namely, optional, the method comprises the following steps of predicting the content timeliness of the content to be distributed based on the semantics of the content label, the label characteristics and the interaction characteristics corresponding to the acquired content interaction information, and specifically comprises the following steps:
(21) segmenting a label text corresponding to the content label based on the semantic meaning of the content label;
(22) segmenting the label features according to the word segmentation result to obtain element features corresponding to each text element in the label text;
(23) and predicting the content timeliness of the content to be distributed based on the element characteristics corresponding to each text element in the label text and the interaction characteristics corresponding to the acquired content interaction information.
Specifically, a preset timeliness prediction model can be obtained, and similar to the traditional search, a challenge of the content delivery system is how to obtain the accuracy and the expansibility of a delivery result at the same time. Recommended contents are accurate contents, the interest of the user is convergent, freshness is avoided, and the method is not beneficial to long-term user retention; the recommended content is too generalized, the accurate interest of the user cannot be met, and the user loss risk is large, so that the timeliness prediction model can be a wide & deep model in the application. Referring to fig. 1c, the timeliness prediction model includes a linear model and a depth model, where the linear model is used to process the interaction features, the depth model is used to process the element features corresponding to each text element, and finally, the two output results are spliced to output the content timeliness of the content to be distributed, and the linear model can efficiently realize the memory capacity by using the cross features to achieve the purpose of accurate classification. Linear models achieve some generalization capability by adding some broad class features. However, linear models, limited by the training data, cannot achieve generalization that has not occurred in the training data. In the depth model, the generalization capability of the model can be realized through the learned low-latitude dense vector, and further the generalization recommendation of unseen contents is realized. When the matrix of the model is sparse, the model can be excessively generalized, a lot of content without correlation is recommended, and the accuracy cannot be guaranteed.
It should be noted that the content interaction information may include comments of a plurality of users for the same content, and the relationship between the comments is not a linear relationship, and in the present application, the content interaction information is processed through a linear model, so, in order to solve the above-mentioned non-linear problem, the interaction features corresponding to the collected content interaction information may be combined, the feature combination refers to a composite feature that encodes a non-linear law in a feature space by multiplying two or more input features, then, the degree of user attention of the content to be distributed in different preset time periods is predicted based on the composite feature, a first degree of attention corresponding to each preset time period is obtained, and meanwhile, the degree of user attention of the content to be distributed in different preset time periods is predicted according to the element features corresponding to each text element in the tag text, obtaining a second degree of interest corresponding to each preset time period, and finally, fusing the first degree of interest and the second degree of interest corresponding to the same preset time period to obtain content timeliness of the content to be distributed in each preset time period, that is, optionally, in some embodiments, the step "predicting the content timeliness of the content to be distributed through a timeliness prediction model based on element features corresponding to each text element in the tag text and interaction features corresponding to the acquired content interaction information" may specifically include:
(31) performing feature combination on interaction features corresponding to the acquired content interaction information to obtain combination features;
(32) predicting the user attention degree of the content to be distributed in different preset time periods according to the combined characteristics to obtain a first attention degree corresponding to each preset time period;
(33) predicting the user attention degree of the content to be distributed in different preset time periods according to the element characteristics corresponding to each text element in the label text to obtain a second attention degree corresponding to each preset time period;
(34) and fusing the first attention and the second attention corresponding to each preset time period to obtain the content timeliness of the content to be distributed in each preset time period.
As mentioned above, the content timeliness is measured by the interest (i.e. attention) of the user in the distributed content, and therefore, the user attention degree of the content to be distributed in different preset time periods is predicted according to the combined features, the user attention degree of the content to be distributed in different preset time periods is predicted according to the element feature corresponding to each text element in the tag text, and finally, the two predicted attention degrees are fused to obtain the content timeliness of the content to be distributed, where the preset time periods may be determined according to actual situations, for example, taking day as a unit, and the size of one preset time period is 3 days, and then the content timeliness of the content to be distributed in 3 days, 6 days in the future, 9 days in the future, and so on can be predicted.
