CN116595258A - Content recommendation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to an artificial intelligence technology, and discloses a content recommendation method for medical study video recommendation, which comprises the following steps: acquiring historical behavior data of a user and a user identifier; when the user identifier is a new user, acquiring a content heat value of each content to be recommended in a preset content set to be recommended, screening the content set to be recommended by using the content heat value to obtain a first screening result, and sending the first screening result to terminal equipment of the user; and when the user identifier is not a new user, screening the content set to be recommended according to browsing behavior data and content exposure data in different time zones in the historical behavior data, and recommending a corresponding screening result to the user. The invention also relates to a blockchain technology, and the content to be recommended can be stored in a blockchain node. The invention also provides a content recommendation device, equipment and medium, which can be used in the medical field and can improve the flexibility of content recommendation such as medical study video.
Description
Technical Field
The present invention relates to the field of artificial intelligence technology and medical science and technology, and in particular, to a content recommendation method, device, electronic apparatus, and storage medium.
Background
In the Internet era, a large amount of content is produced, pushed and consumed every day, a user is also faced with massive information, and how to screen massive content and pertinently recommend the massive content to the user is an important technology.
However, the current content recommendation method can only recommend the content by using a single fixed recommendation method, and the different demands of users with different categories cannot be recommended to the medical learning video wanted by the user, so that the flexibility of content recommendation such as the medical learning video is poor.
Disclosure of Invention
The invention provides a content recommendation method, a content recommendation device, electronic equipment and a storage medium, and mainly aims to improve flexibility of content recommendation of medical study videos and the like.
Acquiring historical behavior data of a user and a user identifier;
when the user identifier is a new user, acquiring a content heat value of each content to be recommended in a preset content set to be recommended, screening the content set to be recommended by using the content heat value to obtain a first screening result, and sending the first screening result to terminal equipment of the user;
When the user identifier is not a new user, judging whether browsing behavior data in a preset first time interval exist in the historical behavior data;
when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating a recommendation score of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation score to obtain a second screening result, and sending the first screening result to terminal equipment of the user;
when the historical behavior data does not have browsing behavior data in a preset first time interval, judging whether the historical behavior data has browsing behavior data and/or content exposure data in a preset second time interval;
when browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user;
And when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, sending the first screening result to the terminal equipment of the user.
Optionally, the screening the content set to be recommended by using the content heat value to obtain a first screening result includes:
according to the content heat value corresponding to each content to be recommended, all the content to be recommended in the content set to be recommended are arranged in a descending order to obtain a first content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the first content to be recommended sequence to obtain the first screening result.
Optionally, the calculating the recommendation score of each content to be recommended based on the browsing behavior data and the pre-built personalized recommendation model includes:
acquiring user information data of the user, and summarizing the user information data and the browsing behavior data to obtain user characteristic data;
vectorizing the user characteristic data to obtain a user characteristic vector;
acquiring content characteristic data of the content to be recommended, and vectorizing the content characteristic data to obtain a content characteristic vector;
Splicing the user feature vector and the content feature vector to obtain a target feature vector;
extracting features of the target feature vector by utilizing a neural network layer in the personalized recommendation model to obtain a first feature value;
extracting features of the target feature vector by utilizing a factoring machine layer in the personalized recommendation model to obtain a second feature value;
and calculating by using a SIGMOD function based on the first characteristic value and the second characteristic value to obtain a recommendation score of the content to be recommended.
Optionally, screening the content set to be recommended by using the recommendation score to obtain a second screening result, including:
according to the recommendation score corresponding to each content to be recommended, all the content to be recommended in the content set to be recommended are arranged in a descending order to obtain a second content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the second content to be recommended sequence to obtain a second screening result.
Optionally, deleting the browsed and/or exposed content to be recommended in the second time interval in the content set to be recommended to obtain a target content set to be recommended includes:
Determining the content to be recommended which is exposed in the second time interval in the content set to be recommended as exposure content;
acquiring the latest exposure time of the exposure content and the exposure times in the second time interval;
calculating an exposure coefficient of the exposure content based on the exposure times and the exposure time;
determining exposure content with the exposure coefficient larger than a preset exposure coefficient threshold value as target exposure content;
determining the browsed content to be recommended in the second time interval in the content set to be recommended as browsed content;
and deleting the target exposure content and the browsing content in the content set to be recommended to obtain the target content set to be recommended.
