CN113822594A - Interest point grading determination method, electronic equipment and computer program product - Google Patents
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Abstract
The embodiment of the application provides a score determining method of an interest point, electronic equipment and a computer program product. The method comprises the following steps: acquiring travel data with the interest points as destinations; obtaining a popularity score of the interest point based on the travel data of the interest point; obtaining a goodness score of the interest point at least based on the comment score of the interest point; and determining a travel score of the interest point at least based on the heat score and the goodness score of the interest point. The method has higher reliability.
Description
Technical Field
The embodiment of the application relates to the technical field of data mining, in particular to a score determining method for points of interest, electronic equipment and a computer program product.
Background
The user can inquire the information Of a Point Of Interest (POI) by using the existing application software with a map navigation function and living service application software. For example, the user may search the store in the application software by using the store name, and the application software may present the searched information of the location, score, comment, and the like of the store to the user, so as to provide a reference for the user to go out or consume. In the process of researching the existing interest point scoring method, the prior art mainly determines the score of the interest point according to comments and scores fed back by users aiming at the interest point, but the number of the users submitting the user feedback is limited, the user feedback has the subjective feeling of the users, and the problem that the characteristics of the interest point cannot be objectively and comprehensively reflected exists in the process of determining the score of the interest point only by the user feedback.
Disclosure of Invention
In view of the above, embodiments of the present application provide a method for determining a score of a point of interest, an electronic device, and a computer program product, so as to at least partially solve the above problem.
According to a first aspect of the embodiments of the present application, there is provided a method for determining a score of a point of interest, including: acquiring travel data with the interest points as destinations; obtaining a popularity score of the interest point based on the travel data of the interest point; obtaining a goodness score of the interest point at least based on the comment score of the interest point; and determining a travel score of the interest point at least based on the heat score and the goodness score of the interest point.
According to a second aspect of the embodiments of the present application, there is provided a score determining apparatus for a point of interest, including: the first acquisition module is used for acquiring travel data with the interest point as a destination; the second acquisition module is used for acquiring the popularity score of the interest point based on the travel data of the interest point; the third acquisition module is used for acquiring the goodness score of the interest point at least based on the comment score of the interest point; and the fourth obtaining module is used for determining the travel score of the interest point at least based on the heat score and the goodness score of the interest point.
According to a third aspect of embodiments of the present application, there is provided an electronic apparatus, including: the processor, the memory and the communication interface complete mutual communication through the communication bus; the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the point of interest scoring determination method of the first aspect.
According to a fourth aspect of embodiments of the present application, there is provided a computer program product, which when executed by a processor, implements the method for determining a score of a point of interest of the first aspect.
According to the method, the trip scoring is based on the comment scoring, and the popularity scoring determined from objective trip data is integrated, so that the trip scoring information not only reflects the objective preference degree of a trip, but also does not lose effective information of subjective evaluation, and the accuracy and reliability of the trip scoring information are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present application, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1A is a scene schematic diagram of a method for determining a score of a point of interest according to an embodiment of the present application;
fig. 1B is a flowchart of a method for determining a score of a point of interest according to an embodiment of the present application;
fig. 2A is a flowchart of a score determining method for a point of interest according to a second embodiment of the present application;
fig. 2B is a scene schematic diagram of a score determining method for points of interest according to a second embodiment of the present application;
fig. 3 is a structural diagram of a score determining apparatus for points of interest according to a third embodiment of the present application;
fig. 4 is a structural diagram of an electronic device according to a fourth embodiment of the present application.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the embodiments of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application shall fall within the scope of the protection of the embodiments in the present application.
The following further describes specific implementations of embodiments of the present application with reference to the drawings of the embodiments of the present application.
Example one
The embodiment of the application provides a score determining method for an interest point. For convenience of understanding, an application scenario of the method provided in the first embodiment of the present application is described, and fig. 1A is shown to show an interface diagram for showing scores of points of interest and related information in application software with a map navigation function. The method can facilitate the travelers to quickly acquire the relevant information of the interest points so as to quickly screen out the interest points meeting the demands of the travelers. A Point Of Interest (also referred to as a POI) represents a real-world location, such as a store, a mall, or other location, without limitation.
