CN108197239B - Method and device for generating point of interest network topological graph - Google Patents
Method and device for generating point of interest network topological graph Download PDFInfo
- Publication number
- CN108197239B CN108197239B CN201711478084.XA CN201711478084A CN108197239B CN 108197239 B CN108197239 B CN 108197239B CN 201711478084 A CN201711478084 A CN 201711478084A CN 108197239 B CN108197239 B CN 108197239B
- Authority
- CN
- China
- Prior art keywords
- resource files
- resource
- correlation
- operating
- files
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/16—File or folder operations, e.g. details of user interfaces specifically adapted to file systems
- G06F16/168—Details of user interfaces specifically adapted to file systems, e.g. browsing and visualisation, 2d or 3d GUIs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/16—File or folder operations, e.g. details of user interfaces specifically adapted to file systems
-
- 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
- G06F16/904—Browsing; Visualisation therefor
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Human Computer Interaction (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Information Transfer Between Computers (AREA)
Abstract
In the embodiment of the present invention, a method for generating a network topology map of a point of interest is provided, including: calculating the relevancy of any two resource files in N resource files, and associating the any two resource files according to the relevancy, wherein N is an integer greater than 1; establishing a network topological graph of the points of interest after each resource file in the N resource files is associated; in the scheme, the relevancy among the resource files is calculated, the resource files are associated through the relevancy, and the resource files are not manually associated, so that the accuracy of classifying the resource files is improved, and the accuracy of searching the resource files by a user is improved.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a method and a device for generating a network topological graph of a point of interest.
Background
With the development of the internet and the popularization of intelligent terminals, a large number of application programs are produced at the same time. In order to meet the needs of people, resources in the application programs are also increasingly abundant, for example, picture resources, audio resources, video resources and the like, and the video resources also include a large number of short video resources, such as uploaded resources of a second-taking application program, a beautiful-taking application program, a micro-vision application program and the like.
Due to the fact that resources are extremely rich, in the process of using the application program, a large amount of time is often consumed to search interested resources from dazzling resources, and the defect of low efficiency exists, and user experience is further affected.
In order to enable a user to find interesting resources as soon as possible, various resources are generally classified at present, and then the user searches for the interesting resources according to categories.
Disclosure of Invention
In view of the above problems, the present invention is provided to provide a method and an apparatus for generating a point of interest network topology map, which overcome or at least partially solve the above problems, so as to solve the defects in the prior art that the categories corresponding to the resource files are not matched and the accuracy of the resource files searched by the user according to the categories is low.
According to a first aspect of the present invention, there is provided a method for generating a point of interest network topology map, including: calculating the relevancy of any two resource files in N resource files, and associating the any two resource files according to the relevancy, wherein N is an integer greater than 1; and establishing a network topological graph of the points of interest after each resource file in the N resource files is associated.
According to a second aspect of the present invention, there is provided an apparatus for generating a network topology map of points of interest, comprising: the computing unit is used for computing the correlation degree of any two resource files in N resource files, wherein N is an integer greater than 1; the association unit is used for associating the two arbitrary resource files according to the correlation; and the establishing unit is used for establishing the network topological graph of the interest point after each resource file in the N resource files is associated.
In the embodiment of the present invention, a method for generating a network topology map of a point of interest is provided, including: calculating the relevancy of any two resource files in N resource files, and associating the any two resource files according to the relevancy, wherein N is an integer greater than 1; establishing a network topological graph of the points of interest after each resource file in the N resource files is associated; in the scheme, the relevancy among the resource files is calculated, the resource files are associated through the relevancy, and the resource files are not manually associated, so that the accuracy of classifying the resource files is improved, and the accuracy of searching the resource files by a user is improved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a proposed method for generating a point of interest network topology graph according to an embodiment of the invention;
FIG. 2A is a schematic diagram of a proposed topology of a point of interest network according to an embodiment of the invention;
FIG. 2B is another schematic diagram of a proposed point of interest network topology according to an embodiment of the invention;
fig. 3 is a schematic diagram of an apparatus for generating a network topology map of points of interest according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Fig. 1 schematically shows a flowchart of a method 10 of generating a point of interest network topology according to an embodiment of the present invention. As shown in fig. 1, the method 10 may include steps 100, 110.