Optionally, the calculating the exposure coefficient of the exposure content based on the exposure times and the exposure time includes:
calculating the difference between the exposure time and the current time to obtain an exposure time difference;
calculating the reciprocal of the ratio of the exposure time difference value to the preset second time interval length to obtain an exposure time coefficient;
normalizing the exposure times to obtain an exposure time coefficient;
and weighting calculation is carried out by using preset exposure weight, the exposure time coefficient and the exposure frequency coefficient to obtain the exposure coefficient.
In order to solve the above problems, the present invention also provides a content recommendation apparatus, comprising:
the data acquisition module is used for acquiring historical behavior data of a user and a user identifier;
the new user recommending module is used for acquiring a content heat value of each content to be recommended in a preset content set to be recommended when the user identifier is a new user, screening the content set to be recommended by using the content heat value to obtain a first screening result, and sending the first screening result to terminal equipment of the user;
the non-new user recommending module is used for judging whether browsing behavior data in a preset first time interval exist in the historical behavior data or not when the user identifier is not a new user; when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating a recommendation score of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation score to obtain a second screening result, and sending the first screening result to terminal equipment of the user; when the historical behavior data does not have browsing behavior data in a preset first time interval, judging whether the historical behavior data has browsing behavior data and/or content exposure data in a preset second time interval; when browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user; and when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, sending the first screening result to the terminal equipment of the user.
Optionally, the screening the content set to be recommended by using the content heat value to obtain a first screening result includes:
according to the content heat value corresponding to each content to be recommended, all the content to be recommended in the content set to be recommended are arranged in a descending order to obtain a first content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the first content to be recommended sequence to obtain the first screening result.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one computer program; a kind of electronic device with high-pressure air-conditioning system
And a processor executing the computer program stored in the memory to implement the content recommendation method.
In order to solve the above-described problems, the present invention also provides a computer-readable storage medium having stored therein at least one computer program that is executed by a processor in an electronic device to implement the content recommendation method described above.
The embodiment of the invention obtains the content heat value of each content to be recommended in a preset content set to be recommended, screens the content set to be recommended by using the content heat value to obtain a first screening result, and sends the first screening result to the terminal equipment of the user; when the user identifier is not a new user, judging whether browsing behavior data in a preset first time interval exist in the historical behavior data; when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating a recommendation score of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation score to obtain a second screening result, and sending the first screening result to terminal equipment of the user; when the historical behavior data does not have browsing behavior data in a preset first time interval, judging whether the historical behavior data has browsing behavior data and/or content exposure data in a preset second time interval; when browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user; based on the difference of historical behavior data, users are indirectly identified and classified, so that the users in different categories are recommended by using corresponding recommendation methods, and compared with the case that all the users in all the categories use one type of content recommendation, the content recommendation is more targeted and flexible, so that the content recommendation method, the device, the electronic equipment and the readable storage medium provided by the embodiment of the invention improve the flexibility of the content recommendation of the medical learning video.
Drawings
FIG. 1 is a flowchart illustrating a content recommendation method according to an embodiment of the present application;
FIG. 2 is a schematic block diagram of a content recommendation device according to an embodiment of the present application;
fig. 3 is a schematic diagram of an internal structure of an electronic device for implementing a content recommendation method according to an embodiment of the present application;
the achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The embodiment of the application provides a content recommendation method. The execution subject of the content recommendation method includes, but is not limited to, at least one of a server, a terminal, and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the content recommendation method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: the server can be an independent server, or can be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDNs), basic cloud computing services such as big data and artificial intelligent platforms, and the like.
Referring to fig. 1, a flowchart of a content recommendation method according to an embodiment of the present invention is shown, where in the embodiment of the present invention, the content recommendation method includes:
s1, acquiring historical behavior data of a user and a user identifier;
the historical behavior data in the embodiment of the invention comprises all browsing behavior data and content exposure data from the time when a user registers an account, wherein the browsing behavior data comprises: the user plays/browses the content, and the content exposure data comprises: the content recommendation platform recommends content to the user. The user identification is a category identifying whether the user is a new user, comprising: new users and non-new users.
