CN113961813A - Information recommendation method, device, equipment and storage medium based on artificial intelligence - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, and discloses an information recommendation method based on artificial intelligence, which comprises the following steps: when a login request of a personal portal website is received, analyzing the login request to obtain identification information of a user; according to the identification information, searching personal information and user behavior data corresponding to the identification information in a database; searching corresponding internet hot spot information according to the personal information and the user behavior data, and extracting topic information from the internet hot spot information in a keyword matching mode; analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data; and determining recommendation information according to the association degree between the topic information and each resource data, and recommending the recommendation information to the user. The invention improves the accuracy of the recommendation information.
Description
Technical Field
The application belongs to the technical field of artificial intelligence, and particularly relates to an information recommendation method, device, equipment and storage medium based on artificial intelligence.
Background
At present, a personal portal website and a third-party portal website are not intercommunicated, and a plurality of portal websites do not count user behaviors or hide the user behaviors, so that portal website information cannot be recommended directly by analyzing user behavior data for a third-party platform; the method for pushing news based on news popularity has the problems that news popularity assessment is not accurate enough and accurate pushing cannot be carried out by combining personal preference of users.
Disclosure of Invention
In view of the above, the present invention provides an information recommendation method, apparatus, device and storage medium based on artificial intelligence, and aims to solve the technical problem of inaccurate recommendation information in the prior art.
In order to achieve the above object, the present invention provides an information recommendation method based on artificial intelligence, wherein the method comprises:
when a login request of a personal portal website is received, analyzing the login request to obtain identification information of a user;
according to the identification information, searching personal information and user behavior data corresponding to the identification information in a database;
searching corresponding internet hot spot information according to the personal information and the user behavior data, and extracting topic information from the internet hot spot information in a keyword matching mode;
analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data;
and determining recommendation information according to the association degree between the topic information and each resource data, and recommending the recommendation information to the user.
Preferably, when receiving a login request of a personal portal website, analyzing the login request to obtain identification information of a user, including:
acquiring an identification attribute corresponding to the identification information according to the identification information;
converting the identification attribute according to a preset conversion rule to obtain an address identification code corresponding to the identification attribute;
and determining the user source of the user according to the address identification code.
Preferably, the searching for corresponding internet hot spot information according to the personal information and the user behavior data and extracting topic information from the internet hot spot information in a keyword matching manner includes:
acquiring network information ranked at the top in a search engine according to the website weight, and determining the network information as the internet hotspot information;
classifying the Internet hotspot information through a pre-trained article classification model to obtain a hotspot category to which each piece of Internet hotspot information belongs;
and extracting topic information from the Internet hot spot information in a keyword matching mode, and generating a corresponding relation between the hot spot category and the topic information.
Preferably, the analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data includes:
extracting keywords corresponding to the resource data through the pre-trained information recommendation model;
and vectorizing each keyword to obtain a characterization vector corresponding to each resource data.
Preferably, the analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data further includes:
calculating Euclidean distances between the topic information and each characterization vector;
and determining the association degree between the topic information and each characterization vector according to the Euclidean distance between the topic information and each characterization vector.
Preferably, the user behavior data includes: the user corresponding attention information, input record information, collection information and browsing information; the personal information includes a name, an age, account information, and taste information of the user.
Preferably, before analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data, the method includes:
learning sample data based on machine learning, and adjusting parameters of an information recommendation model in training in real time according to a learning result of the machine learning;
and when the convergence of the loss function corresponding to the information recommendation model in training is detected, obtaining the pre-trained information recommendation model.
In order to achieve the above object, the present invention further provides an artificial intelligence based information recommendation apparatus, comprising:
the acquisition module is used for analyzing the login request to obtain the identification information of the user when the login request of the personal portal website is received;
the searching module is used for searching the personal information and the user behavior data corresponding to the identification information in a database according to the identification information;
the extraction module is used for searching corresponding Internet hot spot information according to the personal information and the user behavior data and extracting topic information from the Internet hot spot information in a keyword matching mode;
the analysis module is used for analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data;
and the recommending module is used for determining recommending information according to the association degree between the topic information and each resource data and recommending the recommending information to the user.
In order to achieve the above object, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a program executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based information recommendation method.
In order to achieve the above object, the present invention further provides a computer-readable storage medium storing an artificial intelligence based information recommendation program, which when executed by a processor, implements the steps of the artificial intelligence based information recommendation method.
