CN112800347A - Intelligent recommendation system and method based on intelligent media service platform - Google Patents
Intelligent recommendation system and method based on intelligent media service platform Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
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Abstract
The embodiment of the invention relates to the technical field of information recommendation, and particularly discloses an intelligent recommendation system and method based on an intelligent media service platform. In the intelligent recommendation system, extracting user keywords based on the current user historical data information, extracting preset keywords based on the preset data, and generating recommendation information pushed in a preset sequence according to the user keywords and the preset keywords; sending the recommendation information to the terminal, and displaying the recommendation information by the terminal; therefore, when the user arrives at a new place, the embodiment of the invention can obtain the recommendation information matched with the interest points of the surrounding users while obtaining the recommendation information matched with the interest points of the user based on the combination of the interest points of the user and the interest points of the surrounding other users, thereby avoiding the problem that the content recommended by the traditional recommendation method and the interest points of the user are too centralized, and being capable of well recommending the content really needed by the user.
Description
Technical Field
The embodiment of the invention relates to the technical field of information recommendation, in particular to an intelligent recommendation system and method based on an intelligent media service platform.
Background
With the increasing development of information technology, information recommendation based on user preferences, information concerned by history, information concerned by friends and friends, and the like has become an important content for current network technology applications. In the current scheme of information recommendation, the adopted mainstream recommendation method is an Item-Based collaborative filtering recommendation algorithm, and Based on the information recommendation method, because a user using a plurality of items has great influence on the overall recommendation, and the popular items are strongly associated with most other items, the popular items are easily pushed out, but the recommendation method has no followability, when the user arrives at a new place, the content recommended by the traditional recommendation method and the interest points of the user are too centralized, and the content really needed by the user cannot be well recommended for the user.
Disclosure of Invention
The embodiment of the invention aims to provide an intelligent recommendation system and method based on an intelligent media service platform, so as to solve the problems in the background technology.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
an intelligent recommendation system based on an intelligent media service platform comprises a terminal and a cloud end communicated with the terminal;
the terminal is used for acquiring basic information of a current user, wherein the basic information comprises historical data information and geographical position information; sending the basic information to a cloud end; receiving and displaying recommendation information returned by the cloud;
the cloud end is used for receiving the basic information, calling preset data matched with a current user in an information database according to the geographic position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information, extracting a user keyword based on the current user historical data information, extracting a preset keyword based on the preset data, and generating recommendation information pushed in a preset sequence according to the user keyword and the preset keyword; and sending the recommendation information to the terminal.
As a further limitation of the preferred embodiment of the present invention, the terminal specifically includes:
the information acquisition unit is used for acquiring basic information of a current user, wherein the basic information comprises historical data information and geographical position information;
the first sending unit is used for sending the basic information to a cloud end;
and the first receiving unit is used for receiving and displaying the recommendation information returned by the cloud.
As a further limitation of the preferred embodiment of the present invention, the cloud specifically includes:
a second receiving unit, configured to receive the basic information;
the data calling unit is used for calling preset data matched with the current user in an information database according to the geographic position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information;
a first extraction unit, configured to extract a user keyword based on the current user history data information;
a second extraction unit configured to extract a preset keyword based on the preset data;
the information generating unit is used for generating recommendation information pushed according to a preset sequence according to the user keywords and preset keywords;
and the second sending unit is used for sending the recommendation information to the terminal.
As a further limitation of the preferred embodiment of the present invention, the data retrieving unit specifically includes:
the traversing module is used for traversing all the preset position information in the information database;
the marking module is used for marking preset position information corresponding to the current user geographical position information;
and the confirming module is used for taking preset data information corresponding to the preset position information as preset data.
As a further limitation of the preferred embodiment of the present invention, the first extraction unit specifically comprises:
the first word segmentation module is used for performing word segmentation operation on all sentences of the current user historical data information text to obtain word units;
the first acquisition module is used for acquiring word characteristics of the word unit, sentence characteristics of the word unit in a corresponding historical data information text sentence and text characteristics of the word unit in the historical data information text;
and the first extraction module is used for extracting keywords from the sentences of each historical data information text by using the word characteristics, sentence characteristics and text characteristics of the word unit in each analysis sentence based on the machine learning model established by the machine learning algorithm.