Optionally, in the embodiment of the present invention, the user is a login user of a certain medical learning video platform, and the content is a medical learning video in the platform, for example: the embodiment of the invention does not limit the type and the content of medical study videos, such as respiratory science study videos, pediatric study videos and the like.
S2, judging whether the user identifier is a new user or not;
in the embodiment of the invention, in order to recommend the proper medical learning video to the user, the user needs to know whether the user is a new user or not, so that whether the user is a new user or not is judged by judging the user identification.
S3, when the user identifier is a new user, acquiring a content heat value of each content to be recommended in a preset content set to be recommended, screening the content set to be recommended by using the content heat value to obtain a first screening result, and sending the first screening result to terminal equipment of the user;
in the embodiment of the invention, when the user identifier is a new user, the user is indicated to be the new user, and the user does not have browsing behavior actions and content exposure behaviors of corresponding content, so that behavior preference characteristics of the user cannot be obtained, therefore, a content popularity value of each content to be recommended in a preset content set to be recommended is obtained, the content popularity value is utilized to screen the content set to be recommended, a first screening result is obtained, and the first screening result is sent to the terminal equipment of the user, wherein the content popularity value is a parameter value representing the popularity of the corresponding content to be recommended, and can be an interactive behavior value such as the playing number, the praying number, the newly added playing number, the comment number, the attention number, the collection number and the like of the content, or can be a content popularity value calculated by utilizing one or more interactive behavior values.
For example: the method comprises the steps that to-be-recommended content is to-be-recommended medical learning video, in order to judge which to-be-recommended medical videos can be recommended to a user, the content heat value of each to-be-recommended medical learning video is obtained, the recommending degree of each to-be-recommended medical learning video is measured through the content heat value, then the appropriate to-be-recommended medical learning video is screened and recommended to the user, namely, the content heat value of each to-be-recommended content in a preset to-be-recommended content set is obtained, the to-be-recommended content set is screened by utilizing the content heat value, a first screening result is obtained, and the first screening result is sent to terminal equipment of the user. Further, in the embodiment of the present invention, the screening the content set to be recommended by using the content popularity value to obtain a first screening result includes:
according to the content heat value corresponding to each content to be recommended, all the content to be recommended in the content set to be recommended are arranged in a descending order to obtain a first content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the first content to be recommended sequence to obtain the first screening result.
Optionally, in the embodiment of the present invention, the terminal device may be an intelligent terminal such as a mobile phone, a computer, a tablet, and the like.
In the embodiment of the invention, in order to screen suitable content to be recommended for recommendation, the content set to be recommended is screened by using the content heat value, so as to obtain a first screening result, for example: the content to be recommended is medical learning videos to be recommended, the content to be recommended is within 5 ranks within a preset ranking range, the medical learning videos to be recommended are ordered according to the content heat value, and the medical learning videos to be recommended within 5 ranks are screened to obtain a first screening result.
In another embodiment of the present invention, the content to be recommended may be stored in a blockchain node, and the high throughput characteristic of the blockchain node is utilized to improve the data access efficiency.
S4, judging whether browsing behavior data in a preset first time interval exist in the historical behavior data or not when the user identifier is not a new user;
in the embodiment of the invention, when the user is not a new user, whether the historical behavior data has browsing behavior data in a preset first time interval is judged, and when the historical behavior data has browsing behavior data in the preset first time interval, the recent browsing behavior preference of the user can be analyzed according to the browsing behavior data of the user, so that targeted recommendation can be performed.
Further, before determining whether the historical behavior data includes browsing behavior data within the preset first time interval, the method further includes:
and taking the current time as a right endpoint, and taking a preset first time interval as an interval length to construct a time interval, so as to obtain the time interval.
Specifically, in the embodiment of the present invention, the time precision of the current time is not limited, where the time precision may be days, hours, minutes, seconds, and the like, and the length of the first time interval is not limited, and preferably, the first time interval is 14 days.