According to the method and the system, the topic information in the Internet hot spot information is extracted by combining the personal information and the user behavior data, and the topic information is analyzed through a pre-trained information recommendation model to combine the topic information and the resource data in the personal portal website, so that the recommendation information is determined, the recommendation information can be combined with the Internet hot spot information and is associated with the user personal information and the user behavior data, the current hot spot consultation is combined, the personal preference and behavior habit of the user are not deviated, the accurate recommendation information is determined and recommended to the user, and the accuracy of the recommendation information is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a diagram of an electronic device according to a preferred embodiment of the present invention;
FIG. 2 is a block diagram of a preferred embodiment of the artificial intelligence based information recommendation device of FIG. 1;
FIG. 3 is a flowchart illustrating a preferred embodiment of an artificial intelligence-based information recommendation method according to the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 is a schematic diagram of an electronic device 1 according to a preferred embodiment of the invention.
The electronic device 1 includes but is not limited to: memory 11, processor 12, display 13, and network interface 14. The electronic device 1 is connected to a network through a network interface 14 to obtain raw data. The network may be a wireless or wired network such as an Intranet (Internet), the Internet (Internet), a Global System for mobile communications (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), or a Wi-Fi communication network.
The memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 11 may be an internal storage unit of the electronic device 1, such as a hard disk or a memory of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like equipped with the electronic device 1.
Of course, the memory 11 may also comprise both an internal memory unit and an external memory device of the electronic device 1. In this embodiment, the memory 11 is generally used for storing an operating system installed in the electronic device 1 and various application software, such as program codes of the artificial intelligence based information recommendation program 10. Further, the memory 11 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 12 is typically used for controlling the overall operation of the electronic device 1, such as performing data interaction or communication related control and processing. In this embodiment, the processor 12 is configured to execute the program code stored in the memory 11 or process data, for example, execute the program code of the artificial intelligence based information recommendation program 10.
The display 13 may be referred to as a display screen or display unit. In some embodiments, the display 13 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-emitting diode (OLED) touch screen, or the like. The display 13 is used for displaying information processed in the electronic device 1 and for displaying a visual work interface, e.g. displaying the results of data statistics.
The network interface 14 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), the network interface 14 typically being used for establishing a communication connection between the electronic device 1 and other electronic devices.
Fig. 1 shows only the electronic device 1 with components 11-14 and the artificial intelligence based information recommender 10, but it will be understood that not all of the shown components are required and that more or less components may be implemented instead.
Optionally, the electronic device 1 may further comprise a target user interface, the target user interface may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional target user interface may further comprise a standard wired interface and 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 Organic Light-Emitting Diode (OLED) touch screen, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized target user interface, among other things.
The electronic device 1 may further include a Radio Frequency (RF) circuit, a sensor, an audio circuit, and the like, which are not described in detail herein.
In the above embodiment, the processor 12, when executing the artificial intelligence based information recommendation program 10 stored in the memory 11, may implement the following steps:
when a login request of a personal portal website is received, analyzing the login request to obtain identification information of a user;
according to the identification information, searching personal information and user behavior data corresponding to the identification information in a database;
searching corresponding internet hot spot information according to the personal information and the user behavior data, and extracting topic information from the internet hot spot information in a keyword matching mode;
analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data;
and determining recommendation information according to the association degree between the topic information and each resource data, and recommending the recommendation information to the user.
For detailed description of the above steps, please refer to the following description of fig. 2 regarding a functional block diagram of an embodiment of the artificial intelligence based information recommendation apparatus 100 and fig. 3 regarding a flowchart of an embodiment of an artificial intelligence based information recommendation method.
Referring to fig. 2, a functional block diagram of an artificial intelligence based information recommendation apparatus 100 according to the present invention is shown.
The artificial intelligence based information recommendation device 100 of the present invention can be installed in an electronic device. According to the implemented functions, the artificial intelligence based information recommendation apparatus 100 may include: an acquisition module 110, a search module 120, an extraction module 130, an analysis module 140, and a recommendation module 150. The module of the present invention, 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 a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the obtaining module 110 is configured to, when receiving a login request of a personal portal website, parse the login request to obtain identification information of a user.
In an exemplary embodiment, the personal web portal may be any type of personal web portal. For example, the portal service system may be a personal portal site for shopping, a personal portal site for finance, a personal portal site for school, a personal portal site for news, a personal portal site for travel, a personal portal site for entertainment, a personal portal site for search engine, a regional personal portal site, or the like.
Illustratively, identification information of users logged in on a personal portal website is obtained, the identification information is used for distinguishing identities of different users, and the identification information of each user refers to only one user.