As a further limitation of the preferred embodiment of the present invention, the second extraction unit specifically comprises:
the second word segmentation module is used for performing word segmentation operation on all sentences of a preset data text of a preset user to obtain word units;
the second acquisition module is used for acquiring word characteristics of the word unit, sentence characteristics of the word unit in a corresponding preset data text sentence and text characteristics of the word unit in the preset data text;
and the second extraction module is used for extracting keywords from the sentences of each preset data text by using the word characteristics, sentence characteristics and text characteristics of the word unit in each analysis sentence based on the machine learning model established by the machine learning algorithm.
As a further limitation of the preferred embodiment of the present invention, the information generating unit specifically includes:
the establishing module is used for establishing a recommended content library;
the first matching module is used for matching first recommended content corresponding to the user keywords in the recommended content library;
the second matching module is used for matching second recommended content corresponding to the preset keywords in the recommended content library;
the comparison module compares the first recommended content with the second recommended content;
the content definition module is used for taking a part of the first recommended content, which is overlapped with the second recommended content, as first priority recommended content, taking the rest part of the first recommended content as second priority recommended content, and taking the rest part of the second recommended content as third priority recommended content;
and the content pushing module is used for sequentially pushing the first priority recommended content, the second priority recommended content and the third priority recommended content.
An intelligent recommendation method based on an intelligent media service platform, the method comprising:
acquiring basic information of a current user, wherein the basic information comprises historical data information and geographical position information;
calling preset data matched with the current user in an information database according to the geographical position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information;
extracting a user keyword based on the current user historical data information;
extracting preset keywords based on the preset data;
and generating recommendation information which is sequenced according to a preset rule according to the user keywords and preset keywords.
As a further limitation of the preferred embodiment of the present invention, the step of retrieving preset data matched with the current user in the information database according to the geographic location information specifically includes:
traversing all preset position information in the information database;
marking preset position information corresponding to the current user geographical position information;
and using preset data information corresponding to the preset position information as preset data.
As a further limitation of the preferred embodiment of the present invention, the step of generating recommendation information to be pushed in a preset order according to the user keyword and a preset keyword specifically includes:
establishing a recommended content library;
matching first recommended content corresponding to the user keywords in the recommended content library;
matching second recommended content corresponding to the preset keywords in the recommended content library;
comparing the first recommended content and the second recommended content;
taking a part of the first recommended content and the second recommended content which are overlapped as first priority recommended content, taking the rest part of the first recommended content as second priority recommended content, and taking the rest part of the second recommended content as third priority recommended content;
and pushing the first priority recommended content, the second priority recommended content and the third priority recommended content in sequence.
Compared with the prior art, the invention has the beneficial effects that:
in the intelligent recommendation system provided by the embodiment of the invention, historical data information and geographical position information of a current user are acquired through a terminal; calling preset data matched with a current user in an information database according to the geographical position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information, extracting a user keyword based on the current user historical data information, extracting a preset keyword based on the preset data, and generating recommendation information pushed in a preset sequence according to the user keyword and the preset keyword; sending the recommendation information to the terminal, and displaying the recommendation information by the terminal; therefore, when the user arrives at a new place, the embodiment of the invention can obtain the recommendation information matched with the interest points of the surrounding users based on the combination of the interest points of the user and the interest points of the surrounding other users, and simultaneously can obtain the recommendation information matched with the interest points of the surrounding users, thereby enlarging the information acquisition of the user, avoiding the problem that the contents recommended by the traditional recommendation method and the interest points of the user are too centralized, and well recommending the really needed contents for the user.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 is a system architecture diagram of an intelligent recommendation system based on an intelligent media service platform according to an embodiment of the present invention.
Fig. 2 is a block diagram of an intelligent recommendation system based on an intelligent media service platform according to an embodiment of the present invention.
Fig. 3 is a block diagram of a data retrieving unit according to a third embodiment of the present invention.