The conventional interest point score is usually determined by the comment content and the score filled by a traveler, and the score is easily influenced by the false comment content and the score, so that the interest point score cannot reflect the true condition. In order to solve the problem, the trip score of the point of interest shown in fig. 1A integrates information of trip data related to the point of interest, and since the trip data is real data and it is difficult to make false data, objectivity and reliability of the trip score can be improved.
The method provided in the first embodiment of the present application is described in detail below with reference to the scenario shown in fig. 1A, and it should be noted that fig. 1A is only an application scenario of the method provided in the first embodiment of the present application, and does not represent that the method must be applied to the scenario shown in fig. 1A.
FIG. 1B shows a schematic flow chart of the steps of one such method, including the steps of:
step S102: and acquiring travel data with the interest points as destinations.
In a feasible manner, the travel data may include vehicle navigation data, walking navigation data, riding navigation data, and the like, and may also include travel data of other scenes, which is not limited in this respect. Travel data with the interest points as destinations can be screened out according to destination information or route point information of the travel data.
Step S104: and obtaining the heat degree score of the interest point based on the travel data of the interest point.
Since the quantity of the travel data of the interest point in a period reflects the popularity of the interest point in the period to a certain extent, the popularity score of the interest point can be determined based on the quantity of the travel data of the interest point.
For example, the travel data quantity of all the interest points which are similar to the interest points and located in the same geographic area is sorted, and then the heat degree score of the interest points is determined according to the sorting result.
Step S106: and obtaining a goodness score of the interest point at least based on the comment score of the interest point.
In one possible approach, the review score of the user for the point of interest is taken as the goodness score. Because the comment scores of the users comprise subjective feelings of the users who have visited the interest points, the comment scores also reflect the goodness of the interest points to a certain extent. If the comment scores given by a large number of users to the interest point are high, the interest point is better represented. On the contrary, if the comment scores given by a large number of users to the interest point are low, the interest point is represented to be low in goodness.
Step S108: and determining a travel score of the interest point at least based on the heat score and the goodness score of the interest point.
In order to improve reliability and accuracy of trip scoring, in the embodiment, trip scoring is determined at least according to scoring based on heat and scoring based on goodness, so that objective trip data is fused, subjective feelings of users on interest points are fused, trip scoring is more real and comprehensive, and the problem that comment scoring is easily influenced by invalid and false user comments is effectively solved.
In a feasible manner, the heat weight corresponding to the heat score and the goodness weight corresponding to the goodness score can be acquired, the product of the heat score and the heat weight and the product of the goodness weight and the goodness score are respectively calculated, and then the sum of the products is used as the trip score.
The popularity weight and the goodness weight can be set based on experience and can also be obtained through machine learning model learning, so that the accuracy of the popularity weight and the goodness weight can be improved, and the accuracy of trip scoring can be further improved.
In the embodiment, the determined trip score integrates objective trip data and subjective feeling of the user on the interest point, so that the trip score is more real and comprehensive, and the problem that the comment score is easily influenced by invalid and false user comments is effectively solved.
According to the method provided by the embodiment of the application, the trip score is based on the comment score, and the popularity score determined from objective trip data is integrated, so that the trip score information not only reflects the objective preference degree of a trip, but also does not lose effective information of subjective evaluation, and the accuracy and reliability of the trip score information are improved.
Example two
Referring to fig. 2A, a flowchart illustrating steps of a score determining method for a point of interest according to a second embodiment of the present application is shown.
In this embodiment, the method includes the steps of:
step S200 a: and acquiring the search data of the interest points.
The search data includes, but is not limited to, the number of searches at different search entries (e.g., search bar, search perimeter entries, etc.). Of course, the search data may further include a search time period, a search location, and the like according to different needs, which is not limited.
Step S200 b: and determining the index score of the interest point according to the attribute data of the interest point and the search data.