For example, the identification information may be a user Identity identification number (ID), each user ID refers to a unique user, the user IDs of different login users are different, and by obtaining the user ID of the user logged in on the personal portal website, it is convenient to subsequently extract data corresponding to the user ID, thereby implementing information recommendation for the user. The user ID may include an identification number of an identification card, a registered account number, a unique code preset for each user, a mobile phone number, and the like. The description is merely exemplary in nature and is in no way intended to be limiting.
Illustratively, when a user is detected to log in a personal portal, identification information of the user currently logged in is acquired.
Optionally, in a possible implementation process, when a login request of a personal portal website is received, parsing the login request to obtain identification information of a user includes:
acquiring an identification attribute corresponding to the identification information according to the identification information;
converting the identification attribute according to a preset conversion rule to obtain an address identification code corresponding to the identification attribute;
and determining the user source of the user according to the address identification code.
In this embodiment, an identification attribute corresponding to the identification information is obtained, where the identification attribute is used to identify the terminal information of the user source, and the terminal information of the user source includes: the method comprises the following steps of PC end webpage access source, mobile end application program access source and mobile end small program access source. Different user sources are identified, and the matching of recommendation information is enhanced according to different configuration information configured by different user sources.
The searching module 120 is configured to search, according to the identification information, personal information and user behavior data corresponding to the identification information in a database;
in an exemplary embodiment, the personal information may include information corresponding to the user, such as name, age, sex, height, contact method, address, identification number, study history, work, account information registered by the user at a personal portal site, hobbies, and the like. The user behavior data comprises the attention information, the input record information, the collection information, the browsing information, the comment information, the browsing duration, the shielding record, the searching record, the sharing record and the like corresponding to the target personal portal website of the user.
In the database of the personal portal website, the identification information of each user, and the personal information and the user behavior data corresponding to each identification information are stored. After the identification information of the user is obtained, the personal information and the user behavior data corresponding to the identification information are searched in the database based on the identification information of the user.
Illustratively, a user fills in a login account (which can be used as a user ID of the user) and a password according to a registration process of a certain personal portal, and registers in the personal portal, and the personal portal generates account information for the user according to the login account and the password filled in by the user. The user can also be prompted to complete the information so as to collect information of the name, age, sex, height, contact way, address, hobby and the like of the user. The account information corresponding to the user and the perfect information are classified into personal information corresponding to the user, the personal information is associated with the user ID of the user, and the associated information is stored in a database corresponding to a personal portal website.
When a user logs in a personal portal website and performs operations such as attention, collection, search, browsing, shielding, deleting, commenting and the like in the personal portal website, the operation behaviors of the user are recorded, and data generated based on the operation behaviors are recorded. Classifying the operation behaviors of the user and data generated based on the operation behaviors into user behavior data corresponding to the user, associating the user behavior data with the identification information of the user, and storing the associated information in a database corresponding to the personal portal website. The description is given for illustrative purposes only and is not intended to be limiting.
After the user ID of the user is obtained, the personal information and the user behavior data corresponding to the user ID are searched in the database based on the user ID of the user.
In the embodiment, the user ID of the user of the personal portal site is obtained, so that the personal information and the user behavior data of the user can be conveniently extracted subsequently, and accurate information can be recommended to the user in a targeted manner.
Optionally, in a possible implementation process, extracting initial data of the user according to the identification information; and eliminating random behavior data of the user in the initial data to obtain target data, wherein the target data are personal information and user behavior data corresponding to the identification information.
Illustratively, the initial data also includes user behavior data and personal information of the user. The user behavior data and the personal information in the initial data are the same as the user behavior data and the personal information included in the target data. And determining random behavior data in the user behavior data in the initial data, and eliminating the random behavior data to obtain target data.
Random behavior data refers to data that results from sudden behavior under certain conditions. For example, the user always browses current news information, suddenly browses star information for a certain time, and does not browse the star information any more after that, and the user may carelessly browse the star information by sliding the hands. The data generated by browsing the star information is the random behavior data of the user.
For example, the times of browsing different types of information by a user within a certain time can be detected, and if the times of browsing certain information is detected to be less than a preset time, behavior data generated by browsing the type of information is marked as random behavior data.
Optionally, the time length for the user to browse different types of information within a certain time period may also be detected, and if it is detected that the time length for browsing a certain type of information is less than a preset time length, behavior data generated by browsing the type of information is marked as random behavior data. The description is given for illustrative purposes only and is not intended to be limiting.
And determining all random behavior data in the user behavior data, and eliminating the random behavior data to obtain target data.