Fig. 4 is a block diagram of a first extraction unit according to a third embodiment of the present invention.
Fig. 5 is a block diagram of a second extraction unit according to a fourth embodiment of the present invention.
Fig. 6 is a block diagram of an information generating unit according to a fifth embodiment of the present invention.
Fig. 7 is a flowchart of an intelligent recommendation method based on an intelligent media service platform according to a sixth embodiment of the present invention.
Fig. 8 is a sub-flow diagram of an intelligent recommendation method based on an intelligent media service platform according to a seventh embodiment of the present invention.
Fig. 9 is a sub-flow block diagram of an intelligent recommendation method based on an intelligent media service platform according to an eighth embodiment of the present invention.
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.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that, although the terms first, second, etc. may be used herein to describe various functional blocks in embodiments of the present invention, these functional blocks should not be limited by these terms. These terms are only used to distinguish one type of functional module from another. For example, a first sending unit may also be referred to as a second sending unit without departing from the scope of embodiments of the present invention, which does not necessarily require or imply any such actual relationship or order between such entities or operations. Similarly, the second transmission unit may also be referred to as the first transmission unit. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus 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, method, article, or apparatus.
It can be understood that, in the current scheme of information recommendation, the mainstream recommendation method adopted is Item-Based collaborative filtering recommendation algorithm, Based on the information recommendation method, since a user using many items has a great influence on the overall recommendation, and the hot items are strongly associated with most other items, the hot items are easily pushed out, but the recommendation method has no followability, when the user arrives at a new place, the content recommended by the traditional recommendation method is too concentrated with the interest points of the user, and the content really needed by the user cannot be well recommended for the user.
In order to solve the above problems, in the intelligent recommendation system provided in the embodiment of the present invention, historical data information and geographic location information of a current user are obtained through a terminal; calling preset data matched with a current user in an information database according to the geographical position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information, extracting a user keyword based on the current user historical data information, extracting a preset keyword based on the preset data, and generating recommendation information pushed in a preset sequence according to the user keyword and the preset keyword; sending the recommendation information to the terminal, and displaying the recommendation information by the terminal; therefore, when the user arrives at a new place, the embodiment of the invention can obtain the recommendation information matched with the interest points of the surrounding users based on the combination of the interest points of the user and the interest points of the surrounding other users, and simultaneously can obtain the recommendation information matched with the interest points of the surrounding users, thereby enlarging the information acquisition of the user, avoiding the problem that the contents recommended by the traditional recommendation method and the interest points of the user are too centralized, and well recommending the really needed contents for the user.
FIG. 1 is a system architecture diagram of an intelligent recommendation system based on an intelligent media service platform according to an embodiment of the present invention.
Specifically, the system architecture may include a cloud 200, and a plurality of terminals 100 in communication with the cloud 200 through a network;
the network may be a medium for providing a communication link between the terminal 100 and the cloud 200. In particular, the network may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
A user may interact with terminal 100 to effect the transfer of information, etc.
The terminal 100 may be hardware or software. When the terminal 100 is hardware, it may be various electronic devices having communication, information processing, and display screens, including but not limited to smart phones, tablet computers, e-book readers, MP3 players, MP4 players, laptop portable computers, desktop computers, and the like. When the terminal 100 is software, it can be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules, or as a single piece of software or software module. And is not particularly limited herein.
The first embodiment is as follows:
FIG. 2 shows a block diagram of an intelligent recommendation system based on an intelligent media service platform according to an embodiment of the present invention.
Specifically, the embodiment of the invention provides an intelligent recommendation system based on an intelligent media service platform, which comprises a terminal 100 and a cloud end 200 communicated with the terminal 100;
the terminal 100 is configured to acquire basic information of a current user, where the basic information includes historical data information and geographic location information; sending the basic information to a cloud end; receiving and displaying recommendation information returned by the cloud;
in the specific implementation of the embodiment of the present invention, after obtaining the authorization, the terminal 100 collects the basic information of the current user, and it can be understood that the basic information includes the historical data information of the current user in the use process of the terminal 100, such as the historical data information left when using the APP of the terminal 100; and under the condition of obtaining the authorization, the location of the current user is located through a location module configured by the terminal 100, so that the geographical location information of the current user is obtained.