The attribute data includes the geographic location of the point of interest, the category, whether there is a picture, whether there is a phone call, and so on.
The index score expressed in numerical form can be obtained by processing attribute data, search data, and the like. The index score serves as a basic score for biased usability, and not only indicates the preference of a traveler for the interest points, but also indicates the richness of the attributes of the interest points.
In one possible approach, the index score is obtained by the following process:
process A: and determining index data of the interest points according to the attribute data and the search data.
Since the attribute data includes data such as a geographical position, a category, whether or not there is a picture, and whether or not there is a telephone, which are not easily quantized, the data such as whether or not there is a picture, whether or not there is a telephone, and the like can be quantized into the abundance data in an appropriate manner in order to obtain more accurate index data. In addition, the attribute data may include other data, such as the number of trips, and the like.
When the index data is obtained, the interest points and other interest points in the same category in the same geographic area (the geographic area may be determined as needed, for example, in the same area, or the same city, etc.) may be sorted according to the attribute data and the search data, and then the index data is determined according to the sorting result.
In a specific example, the interest point is a museum in area a, and the other interest points may be museums in area a as well. When determining the index data, sorting the search times of all similar interest points in the area A according to the sequence from large to small, and further determining the search ranking. The ratio of the search rank to the total number of all points of interest is calculated as the search score. Scores corresponding to the travel data and the abundance data in the attribute data can be obtained in a similar manner. These scores are then weighted and summed as index data. In this embodiment, the exponent data is represented by a value in the interval of 0 to 10.
Optionally, for data with a large difference in the number of different points of interest, such as search data and travel data, the data may be sorted after being smoothed.
One particular smoothing process may be to base 10 logarithms of the search data (or other data to be smoothed) and then sort by logarithms. This allows for better data smoothness without loss of variance between data.
And a process B: an index score is determined from the index data.
The index score may be expressed as: log (x) +4, where log (x) is the logarithm of the exponential data to base e and x is the exponential data. The score of the index is guaranteed to be in the numerical interval of 0-5 by adding 4 to log (x). Therefore, the difference between different interest points can be smoothed, the data distribution is smoother, and the difference can be kept.
The distribution interval of the index scores may be adjusted as necessary, and is not limited to the range exemplified in the present embodiment.
Step S202: and acquiring travel data with the interest points as destinations.
In a feasible manner, the travel data may include vehicle navigation data, walking navigation data, riding navigation data, and the like, and may also include travel data of other scenes, which is not limited in this respect. Travel data with the interest points as destinations can be screened out according to destination information or route point information of the travel data.
Step S204: and obtaining the heat degree score of the interest point based on the travel data of the interest point.
In one possible approach, step S204 may be implemented as: acquiring the quantity of travel data with the same category as the interest points and other interest points in the same geographic area as destinations; sequencing all the interest points which have the same category as the interest points and are in the same geographic area according to the sequence of the quantity of the travel data from large to small so as to determine the travel ranking of the interest points; and determining the heat degree score of the interest points according to the appearance ranking and the total number of all the interest points.
If the interest point is the chinese cabbage of the area a, the other interest points may be other chinese cabbage in the area a. All points of interest may be other points of interest and the sum of the points of interest, i.e., all of the kalanchoes in area a. The ranking of the trips obtained by sorting the interest points according to the number of the trips from big to small shows the preference degree of the trips to the interest points.
To obtain an accurate heat score and reduce storage and computational load, the heat score can be expressed as: (1-trip ranking/total number of all points of interest) × 2+ 3. This allows the heat score to be mapped to a numerical interval of 3-5.
Step S206: and obtaining a goodness score of the interest point at least based on the comment score of the interest point.
In one possible approach, the user's review score may be directly used as the goodness score.
Alternatively, in another possible manner, to further improve the accuracy of the goodness score, step S206 may be implemented as: and determining a goodness score of the interest point based on the travel data of the interest point and the comment score. Therefore, objective trip data and relatively subjective comment scoring can be integrated, and the good scoring is more accurate and the integrated information is richer.