In the embodiment, the random behavior data in the user behavior data are removed, interference caused by subsequent random behavior data is avoided, the corresponding internet hotspot information can be accurately searched, and then the association degree between the topic information and each resource data is conveniently and accurately calculated subsequently, so that accurate information is recommended to the user based on the association degree. And random behavior data are extracted and removed, so that the workload of the model is reduced, and the processing speed is further improved.
The extraction module 130 is configured to search corresponding internet hotspot information according to the personal information and the user behavior data, and extract topic information from the internet hotspot information in a keyword matching manner;
optionally, in a possible implementation process, the searching for corresponding internet hotspot information according to the personal information and the user behavior data, and extracting topic information from the internet hotspot information in a keyword matching manner includes:
acquiring network information ranked at the top in a search engine according to the website weight, and determining the network information as the internet hotspot information;
classifying the Internet hotspot information through a pre-trained article classification model to obtain a hotspot category to which each piece of Internet hotspot information belongs;
and extracting topic information from the Internet hot spot information in a keyword matching mode, and generating a corresponding relation between the hot spot category and the topic information.
According to the method and the device, the topic information is extracted from the Internet hot spot information, so that the recommendation information can be associated with the Internet hot spot information and the personal information and the user behavior data of the user, the current affair hot spot consultation is combined, the personal preference and the behavior habit of the user are not deviated, the accurate recommendation information is determined and recommended to the user, and the accuracy of the recommendation information is improved.
The analysis module 140 is configured to analyze the topic information and the resource data in the personal portal website through a pre-trained information recommendation model, so as to obtain a degree of association between the topic information and each resource data.
In this embodiment, the information recommendation model pre-trained in the information recommendation device may be an LSTM model, and is obtained by training an initial LSTM model based on target data using a machine learning algorithm.
Specifically, before analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data, the method includes:
learning sample data based on machine learning, and adjusting parameters of an information recommendation model in training in real time according to a learning result of the machine learning;
and when the convergence of the loss function corresponding to the information recommendation model in training is detected, obtaining the pre-trained information recommendation model.
It is understood that the resource data of the personal web portal refers to various different types of information presented in the personal web portal. For example, the resource data may include entertainment information, art information, shopping information, game information, military information, sports information, automobile information, finance information, tourism information, property information, and the like.
Alternatively, the resource data of the personal web portal may be presented in the form of cards (layout blocks), one resource data corresponding to one card (layout block). For example, entertainment information corresponds to a card, art information corresponds to a card, and the like. The description is given for illustrative purposes only and is not intended to be limiting.
Further, the analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data includes:
extracting keywords corresponding to the resource data through the pre-trained information recommendation model;
and vectorizing each keyword to obtain a characterization vector corresponding to each resource data.
In this embodiment, a pre-trained information recommendation model is used to perform sentence segmentation, word segmentation, and prediction on each resource data in sequence. The sentence segmentation processing means that the resource data is divided into short sentences, and the word segmentation processing means that continuous word sequences in the short sentences are divided into a plurality of word sequences. Illustratively, for each resource data, each short sentence of the resource data is subjected to word segmentation processing, so as to obtain a plurality of word sequences.
Optionally, each resource data of the personal web portal may be preprocessed, and the sentence division processing and the word division processing may be performed on the result of the preprocessing. The way of preprocessing the resource data is the same as the way of preprocessing the target data, and is not described here again.
And respectively mapping the word sequences to a vector space, namely respectively converting the word sequences into vectors. Illustratively, each word sequence is input into a pre-trained information recommendation model for processing, and a plurality of word vectors are obtained. The word vector is a vector corresponding to a predicted word after processing a plurality of word sequences. Combining the word vectors to obtain a sentence vector, and determining a characterization vector corresponding to the sentence vector. The characterization vector is used for representing semantic features of a sentence corresponding to the resource data.
Illustratively, the topic information is also expressed in the form of vectors, and cosine similarity between the topic information and each characterization vector is calculated, and the cosine similarity is used for representing the degree of association between the topic information and the characterization vectors. The more the cosine similarity is, the higher the association degree between the topic information and the characterization vector is; the smaller the cosine similarity is, the lower the degree of association between the representative topic information and the token vector is.
Further, the analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data further includes:
calculating Euclidean distances between the topic information and each characterization vector;
and determining the association degree between the topic information and each characterization vector according to the Euclidean distance between the topic information and each characterization vector.
Illustratively, the euclidean distance between the topic information and each of the token vectors is calculated. The Euclidean distance is used for representing the association degree between the topic information and each characterization vector, and the greater the Euclidean distance is, the higher the association degree between the topic information and the characterization vectors is; the smaller the euclidean distance, the lower the degree of association between the representative topic information and the token vector.