Further, in the embodiment of the present invention, after obtaining the basic information of the current user, the terminal 100 sends the basic information to the cloud 200 through the communication module configured in the terminal 100, and after processing the basic information, the cloud 200 can return the recommendation information corresponding to the basic information to the terminal 100 and display the recommendation information through the display screen of the terminal 100, so that the current user can refer to the recommendation information conveniently.
The cloud 200 is used for receiving the basic information, calling preset data matched with a current user in an information database according to the geographic position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information, extracting a user keyword based on the current user historical data information, extracting a preset keyword based on the preset data, and generating recommendation information pushed in a preset sequence according to the user keyword and the preset keyword; and sending the recommendation information to the terminal.
Further, in a specific implementation of the embodiment of the present invention, the information database includes data information of a plurality of preset users, wherein after the terminal used by the preset user obtains the authorization of the preset user, the information data of the preset user is uploaded to the cloud, so as to form the information database including the information data of the plurality of preset users;
it can be understood that when the current user needs to perform information recommendation, the geographic position information of the current user can be acquired, and the corresponding preset position information is matched through the geographic position information, so that preset data of the preset user can be called correspondingly, and the preset data can be used as basic data for performing information recommendation on the current user;
specifically, in the embodiment of the present invention, a specific manner of retrieving preset data matched with the current user in the information database according to the geographic location information is as follows: traversing all preset position information in the information database; marking preset position information corresponding to the current user geographical position information; and using preset data information corresponding to the preset position information as preset data.
Furthermore, in the embodiment of the present invention, in a specific implementation of the step of generating the recommendation information pushed in the preset order according to the user keyword and the preset keyword, a recommended content library is first established based on an information database, and a first recommended content corresponding to the user keyword is matched in the recommended content library through a first matching module; then, matching second recommended content corresponding to the preset keywords in the recommended content library through a second matching module; preferably, the first recommended content and the second recommended content may be compared to find a portion where the first recommended content and the second recommended content overlap, and the portion where the first recommended content and the second recommended content overlap is used as a first priority recommended content by a content definition module, further, the remaining portion of the first recommended content is used as a second priority recommended content, and further, the remaining portion of the second recommended content is used as a third priority recommended content; the first priority recommended content, the second priority recommended content and the third priority recommended content jointly form recommended information, and the first priority recommended content, the second priority recommended content and the third priority recommended content are pushed in sequence.
Specifically, in a preferred embodiment provided by the present invention, the terminal 100 specifically includes:
the information acquiring unit 101 is configured to acquire basic information of a current user, where the basic information includes history data information and geographical location information;
in the specific implementation of the embodiment of the present invention, after obtaining the authorization, the terminal 100 collects the basic information of the current user, and it can be understood that the basic information includes the historical data information of the current user in the use process of the terminal 100, such as the historical data information left when using the APP of the terminal 100; and under the condition of obtaining the authorization, the location of the current user is located through a location module configured by the terminal 100, so that the geographical location information of the current user is obtained.
A first sending unit 102, configured to send the basic information to the cloud 200;
the first receiving unit 103 is configured to receive and display recommendation information returned by the cloud 200.
Specifically, in a preferred embodiment provided by the present invention, the cloud 200 specifically includes:
a second receiving unit 201, configured to receive the basic information;
a data retrieving unit 202, configured to retrieve preset data matched with a current user from an information database according to the geographic location information, where the information database includes preset location information of at least two preset users and preset data information corresponding to the preset location information;
a first extraction unit 203 for extracting a user keyword based on the current user history data information;
a second extracting unit 204, configured to extract a preset keyword based on the preset data;
an information generating unit 205, configured to generate recommendation information pushed in a preset order according to the user keyword and a preset keyword;
a second sending unit 206, configured to send the recommendation information to the terminal.