Step 100: calculating the relevancy of any two resource files in N resource files, and associating the any two resource files according to the relevancy, wherein N is an integer greater than 1;
step 110: and establishing a network topological graph of the points of interest after each resource file in the N resource files is associated.
For example, there are 5 videos: video 1, video 2, video 3, video 4, video 5, calculating the correlation between any two of the 5 videos, such as correlation 1 between video 1 and video 2, correlation 2 between video 1 and video 3, correlation 3 between video 1 and video 4, correlation 4 between video 1 and video 5, correlation 5 between video 2 and video 3, correlation 6 between video 2 and video 4, correlation 7 between video 2 and video 5, correlation 8 between video 3 and video 4, correlation 9 between video 3 and video 5, correlation 10 between video 4 and video 5, correlating video 1 and video 2 according to correlation 1, correlating video 1 and video 3 according to correlation 2, correlating video 1 and video 4 according to correlation 3, and so on, correlating video 4 and video 4 according to correlation 10, And (5) correlating the videos, and finally generating a point-of-interest network topological graph shown in the figure 2A.
In the embodiment of the present invention, there are various ways to calculate the relevancy of any two resource files in the N resource files, and optionally, the following ways may be adopted:
calculating the initial correlation of any two resource files, and taking the initial correlation as the correlation of any two resource files; or
And calculating the initial correlation of any two resource files, optimizing the initial correlation to obtain the target correlation, and taking the target correlation as the correlation of any two resource files.
That is, the resource files may be associated according to their initial relevance, or may also be associated according to their target relevance. If the correlation degrees are different, the distances between the videos are different, the larger the correlation degree is, the smaller the distance between the videos is, and the smaller the correlation degree is, the larger the distance between the videos is. For example, assuming that the videos shown in fig. 2A are associated according to the initial correlation, if the initial correlation between the videos is optimized to obtain the target correlation, the videos are associated according to the target correlation to obtain the network topology of the point of interest shown in fig. 2B.
In the embodiment of the present invention, when calculating the initial correlation of any two resource files in the N resource files, optionally, the following method may be adopted:
and calculating a first probability of continuously operating any two resource files, wherein the first probability is used as the initial correlation, and the continuously operating means that other resource files are not operated in the time interval of operating any two resource files.
For different types of resource files, the corresponding operation modes are different, and for audio or video, the operation can be referred to playing; the operation may refer to browsing pictures or text, but the above is only an example, but the operation is not limited thereto.
For example, when calculating the initial correlation of the video 1 and the video 2, the probability of continuously playing the video 1 and the video 2 may be used as the initial correlation of the video 1 and the video 2, where continuously playing the video 1 and the video 2 means that the video 1 is directly played after the video 1 is played, or means that the video 1 is directly played after the video 2 is played, and no other video is played in the middle.
In the embodiment of the present invention, there are various ways to calculate the first probability of continuously operating any two resource files, and optionally, the following ways may be adopted:
determining a first number of times of continuously operating the any two resource files;
determining a second time for operating each resource file in the N resource files independently, and taking the sum of the second times as a third time;
and taking the ratio of the first times to the third times as the first probability.
For example, when 5 videos are total, when calculating the first probabilities of video 1 and video 2, it is necessary to determine a first number of times, e.g. 500, for continuously playing video 1 and video 2, and then determine a second number of times 1, e.g. 1000, for individually playing video 2, e.g. 2000, for individually playing video 3, e.g. 3000, for individually playing video 4, e.g. 4000, and a second number of times 5, e.g. 5000, for individually playing video 5, wherein the sum of the second number of times 1, the second number of times 2, the second number of times 3, the second number of times 4, and the second number of times 5 is a third number, e.g. 15000, and the ratio of the first number of times to the third number is a first probability, e.g. the ratio of 500 to 15000 is a first probability, i.e. the first probability is 0.033.