In the first case, determining the goodness score of the point of interest based on the travel data of the point of interest and the review score may be implemented as: obtaining a comment score of the interest point; determining the repeated trip quantity corresponding to the interest point according to the trip data of the interest point, and acquiring the repeated trip quantity of other interest points which have the same category as the interest point and are in the same geographic area; sequencing all the interest points which have the same category as the interest points and are in the same geographic area according to the sequence of the repeated travel quantity from large to small so as to determine the repeated travel ranking of the interest points; and determining the goodness score of the interest point according to the repeated travel sequence and the comment score of the interest point.
The repeated travel number is the number of travelers who take the interest point as a destination and have travel times larger than or equal to a set time value. The number-of-times setting value may be determined as needed, for example, 2, 3, or 5, etc. If the set number of times is 2, the number of repeated trips may be the number of travelers who arrive at the point of interest 2 times or more in a period of time. The number of repeated trips of other points of interest can be obtained in the same manner, and therefore, the description is omitted.
And sequencing all interest points according to the row repeating quantity from large to small to obtain a row ranking, and then obtaining a repeated row score according to (1-row ranking/the total quantity of all interest points) × 2+ 3. And weighting and summing the repeated travel score and the comment score of the interest point with the corresponding weights respectively to obtain a goodness score.
In the second case, determining the goodness score of the interest point based on the travel data of the interest point and the review score may be implemented as: obtaining a comment score of the interest point; determining the trip quantity corresponding to the interest point according to the trip data of the interest point, and acquiring the trip quantity of other interest points which have the same category as the interest point and are in the same geographic area; sequencing all interest points which have the same category as the interest points and are in the same geographic area according to the sequence of the travel quantity from large to small so as to determine the travel ranking of the interest points; and determining the goodness score of the interest point according to the trip sorting and the comment score of the interest point.
The trip travel number is the number of travelers who take the interest point as a destination and have travel distances greater than or equal to a distance set value. The distance setting value may be determined as needed, for example, 10km, 20km, or 50km, or the like. If the distance setting value is 10km, the number of the special trips may be the number of travelers who reach the point of interest after a distance of 10km or more in a period of time. The trip quantities of other points of interest can be obtained in the same or similar manner, and are not described in detail.
And sequencing the quantity of the trip travel of all the interest points in a descending order to obtain the trip ranking of the interest points. The trip score is then determined as (1-trip rank/total number of all points of interest) × 2+ 3. And weighting and summing the trip score and the comment score of the interest point with the corresponding weights respectively to obtain a goodness score.
In the third case, the trip repetition score and the trip detail score can be obtained respectively, then the trip detail score and the review score of the interest point are weighted and summed, the trip repetition score and the review score are weighted and summed, and then the two summation results are taken to take the mean value as the goodness score, so that more information can be synthesized, and the accuracy of the goodness score can be improved.
Step S208: and determining a travel score of the interest point at least based on the heat score and the goodness score of the interest point.
In the case of obtaining the index score, step S208 may be implemented as: and determining a travel score of the interest point based on the heat score, the goodness score and the index score of the interest point.
For example, based on the weights corresponding to the heat score, the goodness score and the index score respectively, the heat score, the goodness score and the index score are weighted and calculated to obtain the travel score of the interest point.
The travel score may be expressed as: (first weight index score) + (second weight heat score) + (third weight heat score). Wherein the sum of the first weight, the second weight and the third weight is 1.
Optionally, in order to increase the richness of the display information, in addition to determining the travel score, the following steps may be performed:
step S210: and acquiring any one of travel scores, heat scores or goodness scores of other interest points which have the same category as the interest points and are in the same geographic area.
The obtaining mode of the trip score, the heat score and the goodness score of other interest points is the same as or similar to the obtaining mode of the interest point, and therefore, the obtaining mode is not repeated.
Step S212: determining a travel ranking score or a heat ranking score or a good ranking score of the interest points based on any one of the total number of all interest points of the category within the geographic area, the travel score or the heat score or the good score of all the interest points.