Example two:
fig. 3 shows a block diagram of a data retrieving unit 202 according to a third embodiment of the present invention.
Specifically, in a preferred embodiment provided by the present invention, the data retrieving unit 202 specifically includes:
the traversing module 2021 is configured to traverse all preset position information in the information database;
a marking module 2022, configured to mark preset location information corresponding to the current user geographical location information;
the determining module 2023 is configured to use preset data information corresponding to the preset position information as preset data.
Example three:
fig. 4 shows a block diagram of a first extraction unit according to a third embodiment of the present invention.
Specifically, in a preferred embodiment provided by the present invention, the first extraction unit 203 specifically includes:
the first word segmentation module 2031 is configured to perform word segmentation on all sentences of the current user history data information text to obtain word units;
a first obtaining module 2032, which obtains the word feature of the word unit, the sentence feature of the word unit in the corresponding historical data information text sentence, and the text feature of the word unit in the historical data information text;
the first extraction module 2033 is configured to, based on a machine learning model established by a machine learning algorithm, perform keyword extraction on the sentences of each historical data information text by using the word features, sentence features, and text features of the word units in each analysis sentence.
Example four:
fig. 5 shows a block diagram of a second extraction unit according to a fourth embodiment of the present invention.
Specifically, in a preferred embodiment provided by the present invention, the second extraction unit 204 specifically includes:
the second word segmentation module 2041 is configured to perform word segmentation on all sentences of a preset data text of a preset user to obtain word units;
a second obtaining module 2042, configured to obtain word features of the word unit, sentence features of the word unit in a corresponding preset data text sentence, and text features of the word unit in the preset data text;
the second extraction module 2043 is configured to perform keyword extraction on each sentence of the preset data text by using the word feature, the sentence feature, and the text feature of the word unit in each analysis sentence based on the machine learning model established by the machine learning algorithm.
Example five:
fig. 6 shows a block diagram of an information generating unit according to a fifth embodiment of the present invention.
Specifically, in a preferred embodiment provided by the present invention, the information generating unit 205 specifically includes:
an establishing module 2051, configured to establish a recommended content library;
a first matching module 2052, configured to match, in the recommended content library, first recommended content corresponding to the user keyword;
a second matching module 2053, configured to match a second recommended content corresponding to the preset keyword in the recommended content library;
a comparison module 2054 that compares the first recommended content and the second recommended content;
a content definition module 2055, configured to use a portion where the first recommended content and the second recommended content overlap as a first priority recommended content, use a remaining portion of the first recommended content as a second priority recommended content, and use a remaining portion of the second recommended content as a third priority recommended content;
a content pushing module 2056, configured to sequentially push the first priority recommended content, the second priority recommended content, and the third priority recommended content.
Example six:
FIG. 7 is a flow chart illustrating an intelligent recommendation method based on an intelligent media service platform according to a sixth embodiment of the present invention. Specifically, the embodiment of the invention provides an intelligent recommendation method based on an intelligent media service platform;
the method 300 includes:
step S301, acquiring basic information of a current user, wherein the basic information comprises historical data information and geographical position information;
step S302, preset data matched with a current user in an information database is called according to the geographical position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information;
step S303, extracting user keywords based on the current user historical data information; extracting preset keywords based on the preset data;
and step S304, generating recommendation information which is sequenced according to a preset rule according to the user keywords and preset keywords.
Example seven:
FIG. 8 shows a sub-flow diagram of an intelligent recommendation method based on an intelligent media service platform according to a seventh embodiment of the present invention. Specifically, in a preferred embodiment provided by the present invention, the step S302 of retrieving preset data matched with the current user in the information database according to the geographic location information specifically includes:
step S3021: traversing all preset position information in the information database;
step S3022: marking preset position information corresponding to the current user geographical position information;
step S3023: and using preset data information corresponding to the preset position information as preset data.