And the recommending module 150 is configured to determine recommendation information according to the association between the topic information and each resource data, and recommend the recommendation information to the user.
In this embodiment, all the resource data are sorted according to the degree of association between the topic information and each resource data, recommendation information is determined according to the sorting result, and the recommendation information is recommended to the user. Illustratively, all the resource data are sorted from high relevance to low relevance, one or more resource data sorted at the top are taken as recommendation information, and the recommendation information is recommended to the user. Or, all the resource data may be sorted in the order of the relevance from low to high, and one or more resource data sorted after the sorting may be used as recommendation information and recommended to the user. The description is only exemplary, and not limiting.
Optionally, the recommendation information can be recommended to the user in the form of a card, that is, the information to be recommended is displayed in a conspicuous position of the personal website in the form of a card. For example, the recommendation information is displayed in the form of a card in the center of the current page of the personal portal. The description is given for illustrative purposes only and is not intended to be limiting.
In addition, the invention also provides an information recommendation method based on artificial intelligence. Fig. 3 is a schematic method flow diagram of an embodiment of the artificial intelligence based information recommendation method according to the present invention. When the processor 12 of the electronic device 1 executes the artificial intelligence based information recommendation program 10 stored in the memory 11, the artificial intelligence based information recommendation method is implemented, including steps S101-S105.
Step S101, when receiving the login request of the personal portal website, analyzing the login request to obtain the identification information of the user.
In an exemplary embodiment, the personal web portal may be any type of personal web portal. For example, the portal service system may be a personal portal site for shopping, a personal portal site for finance, a personal portal site for school, a personal portal site for news, a personal portal site for travel, a personal portal site for entertainment, a personal portal site for search engine, a regional personal portal site, or the like.
Illustratively, identification information of users logged in on a personal portal website is obtained, the identification information is used for distinguishing identities of different users, and the identification information of each user refers to only one user.
For example, the identification information may be a user Identity identification number (ID), each user ID refers to a unique user, the user IDs of different login users are different, and by obtaining the user ID of the user logged in on the personal portal website, it is convenient to subsequently extract data corresponding to the user ID, thereby implementing information recommendation for the user. The user ID may include an identification number of an identification card, a registered account number, a unique code preset for each user, a mobile phone number, and the like. The description is merely exemplary in nature and is in no way intended to be limiting.
Illustratively, when a user is detected to log in a personal portal, identification information of the user currently logged in is acquired.
Optionally, in a possible implementation process, when a login request of a personal portal website is received, parsing the login request to obtain identification information of a user includes:
acquiring an identification attribute corresponding to the identification information according to the identification information;
converting the identification attribute according to a preset conversion rule to obtain an address identification code corresponding to the identification attribute;
and determining the user source of the user according to the address identification code.
In this embodiment, an identification attribute corresponding to the identification information is obtained, where the identification attribute is used to identify the terminal information of the user source, and the terminal information of the user source includes: the method comprises the following steps of PC end webpage access source, mobile end application program access source and mobile end small program access source. Different user sources are identified, and the matching of recommendation information is enhanced according to different configuration information configured by different user sources.
Step S102, according to the identification information, searching personal information and user behavior data corresponding to the identification information in a database;
in an exemplary embodiment, the personal information may include information corresponding to the user, such as name, age, sex, height, contact method, address, identification number, study history, work, account information registered by the user at a personal portal site, hobbies, and the like. The user behavior data comprises the attention information, the input record information, the collection information, the browsing information, the comment information, the browsing duration, the shielding record, the searching record, the sharing record and the like corresponding to the target personal portal website of the user.
In the database of the personal portal website, the identification information of each user, and the personal information and the user behavior data corresponding to each identification information are stored. After the identification information of the user is obtained, the personal information and the user behavior data corresponding to the identification information are searched in the database based on the identification information of the user.
Illustratively, a user fills in a login account (which can be used as a user ID of the user) and a password according to a registration process of a certain personal portal, and registers in the personal portal, and the personal portal generates account information for the user according to the login account and the password filled in by the user. The user can also be prompted to complete the information so as to collect information of the name, age, sex, height, contact way, address, hobby and the like of the user. The account information corresponding to the user and the perfect information are classified into personal information corresponding to the user, the personal information is associated with the user ID of the user, and the associated information is stored in a database corresponding to a personal portal website.