For example, a trip ranking score may be obtained by: based on the obtained travel scores of the interest points and the travel scores of other interest points, sorting the travel scores of all the interest points in a descending order to obtain a first ranking, and then according to an expression: (1-first ranking/total interest points) × 100%, a trip ranking score is determined. The trip ranking score can be displayed in a mode of being 91% higher than the Sichuan cuisine in the same city, wherein 91% is the trip ranking score.
The popularity ranking score may be obtained by: based on the heat scores of the interest points and the heat scores of other interest points, sorting the heat scores of all the interest points from big to small to obtain a second ranking, and then according to an expression: (1-second rank/total interest points) × 100%, a hotness ranking score is determined. Presentation information may be generated based on the determined popularity ranking score, such as "over 81% homogeneous". Wherein 81% is the obtained popularity ranking score.
The goodness ranking score may be obtained by: based on the goodness scores of the interest points and the goodness scores of other interest points, sorting the goodness scores of all the interest points from big to small to obtain a third ranking, and then according to an expression: (1-third ranking/total interest points) × 100%, a goodness ranking score is determined. Presentation information may be generated based on the determined goodness ranking score, such as "over 61% homogeneous". 61% of them are the goodness ranking scores.
A schematic diagram showing the trip score, the popularity score, the goodness score, the trip ranking score, the popularity ranking score, the goodness ranking score and the like in the application software is shown in fig. 2B.
It should be noted that, in order to improve the generality of the trip rating and ensure that the differences of the interest points of different categories are not lost, when determining the trip rating, the local navigation or different-place navigation of the traveler, information on whether the interest point is in a familiar place or a frequent place of the user, and the like may be referred to, and whether the navigation time is a festival or an abnormal event may be determined.
The method determines the trip score, the heat score, the goodness score and the like of the interest point based on the trip data, and the trip data is real and objective data, so the reliability of the trip score determined based on the method is higher, the objective data and the subjective comment data of a traveler are well fused, and the problems that the score of the interest point is determined by conventional comment data depending on the traveler, the quality of the comment data is difficult to guarantee, and the cost for obtaining the interest point corresponding to the comment data is extremely high are solved.
The scheme is also based on the heat degree score, the goodness score, the trip score and other interest points of the same category for ranking, so that the trip score ranking, the heat degree score ranking, the goodness score ranking and the like are given, the important information of the interest points can be known clearly, and the possibility of misleading the subjective comments is effectively reduced.
When the score is displayed, each score of the interest point can be displayed in multiple dimensions, so that the problem of single information in a conventional mode of only displaying scores and comment contents which are written by a performer subjectively is solved. The traveler can make a quick decision based on rich information, and the decision making efficiency is improved.
EXAMPLE III
Referring to fig. 3, a block diagram of a score determination apparatus for a point of interest according to a third embodiment of the present application is shown.
The device for determining the score of the interest point comprises the following steps: a first obtaining module 302, configured to obtain travel data with the point of interest as a destination; a second obtaining module 304, configured to obtain a popularity score of the point of interest based on the travel data of the point of interest; a third obtaining module 306, configured to obtain a goodness score of the interest point based on at least the review score of the interest point; a fourth obtaining module 308, configured to determine a travel score of the point of interest based on at least the heat score and the goodness score of the point of interest.
Optionally, the apparatus further comprises: a fifth obtaining module 300a, configured to obtain search data of the point of interest; a first determining module 300b, configured to determine an index score of the interest point according to the attribute data of the interest point and the search data; the fourth obtaining module 308 is configured to determine a travel score of the interest point based on the heat score, the goodness score and the index score of the interest point.
Optionally, the fourth obtaining module 308 is configured to perform weighted calculation on the heat score, the goodness score and the index score based on weights corresponding to the heat score, the goodness score and the index score respectively, so as to obtain a travel score of the interest point.