Example eight:
FIG. 9 is a block diagram illustrating another sub-flow of the intelligent recommendation method based on the intelligent media service platform according to an eighth embodiment of the present invention. Specifically, in an embodiment of the present invention, the step S304 of generating recommendation information to be pushed according to the preset order according to the user keyword and the preset keyword specifically includes:
step S3041, establishing a recommended content library;
step S3042, matching first recommended content corresponding to the user keyword in the recommended content library;
step S3043, matching second recommended content corresponding to the preset keywords in the recommended content library;
step S3044 of comparing the first recommended content and the second recommended content;
step S3045, using the overlapped part of the first recommended content and the second recommended content as a first priority recommended content, using the rest part of the first recommended content as a second priority recommended content, and using the rest part of the second recommended content as a third priority recommended content;
step S3046, sequentially pushing the first priority recommended content, the second priority recommended content, and the third priority recommended content.
The embodiment of the present invention further provides an apparatus, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the intelligent recommendation method based on the intelligent media service platform are implemented.
The embodiment of the invention also provides a storage medium, wherein the storage medium stores a computer program, and the computer program realizes the steps of the intelligent recommendation method based on the intelligent media service platform when being executed by a processor.
Illustratively, a computer program can be partitioned into one or more modules, which are stored in memory and executed by a processor to implement the present invention. One or more of the modules may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the terminal device.
Those skilled in the art will appreciate that the above description of the terminal device is merely exemplary and not limiting, and that more or fewer components than those described above may be included, or certain components may be combined, or different components may be included, such as input output devices, network access devices, buses, etc.
The processor may be a central processing unit, but may also be other general purpose processors, digital signal processors, application specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or the like. The general-purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is the control center of the terminal equipment and connects the various parts of the entire user terminal using various interfaces and lines.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the terminal device by operating or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory 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 according to the use of the berth-status display system, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card, a secure digital card, a flash memory card, at least one magnetic disk storage device, a flash memory device, or other volatile solid state storage device.
The terminal device integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the modules/units in the system according to the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium and used by a processor to implement the functions of the system embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
In summary, in the intelligent recommendation system provided in the embodiment of the present invention, the historical data information and the geographic location information of the current user are obtained through the terminal; calling preset data matched with a current user in an information database according to the geographical position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information, extracting a user keyword based on the current user historical data information, extracting a preset keyword based on the preset data, and generating recommendation information pushed in a preset sequence according to the user keyword and the preset keyword; sending the recommendation information to the terminal, and displaying the recommendation information by the terminal; therefore, when the user arrives at a new place, the embodiment of the invention can obtain the recommendation information matched with the interest points of the surrounding users based on the combination of the interest points of the user and the interest points of the surrounding other users, and simultaneously can obtain the recommendation information matched with the interest points of the surrounding users, thereby enlarging the information acquisition of the user, avoiding the problem that the contents recommended by the traditional recommendation method and the interest points of the user are too centralized, and well recommending the really needed contents for the user.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An intelligent recommendation system based on an intelligent media service platform is characterized by comprising a terminal and a cloud end communicated with the terminal;
the terminal is used for acquiring basic information of a current user, wherein the basic information comprises historical data information and geographical position information; sending the basic information to a cloud end; receiving and displaying recommendation information returned by the cloud;
the cloud end is used for receiving the basic information, calling preset data matched with a current user in an information database according to the geographic position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information, extracting a user keyword based on the current user historical data information, extracting a preset keyword based on the preset data, and generating recommendation information pushed in a preset sequence according to the user keyword and the preset keyword; and sending the recommendation information to the terminal.
2. The intelligent recommendation system based on the intelligent media service platform as claimed in claim 1, wherein the terminal specifically comprises:
the information acquisition unit is used for acquiring basic information of a current user, wherein the basic information comprises historical data information and geographical position information;
the first sending unit is used for sending the basic information to a cloud end;
and the first receiving unit is used for receiving and displaying the recommendation information returned by the cloud.
3. The intelligent recommendation system based on the intelligent media service platform according to claim 2, wherein the cloud specifically comprises:
a second receiving unit, configured to receive the basic information;
the data calling unit is used for calling preset data matched with the current user in an information database according to the geographic position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information;
a first extraction unit, configured to extract a user keyword based on the current user history data information;
a second extraction unit configured to extract a preset keyword based on the preset data;
the information generating unit is used for generating recommendation information pushed according to a preset sequence according to the user keywords and preset keywords;
and the second sending unit is used for sending the recommendation information to the terminal.