When a user logs in a personal portal website and performs operations such as attention, collection, search, browsing, shielding, deleting, commenting and the like in the personal portal website, the operation behaviors of the user are recorded, and data generated based on the operation behaviors are recorded. Classifying the operation behaviors of the user and data generated based on the operation behaviors into user behavior data corresponding to the user, associating the user behavior data with the identification information of the user, and storing the associated information in a database corresponding to the personal portal website. The description is given for illustrative purposes only and is not intended to be limiting.
After the user ID of the user is obtained, the personal information and the user behavior data corresponding to the user ID are searched in the database based on the user ID of the user.
In the embodiment, the user ID of the user of the personal portal site is obtained, so that the personal information and the user behavior data of the user can be conveniently extracted subsequently, and accurate information can be recommended to the user in a targeted manner.
Optionally, in a possible implementation process, extracting initial data of the user according to the identification information; and eliminating random behavior data of the user in the initial data to obtain target data, wherein the target data are personal information and user behavior data corresponding to the identification information.
Illustratively, the initial data also includes user behavior data and personal information of the user. The user behavior data and the personal information in the initial data are the same as the user behavior data and the personal information included in the target data. And determining random behavior data in the user behavior data in the initial data, and eliminating the random behavior data to obtain target data.
Random behavior data refers to data that results from sudden behavior under certain conditions. For example, the user always browses current news information, suddenly browses star information for a certain time, and does not browse the star information any more after that, and the user may carelessly browse the star information by sliding the hands. The data generated by browsing the star information is the random behavior data of the user.
For example, the times of browsing different types of information by a user within a certain time can be detected, and if the times of browsing certain information is detected to be less than a preset time, behavior data generated by browsing the type of information is marked as random behavior data.
Optionally, the time length for the user to browse different types of information within a certain time period may also be detected, and if it is detected that the time length for browsing a certain type of information is less than a preset time length, behavior data generated by browsing the type of information is marked as random behavior data. The description is given for illustrative purposes only and is not intended to be limiting.
And determining all random behavior data in the user behavior data, and eliminating the random behavior data to obtain target data.
In the embodiment, the random behavior data in the user behavior data are removed, interference caused by subsequent random behavior data is avoided, the corresponding internet hotspot information can be accurately searched, and then the association degree between the topic information and each resource data is conveniently and accurately calculated subsequently, so that accurate information is recommended to the user based on the association degree. And random behavior data are extracted and removed, so that the workload of the model is reduced, and the processing speed is further improved.
Step S103, searching corresponding Internet hot spot information according to the personal information and the user behavior data, and extracting topic information from the Internet hot spot information in a keyword matching mode;
optionally, in a possible implementation process, the searching for corresponding internet hotspot information according to the personal information and the user behavior data, and extracting topic information from the internet hotspot information in a keyword matching manner includes:
acquiring network information ranked at the top in a search engine according to the website weight, and determining the network information as the internet hotspot information;
classifying the Internet hotspot information through a pre-trained article classification model to obtain a hotspot category to which each piece of Internet hotspot information belongs;
and extracting topic information from the Internet hot spot information in a keyword matching mode, and generating a corresponding relation between the hot spot category and the topic information.
According to the method and the device, the topic information is extracted from the Internet hot spot information, so that the recommendation information can be associated with the Internet hot spot information and the personal information and the user behavior data of the user, the current affair hot spot consultation is combined, the personal preference and the behavior habit of the user are not deviated, the accurate recommendation information is determined and recommended to the user, and the accuracy of the recommendation information is improved.
Step S104, analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data.
In this embodiment, the information recommendation model pre-trained in the information recommendation device may be an LSTM model, and is obtained by training an initial LSTM model based on target data using a machine learning algorithm.
Specifically, before analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data, the method includes:
learning sample data based on machine learning, and adjusting parameters of an information recommendation model in training in real time according to a learning result of the machine learning;
and when the convergence of the loss function corresponding to the information recommendation model in training is detected, obtaining the pre-trained information recommendation model.
It is understood that the resource data of the personal web portal refers to various different types of information presented in the personal web portal. For example, the resource data may include entertainment information, art information, shopping information, game information, military information, sports information, automobile information, finance information, tourism information, property information, and the like.
Alternatively, the resource data of the personal web portal may be presented in the form of cards (layout blocks), one resource data corresponding to one card (layout block). For example, entertainment information corresponds to a card, art information corresponds to a card, and the like. The description is given for illustrative purposes only and is not intended to be limiting.
Further, the analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data includes:
extracting keywords corresponding to the resource data through the pre-trained information recommendation model;
and vectorizing each keyword to obtain a characterization vector corresponding to each resource data.