S5, when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating recommendation scores of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation scores to obtain a second screening result, and sending the first screening result to terminal equipment of the user;
in the embodiment of the invention, when the historical behavior data has the browsing behavior data in the preset first time interval, the recent browsing behavior preference of the user can be analyzed according to the browsing behavior data of the user, and the targeted recommendation can be performed.
Further, in the embodiment of the present invention, the personalized recommendation model is a deep FM model or an FM model.
In detail, when the personalized recommendation model in the embodiment of the present invention is a deep fm model, calculating the recommendation score of each content to be recommended based on the browsing behavior data and the pre-built personalized recommendation model includes:
acquiring user information data of the user, and summarizing the user information data and the browsing behavior data to obtain user characteristic data;
vectorizing the user characteristic data to obtain a user characteristic vector;
acquiring content characteristic data of the content to be recommended, and vectorizing the content characteristic data to obtain a content characteristic vector;
splicing the user feature vector and the content feature vector to obtain a target feature vector;
extracting features of the target feature vector by utilizing a neural network layer in the personalized recommendation model to obtain a first feature value;
extracting features of the target feature vector by utilizing a factoring machine layer in the personalized recommendation model to obtain a second feature value;
and calculating by using a SIGMOD function based on the first characteristic value and the second characteristic value to obtain a recommendation score of the content to be recommended.
Specifically, in the embodiment of the present invention, the user information data is basic information of a user, for example: gender, age, etc., the content characteristic data of the content to be recommended are content types, content titles, etc., in detail, in the embodiment of the present invention, the unique thermal coding algorithm is used to convert the user characteristic data into vectors, then the eboding layer in the personalized recommendation model is used to perform higher-order vector conversion on the converted vectors to obtain the user characteristic vectors, the vectorization of the content characteristic data is obtained by a similar method and is not repeated, further, in the embodiment of the present invention, the target characteristic vectors are input into the neural network layer to obtain the first characteristic values, and the target characteristic vectors are input into the factorizer layer to obtain the second characteristic values.
In detail, in the embodiment of the present invention, the first feature value and the second feature value are summed to obtain a target feature value, and the target feature value is calculated as a variable parameter of the SIGMOD function to obtain the recommendation score.
Further, in the embodiment of the present invention, screening the set of content to be recommended by using the recommendation score to obtain a second screening result includes:
According to the recommendation score corresponding to each content to be recommended, all the content to be recommended in the content set to be recommended are arranged in a descending order to obtain a second content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the second content to be recommended sequence to obtain a second screening result.
In the embodiment of the invention, the content to be recommended is measured through the recommendation score, and the content to be recommended with the top ranking is screened to obtain a second screening result, for example: the content to be recommended is medical learning videos to be recommended, the content to be recommended is within 3 ranks within a preset ranking range, the medical learning videos to be recommended are ordered according to the size of the recommendation score, and the medical learning videos to be recommended within 3 ranks are screened to obtain a second screening result.
S6, judging whether browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data or not when the browsing behavior data in the first time interval are not preset in the historical behavior data;
in the embodiment of the invention, when the historical behavior data does not have the browsing behavior data in the preset first time interval, the recent browsing behavior preference of the user cannot be analyzed according to the browsing behavior data of the user, so that corresponding content recommendation is required to be performed according to the browsing data and the content exposure data of the user at an earlier stage, and whether the historical behavior data has the browsing behavior data and/or the content exposure data in the preset second time interval is judged.
Before judging whether the historical behavior data has the browsing behavior data and the content exposure data in the preset second time interval, the method further comprises the following steps:
and taking the current time as a right endpoint, and taking a preset second time interval as an interval length to construct a time interval, so as to obtain the time interval.
Specifically, in the embodiment of the present invention, the second time interval is greater than the first time interval, and optionally, the second time interval is 90 days.