It should be noted that the first number may be the sum of the number of times that the video 1 is played directly from the video 2 and the number of times that the video 1 is played directly from the video 2.
In the embodiment of the present invention, when the first probability of continuously operating any two resource files is calculated, the following method may also be adopted:
determining a first number of users operating the any two resource files;
determining a second user number for operating a first resource file and a third user number for operating a second resource file in any two resource files;
and taking the ratio of the first user number to the sum of the second user number and the third user number as the first probability.
For example, when 5 videos are total, and the first probability of video 1 and video 2 is calculated, it is necessary to determine a first number of users, such as 500 users, playing video 1 and video 2 continuously, determine a second number of users 1, such as 2000 users, playing video 2 individually, and a second number of users 2, such as 8000 times, wherein the sum of the second number of users 1 and the second number of users 2 is 10000 users, and use the ratio of the first number of users to the sum of the second number of users 1 and the second number of users 2 as the first probability, such as the ratio of 500 to 10000 as the first probability, that is, the first probability is 0.05.
Wherein, the first number of users may be the sum of the number of videos 1 and 2 played continuously and the number of videos 2 and 1 played continuously.
In the embodiment of the present invention, when the initial correlation is optimized to obtain the target correlation, optionally, the following method may be adopted:
respectively calculating the probability of operating the second resource file of any two resource files at intervals of 1,2, … … and N after operating the first resource file of any two resource files to obtain a plurality of second probabilities;
and calculating the target correlation degree according to the plurality of second probabilities and the initial correlation degree.
For example, there are 5 videos: when optimizing the initial correlation between the videos 1 and 2, in addition to calculating the initial correlation between the videos 1 and 2, calculating at least one video playing video 2 among the videos 3, 4 and 5 after the video 1 is played, if the video 1 is played, playing the video 3, playing the second probability 1 of the video 2, playing the video 4 after the video 1 is played, playing the second probability 2 of the video 2 again, playing the video 5 after the video 1 is played, playing the second probability 3 of the video 2 again, playing the videos 3 and 4 after the video 1 is played, playing the second probability 4 of the video 2 again, playing the videos 3 and 5 after the video 1 is played, playing the videos 3 and 5 after the video 2 is played, playing the videos 4 and 5 after the video 1 is played, playing the videos 4 and 2 after the video 1 is played, And the video 5 plays the second probability 6 of the video 2 again, after the video 1 is played, the videos 3, 4 and 5 are played, the second probability 7 of the video 2 is played again, and the target correlation is obtained by optimizing the initial correlation according to the second probability 1, the second probability 2, the second probability 3, the second probability 4, the second probability 5, the second probability 6 and the second probability 7.
In the embodiment of the present invention, when the initial correlation is optimized to obtain the target correlation, the following method may be further adopted:
aiming at a first resource file in any two resource files, determining a first group of multimedia resources which continuously operate with a second resource file in any two resource files and a second group of multimedia resources which continuously operate with a third multimedia resource, wherein the third multimedia resource is a file in the N resource files except the any two resource files and continuously operates with the first resource file;
and optimizing the initial correlation degree according to the coincidence degree of the first group of multimedia resources and the second group of multimedia resources to obtain the target correlation degree.
For example, when the target correlation degree is obtained by optimizing the initial correlation degree between the video 1 and the video 2, for the video 1, a first group of videos continuously played with the video 2 is determined, for example, the first group of videos includes the video 3, the video 4 and the video 5, and a second group of videos continuously played with the video 3 is determined, for example, the second group of videos includes the video 2, the video 4 and the video 5, wherein the video 3 is continuously played with the video 1, the coincidence degree of the first group of videos and the second group of videos is calculated, and the initial correlation degree of the video 1 and the video 2 is optimized according to the coincidence degree to obtain the target correlation degree.