In this embodiment, a pre-trained information recommendation model is used to perform sentence segmentation, word segmentation, and prediction on each resource data in sequence. The sentence segmentation processing means that the resource data is divided into short sentences, and the word segmentation processing means that continuous word sequences in the short sentences are divided into a plurality of word sequences. Illustratively, for each resource data, each short sentence of the resource data is subjected to word segmentation processing, so as to obtain a plurality of word sequences.
Optionally, each resource data of the personal web portal may be preprocessed, and the sentence division processing and the word division processing may be performed on the result of the preprocessing. The way of preprocessing the resource data is the same as the way of preprocessing the target data, and is not described here again.
And respectively mapping the word sequences to a vector space, namely respectively converting the word sequences into vectors. Illustratively, each word sequence is input into a pre-trained information recommendation model for processing, and a plurality of word vectors are obtained. The word vector is a vector corresponding to a predicted word after processing a plurality of word sequences. Combining the word vectors to obtain a sentence vector, and determining a characterization vector corresponding to the sentence vector. The characterization vector is used for representing semantic features of a sentence corresponding to the resource data.
Illustratively, the topic information is also expressed in the form of vectors, and cosine similarity between the topic information and each characterization vector is calculated, and the cosine similarity is used for representing the degree of association between the topic information and the characterization vectors. The more the cosine similarity is, the higher the association degree between the topic information and the characterization vector is; the smaller the cosine similarity is, the lower the degree of association between the representative topic information and the token vector is.
Further, the analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data further includes:
calculating Euclidean distances between the topic information and each characterization vector;
and determining the association degree between the topic information and each characterization vector according to the Euclidean distance between the topic information and each characterization vector.
Illustratively, the euclidean distance between the topic information and each of the token vectors is calculated. The Euclidean distance is used for representing the association degree between the topic information and each characterization vector, and the greater the Euclidean distance is, the higher the association degree between the topic information and the characterization vectors is; the smaller the euclidean distance, the lower the degree of association between the representative topic information and the token vector.
And step S105, determining recommendation information according to the association degree between the topic information and each resource data, and recommending the recommendation information to the user.
In this embodiment, all the resource data are sorted according to the degree of association between the topic information and each resource data, recommendation information is determined according to the sorting result, and the recommendation information is recommended to the user. Illustratively, all the resource data are sorted from high relevance to low relevance, one or more resource data sorted at the top are taken as recommendation information, and the recommendation information is recommended to the user. Or, all the resource data may be sorted in the order of the relevance from low to high, and one or more resource data sorted after the sorting may be used as recommendation information and recommended to the user. The description is only exemplary, and not limiting.
Optionally, the recommendation information can be recommended to the user in the form of a card, that is, the information to be recommended is displayed in a conspicuous position of the personal website in the form of a card. For example, the recommendation information is displayed in the form of a card in the center of the current page of the personal portal. The description is given for illustrative purposes only and is not intended to be limiting.
In the embodiment, topic information in internet hot spot information is extracted by combining personal information and user behavior data, and topic information is analyzed by combining a pre-trained information recommendation model for topic information and resource data in a personal portal website, so that recommendation information is determined, the recommendation information can be combined with the internet hot spot information and is associated with the user personal information and the user behavior data, and therefore, the current affair hot spot consultation is combined, the user personal preference and behavior habit are not deviated, accurate recommendation information is determined and recommended to a user, and the accuracy of the recommendation information is improved.
Furthermore, the embodiment of the present invention also provides a computer-readable storage medium, which may be any one or any combination of hard disk, multimedia card, SD card, flash memory card, SMC, Read Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), portable compact disc read only memory (CD-ROM), USB memory, and the like. The computer-readable storage medium comprises a storage data area and a storage program area, the storage data area stores data created according to the use of the blockchain nodes, the storage program area stores an artificial intelligence-based information recommendation program 10, and when the artificial intelligence-based information recommendation program 10 is executed by a processor, the operation of the artificial intelligence-based information recommendation method is realized.
In another embodiment, in order to further ensure the privacy and security of all the presented data, all the data may be stored in a node of a block chain.
It should be noted that the blockchain in the present invention is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The specific implementation of the computer-readable storage medium of the present invention is substantially the same as the above-mentioned specific implementation of the artificial intelligence based information recommendation method, and is not described herein again.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
It should be noted that, the above embodiments of the present invention may acquire and process related data based on an artificial intelligence technique. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
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 robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above, and includes instructions for enabling an electronic device (e.g., a mobile phone, a computer, an electronic apparatus, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An information recommendation method based on artificial intelligence is characterized by comprising the following steps:
when a login request of a personal portal website is received, analyzing the login request to obtain identification information of a user;
according to the identification information, searching personal information and user behavior data corresponding to the identification information in a database;
searching corresponding internet hot spot information according to the personal information and the user behavior data, and extracting topic information from the internet hot spot information in a keyword matching mode;
analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data;
and determining recommendation information according to the association degree between the topic information and each resource data, and recommending the recommendation information to the user.