Further, in the embodiment of the present invention, when the historical behavior data does not have browsing behavior data within the preset first time interval, the user's recent browsing behavior preference characteristics cannot be characterized, so as to determine whether the historical behavior data has browsing behavior data and content exposure data within the preset second time interval,
s7, when browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user;
In the embodiment of the invention, when the browsing behavior data and the content exposure data in the second time interval are preset in the historical behavior data, the content browsed by the user is not recommended, and the recently exposed or repeatedly exposed content is not recommended to the user, so that the browsed and/or exposed content to be recommended in the second time interval in the content set to be recommended is deleted to obtain the target content set to be recommended.
In the embodiment of the present invention, deleting browsed and/or exposed content to be recommended in the second time interval in the content set to be recommended to obtain a target content set to be recommended includes:
determining the content to be recommended which is exposed in the second time interval in the content set to be recommended as exposure content;
acquiring the latest exposure time of the exposure content and the exposure times in the second time interval;
calculating an exposure coefficient of the exposure content based on the exposure times and the exposure time;
determining exposure content with the exposure coefficient larger than a preset exposure coefficient threshold value as target exposure content;
determining the browsed content to be recommended in the second time interval in the content set to be recommended as browsed content;
And deleting the target exposure content and the browsing content in the content set to be recommended to obtain the target content set to be recommended.
Further, in the embodiment of the present invention, the calculating the exposure coefficient of the exposure content based on the exposure times and the exposure time includes:
calculating the difference between the exposure time and the current time to obtain an exposure time difference;
calculating the reciprocal of the ratio of the exposure time difference value to the preset second time interval length to obtain an exposure time coefficient;
normalizing the exposure times to obtain an exposure time coefficient;
and weighting calculation is carried out by using preset exposure weight, the exposure time coefficient and the exposure frequency coefficient to obtain the exposure coefficient.
Specifically, in the embodiment of the invention, the ratio of the exposure times to the maximum exposure times is calculated to obtain the exposure times coefficient, and meanwhile, the exposure time coefficient is multiplied by the corresponding preset exposure weight to obtain the exposure time weight value; multiplying the exposure frequency coefficient by a corresponding preset exposure weight to obtain an exposure frequency weight value, and summing the exposure time weight value and the exposure coefficient weight value to obtain the exposure frequency.
Further, in the embodiment of the present invention, the screening of the target content set to be recommended by using the content popularity value to obtain a third screening result includes:
according to the content heat value corresponding to each content to be recommended, all the content to be recommended in the target content set to be recommended are arranged in a descending order to obtain a third content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the third content to be recommended sequence to obtain a third screening result.
In the embodiment of the invention, in order to screen suitable content to be recommended for recommendation, the content hotness value is used for screening the target content set to be recommended to obtain a third screening result, for example: and the to-be-recommended content is to-be-recommended medical learning videos, the to-be-recommended medical learning videos in the target to-be-recommended content set are ranked according to the content hotness value within a preset ranking range, and the to-be-recommended medical learning videos within the ranking range of 5 are screened to obtain a third screening result.
And S8, when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, the first screening result is sent to the terminal equipment of the user.
In the embodiment of the invention, when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, the user does not perform the related content browsing behavior in a quite long period of time or even log in the corresponding content recommendation system to trigger the content exposure, so that the user is equivalent to a new user, and the first screening result is sent to the terminal equipment of the user.
Fig. 2 is a functional block diagram of the content recommendation device according to the present invention.
The content recommendation apparatus 100 of the present invention may be installed in an electronic device. Depending on the implemented functions, the content recommendation device may include a data acquisition module 101, a new user recommendation module 102, and a non-new user recommendation module 103, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform fixed functions, and are stored in a memory of the electronic device.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the data acquisition module 101 is configured to acquire historical behavior data of a user and a user identifier;
the new user recommending module 102 is configured to obtain a content popularity value of each content to be recommended in a preset content set to be recommended when the user identifier is a new user, screen the content set to be recommended by using the content popularity value to obtain a first screening result, and send the first screening result to a terminal device of the user;
The non-new user recommending module 103 is configured to determine whether browsing behavior data in a preset first time interval exists in the historical behavior data when the user identifier is not a new user; when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating a recommendation score of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation score to obtain a second screening result, and sending the first screening result to terminal equipment of the user; when the historical behavior data does not have browsing behavior data in a preset first time interval, judging whether the historical behavior data has browsing behavior data and/or content exposure data in a preset second time interval; when browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user; and when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, sending the first screening result to the terminal equipment of the user.