The above example is described by taking only video 3 and video 1 of video 3, video 4 and video 5 as an example, but in practical applications, it is possible that video 5 and video 1 are also played continuously, and at this time, there are a plurality of second group videos, such as second group video 1 played continuously with video 3 and second group video 2 played continuously with video 5, and at this time, the initial correlation is optimized to obtain the target correlation according to the coincidence degree 1 of the first group video and the second group video 1 and the coincidence degree 2 of the first group video and the second group video 2.
There may of course be more sets of the second set of videos, but the principle is the same as the above-described process and will not be described in detail here.
In the scheme provided by the embodiment of the invention, the relevancy among the resource files is calculated, and the resource files are associated through the relevancy, but not manually associated, so that the accuracy of classifying the resource files is improved, and the accuracy of searching the resource files by a user is improved.
Referring to fig. 3, in an embodiment of the present invention, an apparatus 30 for generating a network topology map of a point of interest is further provided, including:
a calculating unit 300, configured to calculate a correlation between any two resource files of N resource files, where N is an integer greater than 1;
an associating unit 310, configured to associate the two arbitrary resource files according to the correlation;
the establishing unit 320 is configured to establish a point of interest network topology map after each resource file of the N resource files is associated.
For example, there are 5 videos: video 1, video 2, video 3, video 4, video 5, calculating the correlation between any two of the 5 videos, such as correlation 1 between video 1 and video 2, correlation 2 between video 1 and video 3, correlation 3 between video 1 and video 4, correlation 4 between video 1 and video 5, correlation 5 between video 2 and video 3, correlation 6 between video 2 and video 4, correlation 7 between video 2 and video 5, correlation 8 between video 3 and video 4, correlation 9 between video 3 and video 5, correlation 10 between video 4 and video 5, correlating video 1 and video 2 according to correlation 1, correlating video 1 and video 3 according to correlation 2, correlating video 1 and video 4 according to correlation 3, and so on, correlating video 4 and video 4 according to correlation 10, And (5) correlating the videos, and finally generating a point-of-interest network topological graph shown in the figure 2A.
In this embodiment of the present invention, when the calculating unit 300 calculates the correlation between any two resource files in the N resource files, optionally, the following method may be adopted:
calculating the initial correlation of any two resource files, and taking the initial correlation as the correlation of any two resource files; or
And calculating the initial correlation of any two resource files, optimizing the initial correlation to obtain the target correlation, and taking the target correlation as the correlation of any two resource files.
That is, the resource files may be associated according to their initial relevance, or may also be associated according to their target relevance. If the correlation degrees are different, the distances between the videos are different, the larger the correlation degree is, the smaller the distance between the videos is, and the smaller the correlation degree is, the larger the distance between the videos is. For example, assuming that the videos shown in fig. 2A are associated according to the initial correlation, if the initial correlation between the videos is optimized to obtain the target correlation, the videos are associated according to the target correlation to obtain the network topology of the point of interest shown in fig. 2B.
In this embodiment of the present invention, when the calculating unit 300 calculates the initial correlation of any two resource files in the N resource files, optionally, the following method may be adopted:
and calculating a first probability of continuously operating any two resource files, wherein the first probability is used as the initial correlation, and the continuously operating means that other resource files are not operated in the time interval of operating any two resource files.
For different types of resource files, the corresponding operation modes are different, and for audio or video, the operation can be referred to playing; the operation may refer to browsing pictures or text, but the above is only an example, but the operation is not limited thereto.
In this embodiment of the present invention, when the calculating unit 300 calculates the first probability of continuously operating the two arbitrary resource files, optionally, the following manner may be adopted:
determining a first number of times of continuously operating the any two resource files;
determining a second number of times of independently operating each resource file in the N resource files, and taking the sum of all the second numbers as a third number of times;
and taking the ratio of the first times to the third times as the first probability.