Optionally, the apparatus further comprises: a sixth obtaining module 310, configured to obtain any one of a travel score, a popularity score, or a goodness score of another point of interest that is the same as the category of the point of interest and is in the same geographic area; a second determining module 312, configured to determine a travel ranking score or a heat ranking score or a goodness ranking score of the interest point based on any one of the total number of all interest points of the category in the geographic area, and the travel score or the heat score or the goodness score of all interest points.
Optionally, the second obtaining module 304 is configured to obtain the quantity of travel data with the same category as the point of interest and with other points of interest in the same geographic area as the destination; sequencing all the interest points which have the same category as the interest points and are in the same geographic area according to the sequence of the quantity of the travel data from large to small so as to determine the travel ranking of the interest points; and determining the heat degree score of the interest points according to the travel ranking and the total number of all the interest points.
Optionally, the third obtaining module 306 is configured to determine a goodness score of the point of interest based on the travel data of the point of interest and the review score.
Optionally, the third obtaining module 306 is configured to obtain a review score of the interest point; determining the repeated trip quantity and/or the trip quantity corresponding to the interest point according to the trip data of the interest point, wherein the repeated trip quantity is the quantity of travelers taking the interest point as a destination and having trip times larger than or equal to a set value of times, and the trip quantity is the quantity of travelers taking the interest point as a destination and having trip distances larger than or equal to a set value of distances; acquiring the repeated travel quantity and/or the special travel quantity of other interest points which have the same category as the interest points and are in the same geographical area; sequencing all interest points which have the same category as the interest points and are in the same geographic area according to the sequence of the repeated travel number and/or the specific travel number from large to small so as to determine the repeated travel ranking and/or the specific travel ranking of the interest points; and determining a goodness score of the interest point according to the repeated travel sequence and/or the trip rank and by combining the comment scores of the interest point.
The device for determining interest point information of the present embodiment can achieve the corresponding effect of the foregoing method, and therefore, the detailed description is omitted.
Example four
A fourth embodiment of the present application provides an electronic device, configured to execute the method described in the foregoing embodiment, and referring to fig. 4, a schematic structural diagram of an electronic device according to the fourth embodiment of the present application is shown.
As shown in fig. 4, the electronic device 40 may include: a processor (processor)402, a Communications Interface 404, a memory 406, and a Communications bus 408.
Wherein:
the processor 402, communication interface 404, and memory 406 communicate with each other via a communication bus 408.
A communication interface 404 for communicating with other electronic devices or servers.
The processor 402 is configured to execute the program 410, and may specifically execute relevant steps in the scoring determination method for a point of interest in any one of the above-mentioned embodiments.
In particular, program 410 may include program code comprising computer operating instructions.
The processor 402 may be a processor CPU, or an application Specific Integrated circuit (asic), or one or more Integrated circuits configured to implement embodiments of the present application. The intelligent device comprises one or more processors which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 406 for storing a program 410. Memory 406 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 410 may be specifically configured to be executed by the processor 402 to implement the scoring determination method for the interest point described in the first embodiment. For specific implementation of each step in the program 410, reference may be made to corresponding steps and corresponding descriptions in units in the foregoing embodiment of the method for determining a score of a point of interest, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
EXAMPLE five
Based on the method described in the first embodiment, a fifth embodiment of the present application provides a computer storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the method described in the first embodiment.
EXAMPLE six
Based on the methods described in the foregoing embodiments, a sixth embodiment of the present application provides a computer program product, which when executed by a processor implements the method described in the embodiments.
It should be noted that, according to the implementation requirement, each component/step described in the embodiment of the present application may be divided into more components/steps, and two or more components/steps or partial operations of the components/steps may also be combined into a new component/step to achieve the purpose of the embodiment of the present application.
The above-described methods according to embodiments of the present application may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk, or a magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the methods described herein may be stored in such software processes on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware such as an ASIC or FPGA. It will be appreciated that the computer, processor, microprocessor controller or programmable hardware includes memory components (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the point of interest scoring determination methods described herein. Further, when a general purpose computer accesses code for implementing the point of interest scoring determination methods shown herein, execution of the code transforms the general purpose computer into a special purpose computer for performing the point of interest scoring determination methods shown herein.