In detail, each module in the content recommendation device 100 in the embodiment of the present invention adopts the same technical means as the content recommendation method described in fig. 1 and can produce the same technical effects when in use, and will not be described again here.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the content recommendation method according to the present invention.
The electronic device may comprise a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further comprise a computer program, such as a content recommendation program, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 11 may in other embodiments also be an external storage device of the electronic device, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only for storing application software installed in an electronic device and various types of data, such as codes of content recommendation programs, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules (e.g., content recommendation programs, etc.) stored in the memory 11, and calling data stored in the memory 11.
The communication bus 12 may be a peripheral component interconnect standard (perIPheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 is not limiting of the electronic device and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for supplying power to the respective components, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure classification circuit, power converter or inverter, power status indicator, etc. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
Optionally, the communication interface 13 may comprise a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices.
Optionally, the communication interface 13 may further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The content recommendation program stored in the memory 11 in the electronic device is a combination of a plurality of computer programs, which when run in the processor 10, can implement:
acquiring historical behavior data of a user and a user identifier;
when the user identifier is a new user, acquiring a content heat value of each content to be recommended in a preset content set to be recommended, screening the content set to be recommended by using the content heat value to obtain a first screening result, and sending the first screening result to terminal equipment of the user;
When the user identifier is not a new user, judging whether browsing behavior data in a preset first time interval exist in the historical behavior data;
when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating a recommendation score of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation score to obtain a second screening result, and sending the first screening result to terminal equipment of the user;
when the historical behavior data does not have browsing behavior data in a preset first time interval, judging whether the historical behavior data has browsing behavior data and/or content exposure data in a preset second time interval;
when browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user;
And when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, sending the first screening result to the terminal equipment of the user.
In particular, the specific implementation method of the processor 10 on the computer program may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable medium may be non-volatile or volatile. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
Embodiments of the present invention may also provide a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, may implement:
Acquiring historical behavior data of a user and a user identifier;
when the user identifier is a new user, acquiring a content heat value of each content to be recommended in a preset content set to be recommended, screening the content set to be recommended by using the content heat value to obtain a first screening result, and sending the first screening result to terminal equipment of the user;
when the user identifier is not a new user, judging whether browsing behavior data in a preset first time interval exist in the historical behavior data;
when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating a recommendation score of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation score to obtain a second screening result, and sending the first screening result to terminal equipment of the user;
when the historical behavior data does not have browsing behavior data in a preset first time interval, judging whether the historical behavior data has browsing behavior data and/or content exposure data in a preset second time interval;
when browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user;
And when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, sending the first screening result to the terminal equipment of the user.
Further, the computer-usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A content recommendation method, the method comprising:
acquiring historical behavior data of a user and a user identifier;
when the user identifier is a new user, acquiring a content heat value of each content to be recommended in a preset content set to be recommended, screening the content set to be recommended by using the content heat value to obtain a first screening result, and sending the first screening result to terminal equipment of the user;
when the user identifier is not a new user, judging whether browsing behavior data in a preset first time interval exist in the historical behavior data;
when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating a recommendation score of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation score to obtain a second screening result, and sending the first screening result to terminal equipment of the user;
when the historical behavior data does not have browsing behavior data in a preset first time interval, judging whether the historical behavior data has browsing behavior data and/or content exposure data in a preset second time interval;
When browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user;
and when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, sending the first screening result to the terminal equipment of the user.
2. The content recommendation method as claimed in claim 1, wherein the screening the content set to be recommended using the content popularity value to obtain a first screening result comprises:
according to the content heat value corresponding to each content to be recommended, all the content to be recommended in the content set to be recommended are arranged in a descending order to obtain a first content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the first content to be recommended sequence to obtain the first screening result.