In addition, for text processing in natural language processing, Machine Learning (ML) technology is generally used to implement text processing. The machine learning is a multi-field cross subject and relates to a plurality of subjects such as probability theory, statistics, approximation theory, convex analysis and algorithm complexity theory. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach for computers to have intelligence, and is applied to all fields of artificial intelligence. Machine learning and deep learning generally include technologies such as artificial neural network, belief network, reinforcement learning, transfer learning, inductive learning, and formula learning, in which Natural Language Processing (NLP) is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that enable efficient communication between humans and computers using natural language. Natural language processing is a science integrating linguistics, computer science and mathematics. Therefore, the research in this field will involve natural language, i.e. the language that people use everyday, so it is closely related to the research of linguistics. Natural language processing techniques typically include text processing, semantic understanding, machine translation, robotic question and answer, and knowledge-mapping techniques.
It can be understood that the content interaction information may include content interaction information corresponding to a positive interaction behavior and content interaction information corresponding to a negative interaction behavior, where the positive interaction behavior refers to a positive interaction behavior, such as collection, forwarding, sharing praise, and positive comments for the content, and the negative interaction behavior refers to a negative interaction behavior, such as reporting and negative comments for the content, in order to improve accuracy of subsequent delivery, in some embodiments of the present application, the negative interaction information may be removed, so as to avoid an influence of the negative interaction information on subsequent delivery, that is, the content delivery method provided by the present application may specifically include:
(41) identifying an intent of the collected content interaction information;
(42) according to the intention identification result, reserving the interaction information corresponding to the intention which is a forward intention;
the method comprises the following steps of predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information, and specifically comprises the following steps: and predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the interaction information with the intention being the forward intention.
104. And performing content distribution on the content to be distributed based on the predicted content timeliness.
For example, specifically, the content to be distributed may be sorted according to the predicted content timeliness, and then, the content distribution may be performed on the content to be distributed based on the sorted order, that is, optionally, in some embodiments, the step "content distribution is performed on the content to be distributed based on the predicted content timeliness", specifically, the step may include:
(51) sequencing the contents to be distributed in the contents to be distributed based on the predicted content timeliness to obtain a content list to be distributed;
(52) and distributing the contents to be distributed in the content list to be distributed according to the sequence of the contents to be distributed in the content list to be distributed.
It should be noted that, in practical applications, there may be a case where the content timeliness of the content a to be distributed and the content B to be distributed are the same, for example, the content timeliness of the content a to be distributed and the content B to be distributed are both 3 days, the field to which the content a to be distributed belongs is a news information class, the field to which the content B to be distributed belongs is a science popularization course class, and at this time, the content delivery of the content a to be distributed may be preferentially performed according to the difference in the fields; for another example, the timeliness of the content a to be distributed and the content B to be distributed are both 3 days, and the fields to which the content a to be distributed and the content B to be distributed belong are both popular science courses, at this time, the content to be distributed with a high click rate may be delivered preferentially, and of course, the content to be distributed may be delivered according to other strategies, which is only described by way of example and is not limited to the present application.
According to the method and the device, after the associated information of the content to be distributed is collected, the collected content interaction information is subjected to feature extraction, the interaction feature corresponding to the collected content interaction information is obtained, then the content timeliness of the content to be distributed is predicted according to the semantics of the text description information and the interaction feature corresponding to the collected content interaction information, and finally the content to be distributed is distributed based on the predicted content timeliness. The content distribution method can predict the content timeliness of the content to be distributed according to the content interaction information and the text description information, predict the content timeliness of the content to be distributed through different dimensions, and avoid the problem that the predicted content timeliness is inaccurate due to the fact that the content timeliness of the content to be distributed is predicted by using information of a single dimension.
The method according to the examples is further described in detail below by way of example.
In the present embodiment, the content distribution apparatus will be described by taking an example in which the content distribution apparatus is specifically integrated in a server.
Referring to fig. 2a, a content distribution method may specifically include the following processes:
201. the server collects the associated information of the content to be distributed.
The server may acquire the associated information of the content to be distributed uploaded by the distribution account through network connection.
202. And the server extracts the characteristics of the acquired content interaction information to obtain interaction characteristics corresponding to the acquired content interaction information.