2. The method of claim 1, wherein said parsing said login request to obtain identification information of the user when said login request of the personal portal is received comprises:
acquiring an identification attribute corresponding to the identification information according to the identification information;
converting the identification attribute according to a preset conversion rule to obtain an address identification code corresponding to the identification attribute;
and determining the user source of the user according to the address identification code.
3. The method as claimed in claim 1, wherein the searching for corresponding internet hotspot information according to the personal information and the user behavior data and extracting topic information from the internet hotspot information by means of keyword matching comprise:
obtaining network information ranked at the top in a search engine according to the website weight, and determining the network information as the internet hotspot information;
classifying the Internet hotspot information through a pre-trained article classification model to obtain a hotspot category to which each piece of Internet hotspot information belongs;
and extracting topic information from the Internet hot spot information in a keyword matching mode, and generating a corresponding relation between the hot spot category and the topic information.
4. The method of claim 1, wherein the analyzing the topic information and the resource data in the personal web portal through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data comprises:
extracting keywords corresponding to the resource data through the pre-trained information recommendation model;
and vectorizing each keyword to obtain a characterization vector corresponding to each resource data.
5. The method of claim 4, wherein the analyzing the topic information and the resource data in the personal web portal through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data further comprises:
calculating Euclidean distances between the topic information and each characterization vector;
and determining the association degree between the topic information and each characterization vector according to the Euclidean distance between the topic information and each characterization vector.
6. The method of claim 1, wherein the user behavior data comprises: the user corresponding attention information, input record information, collection information and browsing information; the personal information includes a name, an age, account information, and taste information of the user.
7. The method as claimed in any one of claims 1 to 5, wherein before analyzing the topic information and the resource data in the personal web portal through a pre-trained information recommendation model and obtaining the association degree between the topic information and each resource data, the method comprises:
learning sample data based on machine learning, and adjusting parameters of an information recommendation model in training in real time according to a learning result of the machine learning;
and when the convergence of the loss function corresponding to the information recommendation model in training is detected, obtaining the pre-trained information recommendation model.
8. An artificial intelligence-based information recommendation device, comprising:
the acquisition module is used for analyzing the login request to obtain the identification information of the user when the login request of the personal portal website is received;
the searching module is used for searching the personal information and the user behavior data corresponding to the identification information in a database according to the identification information;
the extraction module is used for searching corresponding Internet hot spot information according to the personal information and the user behavior data and extracting topic information from the Internet hot spot information in a keyword matching mode;
the analysis module is used for analyzing the topic information and the resource data in the personal portal website through a pre-trained information recommendation model to obtain the association degree between the topic information and each resource data;
and the recommending module is used for determining recommending information according to the association degree between the topic information and each resource data and recommending the recommending information to the user.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a program executable by the at least one processor to enable the at least one processor to perform the artificial intelligence based information recommendation method as recited in any one of claims 1 to 7.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores an artificial intelligence based information recommendation program, which when executed by a processor, implements the steps of the artificial intelligence based information recommendation method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116450925A (en) * | 2022-12-27 | 2023-07-18 | 深圳市网新新思软件有限公司 | User relationship analysis method and system based on artificial intelligence |
| CN117278508A (en) * | 2023-08-02 | 2023-12-22 | 中移互联网有限公司 | Recommendation method and device of 5G message chat robot and electronic equipment |
| CN117278508B (en) * | 2023-08-02 | 2024-09-27 | 中移互联网有限公司 | Recommendation method and device of 5G message chat robot and electronic equipment |
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2021
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116450925A (en) * | 2022-12-27 | 2023-07-18 | 深圳市网新新思软件有限公司 | User relationship analysis method and system based on artificial intelligence |
| CN116450925B (en) * | 2022-12-27 | 2023-12-15 | 深圳市网新新思软件有限公司 | User relationship analysis method and system based on artificial intelligence |
| CN117278508A (en) * | 2023-08-02 | 2023-12-22 | 中移互联网有限公司 | Recommendation method and device of 5G message chat robot and electronic equipment |
| CN117278508B (en) * | 2023-08-02 | 2024-09-27 | 中移互联网有限公司 | Recommendation method and device of 5G message chat robot and electronic equipment |
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