In this embodiment of the present invention, when the calculating unit 300 calculates the first probability of continuously operating the two arbitrary resource files, optionally, the following manner may be adopted:
determining a first number of users operating the any two resource files;
determining a second user number for operating a first resource file and a third user number for operating a second resource file in any two resource files;
and taking the ratio of the first user number to the sum of the second user number and the third user number as the first probability.
In this embodiment of the present invention, when the calculating unit 300 optimizes the initial correlation to obtain the target correlation, optionally, the following method may be adopted:
respectively calculating the probability of operating the second resource file of any two resource files at intervals of 1,2, … … and N after operating the first resource file of any two resource files to obtain a plurality of second probabilities;
and optimizing the initial correlation according to the plurality of second probabilities to obtain the target correlation.
In this embodiment of the present invention, when the calculating unit 300 optimizes the initial correlation to obtain the target correlation, optionally, the following method may be adopted:
aiming at a first resource file in any two resource files, determining a first group of multimedia resources which continuously operate with a second resource file in any two resource files and a second group of multimedia resources which continuously operate with a third multimedia resource, wherein the third multimedia resource is a file in the N resource files except the any two resource files and continuously operates with the first resource file;
and optimizing the initial correlation degree according to the coincidence degree of the first group of multimedia resources and the second group of multimedia resources to obtain the target correlation degree.
In the scheme provided by the embodiment of the invention, the relevancy among the resource files is calculated, and the resource files are associated through the relevancy, but not manually associated, so that the accuracy of classifying the resource files is improved, and the accuracy of searching the resource files by a user is further improved.
A1, a method for generating a point of interest network topology map, comprising:
calculating the relevancy of any two resource files in N resource files, and associating the any two resource files according to the relevancy, wherein N is an integer greater than 1;
and establishing a network topological graph of the points of interest after each resource file in the N resource files is associated.
A2, the method of A1, calculating the relevancy of any two resource files in the N resource files, including:
calculating the initial correlation of any two resource files, and taking the initial correlation as the correlation of any two resource files; or
And calculating the initial correlation of any two resource files, optimizing the initial correlation to obtain the target correlation, and taking the target correlation as the correlation of any two resource files.
A3, the method of A2, calculating the initial relevancy of any two resource files of the N resource files, comprising:
and calculating a first probability of continuously operating any two resource files, wherein the first probability is used as the initial correlation, and the continuously operating means that other resource files are not operated in the time interval of operating any two resource files.
A4, the method of A3, calculating a first probability of operating the arbitrary two resource files consecutively, comprising:
determining a first number of times of continuously operating the any two resource files;
determining a second number of times of independently operating each resource file in the N resource files, and taking the sum of all the second numbers as a third number of times;
and taking the ratio of the first times to the third times as the first probability.
A5, the method of A3, calculating a first probability of operating the arbitrary two resource files consecutively, comprising:
determining a first number of users operating the any two resource files;
determining a second user number for operating a first resource file and a third user number for operating a second resource file in any two resource files;
and taking the ratio of the first user number to the sum of the second user number and the third user number as the first probability.
A6, the method according to any one of a2-a5, wherein optimizing the initial correlation to obtain the target correlation comprises:
respectively calculating the probability of operating the second resource file of any two resource files at intervals of 1,2, … … and N after operating the first resource file of any two resource files to obtain a plurality of second probabilities;
and optimizing the initial correlation according to the plurality of second probabilities to obtain the target correlation.
A7, the method according to any one of a2-a5, wherein optimizing the initial correlation to obtain the target correlation comprises:
aiming at a first resource file in any two resource files, determining a first group of multimedia resources which continuously operate with a second resource file in any two resource files and a second group of multimedia resources which continuously operate with a third multimedia resource, wherein the third multimedia resource is a file in the N resource files except the any two resource files and continuously operates with the first resource file;
and optimizing the initial correlation degree according to the coincidence degree of the first group of multimedia resources and the second group of multimedia resources to obtain the target correlation degree.