4. The intelligent recommendation system based on intelligent media service platform according to claim 3, wherein the data retrieving unit specifically comprises:
the traversing module is used for traversing all the preset position information in the information database;
the marking module is used for marking preset position information corresponding to the current user geographical position information;
and the confirming module is used for taking preset data information corresponding to the preset position information as preset data.
5. The intelligent recommendation system based on intelligent media service platform according to claim 3, wherein the first extraction unit specifically comprises:
the first word segmentation module is used for performing word segmentation operation on all sentences of the current user historical data information text to obtain word units;
the first acquisition module is used for acquiring word characteristics of the word unit, sentence characteristics of the word unit in a corresponding historical data information text sentence and text characteristics of the word unit in the historical data information text;
and the first extraction module is used for extracting keywords from the sentences of each historical data information text by using the word characteristics, sentence characteristics and text characteristics of the word unit in each analysis sentence based on the machine learning model established by the machine learning algorithm.
6. The intelligent recommendation system based on intelligent media service platform according to claim 3, wherein the second extraction unit specifically comprises:
the second word segmentation module is used for performing word segmentation operation on all sentences of a preset data text of a preset user to obtain word units;
the second acquisition module is used for acquiring word characteristics of the word unit, sentence characteristics of the word unit in a corresponding preset data text sentence and text characteristics of the word unit in the preset data text;
and the second extraction module is used for extracting keywords from the sentences of each preset data text by using the word characteristics, sentence characteristics and text characteristics of the word unit in each analysis sentence based on the machine learning model established by the machine learning algorithm.
7. The intelligent recommendation system based on intelligent media service platform according to any of claims 3-5, characterized in that the information generation unit specifically comprises:
the establishing module is used for establishing a recommended content library;
the first matching module is used for matching first recommended content corresponding to the user keywords in the recommended content library;
the second matching module is used for matching second recommended content corresponding to the preset keywords in the recommended content library;
the comparison module compares the first recommended content with the second recommended content;
the content definition module is used for taking a part of the first recommended content, which is overlapped with the second recommended content, as first priority recommended content, taking the rest part of the first recommended content as second priority recommended content, and taking the rest part of the second recommended content as third priority recommended content;
and the content pushing module is used for sequentially pushing the first priority recommended content, the second priority recommended content and the third priority recommended content.
8. An intelligent recommendation method based on an intelligent media service platform is characterized by comprising the following steps:
acquiring basic information of a current user, wherein the basic information comprises historical data information and geographical position information;
calling preset data matched with the current user in an information database according to the geographical position information, wherein the information database comprises preset position information of at least two preset users and preset data information corresponding to the preset position information;
extracting a user keyword based on the current user historical data information;
extracting preset keywords based on the preset data;
and generating recommendation information which is sequenced according to a preset rule according to the user keywords and preset keywords.
9. The intelligent recommendation method based on the intelligent media service platform as claimed in claim 8, wherein the step of retrieving the preset data matched with the current user in the information database according to the geographical location information specifically comprises:
traversing all preset position information in the information database;
marking preset position information corresponding to the current user geographical position information;
and using preset data information corresponding to the preset position information as preset data.
10. The intelligent recommendation method based on the intelligent media service platform according to claim 8, wherein the step of generating recommendation information to be pushed in a preset order according to the user keyword and a preset keyword specifically comprises:
establishing a recommended content library;
matching first recommended content corresponding to the user keywords in the recommended content library;
matching second recommended content corresponding to the preset keywords in the recommended content library;
comparing the first recommended content and the second recommended content;
taking a part of the first recommended content and the second recommended content which are overlapped as first priority recommended content, taking the rest part of the first recommended content as second priority recommended content, and taking the rest part of the second recommended content as third priority recommended content;
and pushing the first priority recommended content, the second priority recommended content and the third priority recommended content in sequence.
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