203. And the server predicts the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information.
Specifically, the server may segment the tag feature according to the semantics of the tag text, predict the content timeliness of the content to be distributed based on the segmented feature and the interaction feature corresponding to the acquired content interaction information, further, the server may obtain a preset timeliness prediction model, then perform feature combination on the interaction feature corresponding to the acquired content interaction information to obtain a combination feature, then predict the user attention degree of the content to be distributed at different preset time intervals according to the combination feature to obtain a first attention degree corresponding to each preset time interval, and predict the user attention degree of the content to be distributed at different preset time intervals according to the element feature corresponding to each text element in the tag text to obtain a second attention degree corresponding to each preset time interval, and finally, the server fuses the first attention degree and the second attention degree corresponding to each preset time interval, and obtaining the content timeliness of the content to be distributed in each preset time period.
204. And the server distributes the content to be distributed based on the predicted content timeliness.
For example, specifically, the server may sort the content to be distributed according to the predicted timeliness of the content, and then distribute the content to be distributed based on the sorted order.
To facilitate a further understanding of the content distribution scheme of the present application, please refer to fig. 2b, which illustrates an architecture of a time-dependent prediction model, and the wide & deep model proposed in the present application balances the memory capability and generalization capability of the linear model and the depth model, where y is WX + b. X is a feature portion that includes a base feature and a cross feature. The cross feature is important in the linear part, and can capture the interaction between features to play a role in adding nonlinearity. The cross-over feature can be expressed as:
Figure BDA0002697097980000141
the deep portion is a feed forward network model. The features are first converted to low-dimensional dense vectors, the dimensions typically being between O (10) and O (100). Vectors are initialized randomly and the model is trained as a function of time with minimization. The activation function uses Relu. The feed forward part is represented as follows:
a(l+1)＝f(W(l)a(l)+b(l))
the outputs of the Wide and Deep portions are combined together in a weighted manner and finally output through a logistic loss function, which can be expressed as follows:
Figure BDA0002697097980000142
taking content to be distributed as a video as an example, in a training stage, a server acquires sample video information, wherein the sample video information comprises text description information of the sample video and content interaction information under a corresponding distribution account, the sample video is labeled with real content timeliness, then the server performs feature extraction on the acquired content interaction information to obtain interaction features corresponding to the acquired content interaction information, then the server extracts a video title and a classification label of the sample video from the text description information and performs feature extraction on the video title and the classification label respectively to obtain a title feature of the video title and label features corresponding to the classification label, then the server inputs the title feature and the label features into a depth model of a timeliness prediction model and inputs the interaction features corresponding to the content interaction information into a linear model of the timeliness prediction model, the method comprises the steps that the linear model is used for carrying out feature combination on collected interactive features to obtain combined features, then the server uses the linear model to process the combined features and the interactive features to obtain the user attention degree of a sample video in different preset time periods; in addition, the server processes the title feature and the label feature of the depth model to obtain the user attention degree of the sample video at different preset time periods, wherein the title feature and the label feature can be subjected to embedded vectorization processing respectively, and the parameter of the depth model is updated according to the final loss function; the using stage is the same as that of the previous embodiment, and specific reference is made to the previous embodiment, which is not described herein again.
According to the method and the device, after the server collects the associated information of the content to be distributed, the server extracts the characteristics of the collected content interaction information to obtain the interaction characteristics corresponding to the collected content interaction information, then, the server predicts the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the collected content interaction information, and finally, the server distributes the content to be distributed based on the predicted content timeliness. The server can predict the content timeliness of the content to be distributed according to the content interaction information and the text description information, predict the content timeliness of the content to be distributed through different dimensions, and avoid the problem that the predicted content timeliness is inaccurate due to the fact that the content timeliness of the content to be distributed is predicted through information of a single dimension, therefore, the scheme can accurately push the content to be distributed to a target user within the validity period of the content timeliness, and the content distribution accuracy is improved.
In order to better implement the content distribution method of the present application, the present application further provides a content distribution apparatus (distribution apparatus for short) based on the foregoing content distribution method. Wherein the noun has the same meaning as in the content distribution method described above, and the details of the implementation can be referred to the description in the method embodiment.