A8, an apparatus for generating a topology map of a point of interest network, comprising:
the computing unit is used for computing the correlation degree of any two resource files in N resource files, wherein N is an integer greater than 1;
the association unit is used for associating the two arbitrary resource files according to the correlation;
and the establishing unit is used for establishing the network topological graph of the interest point after each resource file in the N resource files is associated.
A9, the apparatus of A8, the calculating unit calculating the relevancy of any two resource files of the N resource files, comprising:
calculating the initial correlation of any two resource files, and taking the initial correlation as the correlation of any two resource files; or
And calculating the initial correlation of any two resource files, optimizing the initial correlation to obtain the target correlation, and taking the target correlation as the correlation of any two resource files.
A10, the apparatus of A9, the calculating unit calculating the initial relevancy of any two of the N resource files, comprising:
and calculating a first probability of continuously operating any two resource files, wherein the first probability is used as the initial correlation, and the continuously operating means that other resource files are not operated in the time interval of operating any two resource files.
A11, the apparatus of A10, the calculating unit calculating a first probability of operating the arbitrary two resource files consecutively, comprising:
determining a first number of times of continuously operating the any two resource files;
determining a second number of times of independently operating each resource file in the N resource files, and taking the sum of all the second numbers as a third number of times;
and taking the ratio of the first times to the third times as the first probability.
A12, the apparatus of A10, the calculating unit calculating a first probability of operating the arbitrary two resource files consecutively, comprising:
determining a first number of users operating the any two resource files;
determining a second user number for operating a first resource file and a third user number for operating a second resource file in any two resource files;
and taking the ratio of the first user number to the sum of the second user number and the third user number as the first probability.
A13, the apparatus as in any one of a9-a12, the calculating unit optimizing the initial correlation to obtain the target correlation, comprising:
respectively calculating the probability of operating the second resource file of any two resource files at intervals of 1,2, … … and N after operating the first resource file of any two resource files to obtain a plurality of second probabilities;
and optimizing the initial correlation according to the plurality of second probabilities to obtain the target correlation.
A14, the apparatus as in any one of a9-a12, the calculating unit optimizing the initial correlation to obtain the target correlation, comprising:
aiming at a first resource file in any two resource files, determining a first group of multimedia resources which continuously operate with a second resource file in any two resource files and a second group of multimedia resources which continuously operate with a third multimedia resource, wherein the third multimedia resource is a file in the N resource files except the any two resource files and continuously operates with the first resource file;
and optimizing the initial correlation degree according to the coincidence degree of the first group of multimedia resources and the second group of multimedia resources to obtain the target correlation degree.
A15, an apparatus for generating a topology map of a point of interest network, comprising:
one or more processors;
a memory;
a program stored in the memory, which when executed by the one or more processors, causes the processors to perform a method as any one of a1-a7 recites.
A16, a computer readable storage medium storing a program which, when executed by a processor, causes the processor to carry out the method of any one of a1-a 7.
The methods and apparatus provided herein are not inherently related to any particular computer, virtual machine system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of an embodiment may be adaptively changed and disposed in one or more apparatuses other than the embodiment. Several modules of embodiments may be combined into one module or unit or assembly and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-assemblies. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or modules are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments may be used in any combination.
Various apparatus embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the modules in an apparatus according to embodiments of the present invention. The present invention may also be embodied as apparatus programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (12)
1. A method for generating a point of interest network topology map, comprising:
calculating the relevancy of any two resource files in N resource files, and associating the any two resource files according to the relevancy, wherein N is an integer greater than 1;
establishing a network topological graph of the points of interest after each resource file in the N resource files is associated;
wherein, calculating the relevancy of any two resource files in the N resource files comprises:
calculating the initial correlation of any two resource files, and taking the initial correlation as the correlation of any two resource files; or
Calculating the initial correlation of any two resource files, optimizing the initial correlation to obtain a target correlation, and taking the target correlation as the correlation of any two resource files;
optimizing the initial correlation to obtain a target correlation, comprising:
aiming at a first resource file in any two resource files, determining a first group of multimedia resources which continuously operate with a second resource file in any two resource files and a second group of multimedia resources which continuously operate with a third multimedia resource, wherein the third multimedia resource is a file in the N resource files except the any two resource files and continuously operates with the first resource file;
and optimizing the initial correlation degree according to the coincidence degree of the first group of multimedia resources and the second group of multimedia resources to obtain a target correlation degree.