3. The content recommendation method as claimed in claim 1, wherein the calculating a recommendation score for each content to be recommended based on the browsing behavior data and a pre-built personalized recommendation model comprises:
acquiring user information data of the user, and summarizing the user information data and the browsing behavior data to obtain user characteristic data;
vectorizing the user characteristic data to obtain a user characteristic vector;
acquiring content characteristic data of the content to be recommended, and vectorizing the content characteristic data to obtain a content characteristic vector;
splicing the user feature vector and the content feature vector to obtain a target feature vector;
extracting features of the target feature vector by utilizing a neural network layer in the personalized recommendation model to obtain a first feature value;
extracting features of the target feature vector by utilizing a factoring machine layer in the personalized recommendation model to obtain a second feature value;
and calculating by using a SIGMOD function based on the first characteristic value and the second characteristic value to obtain a recommendation score of the content to be recommended.
4. The content recommendation method as claimed in claim 1, wherein said screening the set of content to be recommended with the recommendation score to obtain a second screening result comprises:
According to the recommendation score corresponding to each content to be recommended, all the content to be recommended in the content set to be recommended are arranged in a descending order to obtain a second content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the second content to be recommended sequence to obtain a second screening result.
5. The method for recommending contents according to any one of claims 1 to 4, wherein deleting the browsed and/or exposed contents to be recommended in the second time interval in the set of contents to be recommended to obtain the target set of contents to be recommended comprises:
determining the content to be recommended which is exposed in the second time interval in the content set to be recommended as exposure content;
acquiring the latest exposure time of the exposure content and the exposure times in the second time interval;
calculating an exposure coefficient of the exposure content based on the exposure times and the exposure time;
determining exposure content with the exposure coefficient larger than a preset exposure coefficient threshold value as target exposure content;
determining the browsed content to be recommended in the second time interval in the content set to be recommended as browsed content;
and deleting the target exposure content and the browsing content in the content set to be recommended to obtain the target content set to be recommended.
6. The content recommendation method according to claim 5, wherein the calculating an exposure coefficient of the exposure content based on the exposure times and the exposure time comprises:
calculating the difference between the exposure time and the current time to obtain an exposure time difference;
calculating the reciprocal of the ratio of the exposure time difference value to the preset second time interval length to obtain an exposure time coefficient;
normalizing the exposure times to obtain an exposure time coefficient;
and weighting calculation is carried out by using preset exposure weight, the exposure time coefficient and the exposure frequency coefficient to obtain the exposure coefficient.
7. A content recommendation device, comprising:
the data acquisition module is used for acquiring historical behavior data of a user and a user identifier;
the new user recommending module is used for acquiring a content heat value of each content to be recommended in a preset content set to be recommended when the user identifier is a new user, screening the content set to be recommended by using the content heat value to obtain a first screening result, and sending the first screening result to terminal equipment of the user;
the non-new user recommending module is used for judging whether browsing behavior data in a preset first time interval exist in the historical behavior data or not when the user identifier is not a new user; when browsing behavior data in a preset first time interval exist in the historical behavior data, calculating a recommendation score of each content to be recommended based on the browsing behavior data and a preset personalized recommendation model, screening a content set to be recommended by using the recommendation score to obtain a second screening result, and sending the first screening result to terminal equipment of the user; when the historical behavior data does not have browsing behavior data in a preset first time interval, judging whether the historical behavior data has browsing behavior data and/or content exposure data in a preset second time interval; when browsing behavior data and/or content exposure data in a second time interval are preset in the historical behavior data, deleting browsed and/or exposed content to be recommended in the second time interval in the content to be recommended to obtain a target content set to be recommended, screening the target content set to be recommended by using the content heat value to obtain a third screening result, and sending the third screening result to terminal equipment of the user; and when the browsing behavior data and the content exposure data in the second time interval are not preset in the historical behavior data, sending the first screening result to the terminal equipment of the user.
8. The content recommendation device of claim 7, wherein the filtering the set of content to be recommended using the content popularity value to obtain a first filtering result comprises:
according to the content heat value corresponding to each content to be recommended, all the content to be recommended in the content set to be recommended are arranged in a descending order to obtain a first content sequence to be recommended;
and screening all the contents to be recommended in a preset ranking range in the first content to be recommended sequence to obtain the first screening result.
9. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the content recommendation method according to any one of claims 1 to 6.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the content recommendation method according to any one of claims 1 to 6.
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