Those of ordinary skill in the art will appreciate that the various illustrative elements and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the embodiments of the present application.
The above embodiments are only used for illustrating the embodiments of the present application, and not for limiting the embodiments of the present application, and those skilled in the relevant art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present application, so that all equivalent technical solutions also belong to the scope of the embodiments of the present application, and the scope of patent protection of the embodiments of the present application should be defined by the claims.
Claims (10)
1. A method for determining scores of points of interest comprises the following steps:
acquiring travel data with the interest points as destinations;
obtaining a popularity score of the interest point based on the travel data of the interest point;
obtaining a goodness score of the interest point at least based on the comment score of the interest point;
and determining a travel score of the interest point at least based on the heat score and the goodness score of the interest point.
2. The method of claim 1, wherein the method further comprises:
acquiring search data of the interest points;
determining an index score of the interest point according to the attribute data of the interest point and the search data;
determining a travel score of the interest point at least based on the heat score and the goodness score of the interest point, specifically comprising:
and determining a travel score of the interest point based on the heat score, the goodness score and the index score of the interest point.
3. The method of claim 2, wherein the determining a travel score for the point of interest based on the heat score, the goodness score, and the index score for the point of interest comprises:
and performing weighted calculation on the heat score, the goodness score and the index score based on the weights corresponding to the heat score, the goodness score and the index score respectively to obtain the travel score of the interest point.
4. The method of claim 3, wherein the method further comprises:
acquiring any one of travel scores, heat scores or goodness scores of other interest points which have the same category as the interest points and are in the same geographic area;
determining a travel ranking score or a heat ranking score or a good ranking score of the interest points based on any one of the total number of all interest points of the category within the geographic area, the travel score or the heat score or the good score of all the interest points.
5. The method according to any one of claims 1-4, wherein the obtaining a heat score for the point of interest based on the travel data of the point of interest comprises:
acquiring the quantity of travel data with the same category as the interest points and other interest points in the same geographic area as destinations;
sequencing all the interest points which have the same category as the interest points and are in the same geographic area according to the sequence of the quantity of the travel data from large to small so as to determine the travel ranking of the interest points;
and determining the heat degree score of the interest points according to the travel ranking and the total number of all the interest points.
6. The method according to any one of claims 1 to 4, wherein the obtaining of the point of interest goodness score based on at least the point of interest review scores is in particular:
and determining a goodness score of the interest point based on the travel data of the interest point and the comment score.
7. The method of claim 6, wherein the determining a goodness score for the point of interest based on the travel data for the point of interest and the review score comprises:
obtaining a comment score of the interest point;
determining the repeated trip quantity and/or the trip quantity corresponding to the interest point according to the trip data of the interest point, wherein the repeated trip quantity is the quantity of travelers taking the interest point as a destination and having trip times larger than or equal to a set value of times, and the trip quantity is the quantity of travelers taking the interest point as a destination and having trip distances larger than or equal to a set value of distances;
acquiring the repeated travel quantity and/or the special travel quantity of other interest points which have the same category as the interest points and are in the same geographical area;
sequencing all interest points which have the same category as the interest points and are in the same geographic area according to the sequence of the repeated travel number and/or the specific travel number from large to small so as to determine the repeated travel ranking and/or the specific travel ranking of the interest points;
and determining a goodness score of the interest point according to the repeated travel sequence and/or the trip rank and by combining the comment scores of the interest point.
8. An apparatus for scoring a point of interest, comprising:
the first acquisition module is used for acquiring travel data with the interest point as a destination;
the second acquisition module is used for acquiring the popularity score of the interest point based on the travel data of the interest point;
the third acquisition module is used for acquiring the goodness score of the interest point at least based on the comment score of the interest point;
and the fourth obtaining module is used for determining the travel score of the interest point at least based on the heat score and the goodness score of the interest point.
9. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the point of interest score determining method according to any one of claims 1-8.
10. A computer program product which, when executed by a processor, implements a method of score determination for points of interest as claimed in any one of claims 1 to 8.
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