2. The method of claim 1, calculating an initial relevance of any two of the N resource files, comprising:
and calculating a first probability of continuously operating any two resource files, wherein the first probability is used as the initial correlation, and the continuously operating means that other resource files are not operated in the time interval of operating any two resource files.
3. The method of claim 2, calculating a first probability of operating the any two resource files in succession, comprising:
determining a first number of times of continuously operating the any two resource files;
determining a second number of times of independently operating each resource file in the N resource files, and taking the sum of all the second numbers as a third number of times;
and taking the ratio of the first times to the third times as the first probability.
4. The method of claim 2, calculating a first probability of operating the any two resource files in succession, comprising:
determining a first number of users operating the any two resource files;
determining a second user number for operating a first resource file and a third user number for operating a second resource file in any two resource files;
and taking the ratio of the first user number to the sum of the second user number and the third user number as the first probability.
5. The method of any one of claims 1 to 4, optimizing the initial correlation to obtain a target correlation, comprising:
respectively calculating the probability of operating the second resource file of any two resource files at intervals of 1,2, … … and N after operating the first resource file of any two resource files to obtain a plurality of second probabilities;
and optimizing the initial correlation according to the plurality of second probabilities to obtain a target correlation.
6. An apparatus for generating a point of interest network topology map, comprising:
the computing unit is used for computing the relevancy of any two resource files in N resource files, wherein N is an integer greater than 1;
the association unit is used for associating the two arbitrary resource files according to the correlation;
the establishing unit is used for establishing a network topological graph of the points of interest after each resource file in the N resource files is associated;
wherein, the calculating unit calculates the relevancy of any two resource files in the N resource files, and comprises:
calculating the initial correlation of any two resource files, and taking the initial correlation as the correlation of any two resource files; or
Calculating the initial correlation of any two resource files, optimizing the initial correlation to obtain a target correlation, and taking the target correlation as the correlation of any two resource files;
the calculating unit optimizes the initial correlation to obtain a target correlation, and the method comprises the following steps:
aiming at a first resource file in any two resource files, determining a first group of multimedia resources which continuously operate with a second resource file in any two resource files and a second group of multimedia resources which continuously operate with a third multimedia resource, wherein the third multimedia resource is a file in the N resource files except the any two resource files and continuously operates with the first resource file;
and optimizing the initial correlation degree according to the coincidence degree of the first group of multimedia resources and the second group of multimedia resources to obtain a target correlation degree.
7. The apparatus of claim 6, the computing unit to compute an initial degree of correlation for any two of the N resource files, comprising:
and calculating a first probability of continuously operating any two resource files, wherein the first probability is used as the initial correlation, and the continuously operating means that other resource files are not operated in the time interval of operating any two resource files.
8. The apparatus of claim 7, the computing unit to compute a first probability of operating the arbitrary two resource files consecutively, comprising:
determining a first number of times of continuously operating the any two resource files;
determining a second number of times of independently operating each resource file in the N resource files, and taking the sum of all the second numbers as a third number of times;
and taking the ratio of the first times to the third times as the first probability.
9. The apparatus of claim 7, the computing unit to compute a first probability of operating the arbitrary two resource files consecutively, comprising:
determining a first number of users operating the any two resource files;
determining a second user number for operating a first resource file and a third user number for operating a second resource file in any two resource files;
and taking the ratio of the first user number to the sum of the second user number and the third user number as the first probability.