Referring to fig. 3a, fig. 3a is a schematic structural diagram of a content distribution apparatus provided in the present application, where the distribution apparatus may include an acquisition module 301, an extraction module 302, a prediction module 303, and a distribution module 304, which may specifically be as follows:
the acquisition module 301 is configured to acquire associated information of content to be distributed.
The relevant information of the content to be distributed includes text description information corresponding to a plurality of contents to be distributed and content interaction information under a distribution account corresponding to the contents to be distributed, it can be understood that each content to be distributed has corresponding text description information, the text description information may include a content text corresponding to the content to be distributed, a content title of the content to be distributed, and a classification tag of the content to be distributed, and the acquisition module 301 may acquire the relevant information of the content to be distributed uploaded by the distribution account through network connection.
The extracting module 302 is configured to perform feature extraction on the acquired content interaction information to obtain an interaction feature corresponding to the acquired content interaction information.
The prediction module 303 is configured to predict the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction features corresponding to the acquired content interaction information.
For example, specifically, the prediction module 303 predicts the content timeliness of the content to be distributed by using the semantics of the text description information and the interaction features corresponding to the collected content interaction information, that is, optionally, in some embodiments, the prediction module 303 may specifically include:
the first extraction submodule block is used for extracting a content label of the content to be distributed from the text description information;
the second extraction submodule is used for extracting the characteristics of the content tags to obtain tag characteristics corresponding to the content tags;
and the prediction submodule is used for predicting the content timeliness of the content to be distributed based on the semantics of the content label, the label characteristic and the interaction characteristic corresponding to the acquired content interaction information.
Optionally, in some embodiments, the invoking the prediction sub-module may specifically include:
the word segmentation unit is used for segmenting the label text corresponding to the content label based on the semantic meaning of the content label;
the segmentation unit is used for segmenting the label features according to the word segmentation result to obtain the element features corresponding to each text element in the label text;
and the prediction unit is used for predicting the content timeliness of the content to be distributed based on the element characteristics corresponding to each text element in the label text and the interaction characteristics corresponding to the acquired content interaction information.
Optionally, in some embodiments, the prediction unit may specifically include:
the obtaining subunit is used for obtaining a preset timeliness prediction model;
and the predicting subunit is used for predicting the content timeliness of the content to be distributed through the timeliness prediction model based on the element characteristics corresponding to each text element in the label text and the interaction characteristics corresponding to the acquired content interaction information.
Optionally, in some embodiments, the prediction subunit may specifically be configured to: the method comprises the steps of carrying out feature combination on interaction features corresponding to collected content interaction information to obtain combination features, predicting the user attention degree of the content to be distributed in different preset time periods according to the combination features to obtain a first attention degree corresponding to each preset time period, predicting the user attention degree of the content to be distributed in different preset time periods according to the element features corresponding to each text element in a label text to obtain a second attention degree corresponding to each preset time period, and fusing the first attention degree and the second attention degree corresponding to each preset time period to obtain the content timeliness of the content to be distributed in each preset time period.
Optionally, in some embodiments, referring to fig. 3b, the distribution apparatus may further specifically include an identification module 305, and the identification module 305 may specifically be configured to: identifying the intention of the acquired content interaction information, and keeping the intention as the interaction information corresponding to the forward intention according to the intention identification result;
the prediction module 303 may specifically be configured to: and predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the forward interaction information.
And the distribution module 304 is used for distributing the content to be distributed based on the predicted timeliness of the content.
For example, specifically, the distribution module 304 may sort the content to be distributed according to the predicted content timeliness, and then the distribution module 304 performs content distribution on the content to be distributed based on the arranged order, and optionally, in some embodiments, the distribution module 304 may specifically be configured to: and sequencing the contents to be distributed in the contents to be distributed based on the predicted content timeliness to obtain a content list to be distributed, and distributing the contents to be distributed in the content list to be distributed according to the sequence of the contents to be distributed in the content list to be distributed.
After the acquisition module 301 acquires the associated information of the content to be distributed, the extraction module 302 performs feature extraction on the acquired content interaction information to obtain the interaction features corresponding to the acquired content interaction information, then the prediction module 303 predicts the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction features corresponding to the acquired content interaction information, and finally, the distribution module 304 performs content distribution on the associated information of the content to be distributed based on the predicted content timeliness. The content distribution device can predict the content timeliness of the content to be distributed according to the content interaction information and the text description information, predict the content timeliness of the content to be distributed through different dimensions, and avoid the problem that the predicted content timeliness is inaccurate due to the fact that the content timeliness of the content to be distributed is predicted by using information of a single dimension.
In addition, the present application also provides an electronic device, as shown in fig. 4, which shows a schematic structural diagram of the electronic device related to the present application, specifically:
the electronic device may include components such as a processor 401 of one or more processing cores, memory 402 of one or more computer-readable storage media, a power supply 403, and an input unit 404. Those skilled in the art will appreciate that the electronic device configuration shown in fig. 4 does not constitute a limitation of the electronic device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components. Wherein:
the processor 401 is a control center of the electronic device, connects various parts of the whole electronic device by various interfaces and lines, performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 402 and calling data stored in the memory 402, thereby performing overall monitoring of the electronic device. Optionally, processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 401.
The memory 402 may be used to store software programs and modules, and the processor 401 executes various functional applications and data processing by operating the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 access to the memory 402.
The electronic device further comprises a power supply 403 for supplying power to the various components, and preferably, the power supply 403 is logically connected to the processor 401 through a power management system, so that functions of managing charging, discharging, and power consumption are realized through the power management system. The power supply 403 may also include any component of one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
The electronic device may further include an input unit 404, and the input unit 404 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
Although not shown, the electronic device may further include a display unit and the like, which are not described in detail herein. Specifically, in this embodiment, the processor 401 in the electronic device loads the executable file corresponding to the process of one or more application programs into the memory 402 according to the following instructions, and the processor 401 runs the application program stored in the memory 402, thereby implementing various functions as follows:
acquiring the associated information of the content to be distributed, performing feature extraction on the acquired content interaction information to obtain the interaction features corresponding to the acquired content interaction information, predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction features corresponding to the acquired content interaction information, and performing content distribution on the content to be distributed based on the predicted content timeliness.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
According to the method and the device, after the associated information of the content to be distributed is collected, the collected content interaction information is subjected to feature extraction, the interaction feature corresponding to the collected content interaction information is obtained, then the content timeliness of the content to be distributed is predicted according to the semantics of the text description information and the interaction feature corresponding to the collected content interaction information, and finally the content to be distributed is distributed based on the predicted content timeliness. The content distribution method can predict the content timeliness of the content to be distributed according to the content interaction information and the text description information, predict the content timeliness of the content to be distributed through different dimensions, and avoid the problem that the predicted content timeliness is inaccurate due to the fact that the content timeliness of the content to be distributed is predicted by using information of a single dimension.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, the present application provides a storage medium having stored therein a plurality of instructions that can be loaded by a processor to perform the steps of any of the content distribution methods provided herein. For example, the instructions may perform the steps of:
acquiring the associated information of the content to be distributed, performing feature extraction on the acquired content interaction information to obtain the interaction features corresponding to the acquired content interaction information, predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction features corresponding to the acquired content interaction information, and performing content distribution on the content to be distributed based on the predicted content timeliness.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the storage medium can execute the steps in any content distribution method provided by the present application, the beneficial effects that can be achieved by any content distribution method provided by the present application can be achieved, for details, see the foregoing embodiments, and are not described herein again.
According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in the various alternative implementations described above.
The content distribution method, device, electronic device and storage medium provided by the present application are described in detail above, and a specific example is applied in the present application to explain the principle and the implementation of the present invention, and the description of the above embodiment is only used to help understanding the method and the core idea of the present invention; meanwhile, for those skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A content distribution method, comprising:
acquiring associated information of contents to be distributed, wherein the associated information of the contents to be distributed comprises text description information corresponding to a plurality of contents to be distributed and content interaction information under a distribution account corresponding to the contents to be distributed;
extracting the characteristics of the acquired content interaction information to obtain interaction characteristics corresponding to the acquired content interaction information;
predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information;
and performing content distribution on the content to be distributed based on the predicted content timeliness.
2. The method according to claim 1, wherein the predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction features corresponding to the collected content interaction information comprises:
extracting a content label of the content to be distributed from the text description information;
performing feature extraction on the content label to obtain a label feature corresponding to the content label;
and predicting the content timeliness of the content to be distributed based on the semantics and the label characteristics of the content label and the interaction characteristics corresponding to the acquired content interaction information.
3. The method according to claim 2, wherein the predicting the content timeliness of the content to be distributed based on the semantics of the content tag, the tag characteristics and the interaction characteristics corresponding to the collected content interaction information comprises:
based on the semantics of the content label, performing word segmentation on a label text corresponding to the content label;
segmenting the label features according to word segmentation results to obtain element features corresponding to each text element in the label text;
and predicting the content timeliness of the content to be distributed based on the element characteristics corresponding to each text element in the label text and the interaction characteristics corresponding to the acquired content interaction information.
4. The method according to claim 3, wherein predicting the content timeliness of the content to be distributed based on the element features corresponding to each text element in the tag text and the interaction features corresponding to the collected content interaction information comprises:
acquiring a preset timeliness prediction model;
and predicting the content timeliness of the content to be distributed through the timeliness prediction model based on the element characteristics corresponding to each text element in the label text and the interaction characteristics corresponding to the acquired content interaction information.
5. The method according to claim 4, wherein the predicting the content timeliness of the content to be distributed through the timeliness prediction model based on the element features corresponding to each text element in the tag text and the interaction features corresponding to the collected content interaction information comprises:
performing feature combination on interaction features corresponding to the acquired content interaction information to obtain combination features;
predicting the user attention degree of the content to be distributed in different preset time periods according to the combined characteristics to obtain a first attention degree corresponding to each preset time period;
predicting the user attention degree of the content to be distributed in different preset time periods according to the element characteristics corresponding to each text element in the label text to obtain a second attention degree corresponding to each preset time period;
and fusing the first attention and the second attention corresponding to each preset time period to obtain the content timeliness of the content to be distributed in each preset time period.
6. The method of claim 1, further comprising:
identifying an intent of the collected content interaction information;
according to the intention identification result, reserving the interaction information corresponding to the intention which is a forward intention;
predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information, wherein the predicting comprises the following steps: and predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the interaction information with the intention being the forward intention.
7. The method according to any one of claims 1 to 6, wherein the content distribution of the content to be distributed based on the predicted content timeliness comprises:
sequencing the contents to be distributed in the contents to be distributed based on the predicted content timeliness to obtain a content list to be distributed;
and distributing the contents to be distributed in the content list to be distributed according to the sequence of the contents to be distributed in the content list to be distributed.
8. A content distribution apparatus, characterized by comprising:
the system comprises an acquisition module, a distribution module and a processing module, wherein the acquisition module is used for acquiring the associated information of the content to be distributed, and the associated information of the content to be distributed comprises text description information corresponding to a plurality of contents to be distributed and content interaction information under a distribution account corresponding to the content to be distributed;
the extraction module is used for extracting the characteristics of the acquired content interaction information to obtain interaction characteristics corresponding to the acquired content interaction information;
the prediction module is used for predicting the content timeliness of the content to be distributed according to the semantics of the text description information and the interaction characteristics corresponding to the acquired content interaction information;
and the distribution module is used for carrying out content distribution on the content to be distributed based on the predicted content timeliness.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the content distribution method according to any of claims 1-7 are implemented when the program is executed by the processor.
10. A computer-readable storage medium, having stored thereon a computer program, wherein the computer program, when being executed by a processor, is adapted to carry out the steps of the content distribution method according to any one of claims 1 to 7.
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CN114330295A (en) * 2021-08-04 2022-04-12 腾讯科技（深圳）有限公司 Time efficiency identification, model training and pushing method, device and medium of information
CN115730111A (en) * 2021-09-01 2023-03-03 腾讯科技（深圳）有限公司 Content distribution method, device, equipment and computer readable storage medium
CN115730111B (en) * 2021-09-01 2024-02-06 腾讯科技（深圳）有限公司 Content distribution method, apparatus, device and computer readable storage medium

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