10. The apparatus according to any one of claims 6 to 9, wherein the calculating unit optimizes the initial correlation to obtain a target correlation, and comprises:
respectively calculating the probability of operating the second resource file of any two resource files at intervals of 1,2, … … and N after operating the first resource file of any two resource files to obtain a plurality of second probabilities;
and optimizing the initial correlation according to the plurality of second probabilities to obtain a target correlation.
11. An apparatus for generating a point of interest network topology map, comprising:
one or more processors;
a memory;
a program stored in the memory, which when executed by the one or more processors, causes the processors to perform the method of any one of claims 1-5.
12. A computer-readable storage medium storing a program which, when executed by a processor, causes the processor to perform the method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711478084.XA CN108197239B (en) | 2017-12-29 | 2017-12-29 | Method and device for generating point of interest network topological graph |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711478084.XA CN108197239B (en) | 2017-12-29 | 2017-12-29 | Method and device for generating point of interest network topological graph |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108197239A CN108197239A (en) | 2018-06-22 |
| CN108197239B true CN108197239B (en) | 2021-08-24 |
Family
ID=62586564
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711478084.XA Active CN108197239B (en) | 2017-12-29 | 2017-12-29 | Method and device for generating point of interest network topological graph |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108197239B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110646000A (en) * | 2018-06-26 | 2020-01-03 | 百度在线网络技术（北京）有限公司 | Navigation method, navigation device and storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101984424A (en) * | 2010-10-26 | 2011-03-09 | 浙江工商大学 | Mass inter-media index method |
| WO2013159176A1 (en) * | 2012-04-27 | 2013-10-31 | Research In Motion Limited | Systems and methods for establishing and using a personal linking graph |
| CN104008180B (en) * | 2014-06-09 | 2017-04-12 | 北京奇虎科技有限公司 | Association method of structural data with picture, association device thereof |
| WO2017147396A1 (en) * | 2016-02-24 | 2017-08-31 | Data2Discovery | Object oriented system and method having semantic substructures for machine learning |
-
2017
- 2017-12-29 CN CN201711478084.XA patent/CN108197239B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN108197239A (en) | 2018-06-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108804532B (en) | Query intention mining method and device and query intention identification method and device | |
| CN106096050A (en) | A kind of method and apparatus of video contents search | |
| CN104809154A (en) | Method and device for recommending information | |
| US20160188725A1 (en) | Method and System for Enhanced Content Recommendation | |
| US10885107B2 (en) | Music recommendation method and apparatus | |
| US20080077569A1 (en) | Integrated Search Service System and Method | |
| WO2015032353A1 (en) | Video recommendation method and device | |
| US20110252065A1 (en) | Apparatus and method for semantic-based search and semantic metadata providing server and method of operating the same | |
| WO2016107326A1 (en) | Search recommending method and device based on search terms | |
| US11176143B2 (en) | Location-aware content detection | |
| CN103701875A (en) | Multimedia file pushing method and device | |
| CN104090887A (en) | Music search method and device | |
| US20080189334A1 (en) | Method of Global Popularity based Prioritization in Information Engine with Consumer ==Author and Dynamic Web models for global, multimedia, and mobile Internet | |
| CN109255073B (en) | Personalized recommendation method and device and electronic equipment | |
| CN113407773A (en) | Short video intelligent recommendation method and system, electronic device and storage medium | |
| CN108540860B (en) | Video recall method and device | |
| CN105354318A (en) | File searching method and device | |
| US9268861B2 (en) | Method and system for recommending relevant web content to second screen application users | |
| US10394838B2 (en) | App store searching | |
| CN106777201B (en) | Method and device for sorting recommended data on search result page | |
| CN110515631B (en) | Method for generating application installation data packet, server and computer storage medium | |
| CN108197239B (en) | Method and device for generating point of interest network topological graph | |
| CN106649750B (en) | Searching method and device for multi-meaning term entry | |
| CN115358643A (en) | Message-based upstream and downstream document generation method and device and storage medium | |
| CN105589863B (en) | Searching method, data processing method, device and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |