CN108985898B - Site scoring method and device and computer readable storage medium - Google Patents

Site scoring method and device and computer readable storage medium Download PDF

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CN108985898B
CN108985898B CN201810763165.2A CN201810763165A CN108985898B CN 108985898 B CN108985898 B CN 108985898B CN 201810763165 A CN201810763165 A CN 201810763165A CN 108985898 B CN108985898 B CN 108985898B
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张广驰
樊静窈
崔苗
林凡
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Guangdong University of Technology
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Abstract

The embodiment of the invention discloses a site scoring method, a site scoring device and a computer readable storage medium, which are used for acquiring data information of a target site; the data information can comprise data information collected by the unmanned equipment and historical data information collected from a network; classifying the data information by using a data classification model to obtain classified characteristic information; determining a scoring vector of a target place according to the received user demand information and the initial feature score corresponding to each feature information; and finally determining the score value of the target place according to the score vector of the target place and the score vector of the starting place. The score value of the target place is determined under the condition that the user requirements and the influence degree of the starting place on the target place are fully considered, and the higher the score value is, the higher the probability that the target place meets the actual requirements of the user is. When the place is recommended according to the score value, the recommended place can better meet the requirements of the user.

Description

Site scoring method and device and computer readable storage medium
Technical Field
The invention relates to the technical field of resource recommendation, in particular to a place scoring method, a place scoring device and a computer-readable storage medium.
Background
With the continuous development of science and technology, the science and technology in the world is widely used in the aspects of our lives, and the requirements of people on the construction of spiritual culture are higher and higher. For example, most people on holidays want to go out of home and travel with relatives and friends for play. How to select a target site that meets the user's needs is a difficult problem. Or the user wants to select a restaurant to eat, how to recommend the restaurant meeting the taste of the user is a problem.
In the prior art, a site scoring mode is often sorted depending on historical user scoring, and a complete scoring system is lacked. This way of recommendation often results in a recommendation that is not in accordance with the user's requirements.
Therefore, how to make the recommendation result more meet the user requirement is a problem to be solved urgently by the technical personnel in the field.
Disclosure of Invention
The embodiment of the invention aims to provide a place scoring method, a place scoring device and a computer readable storage medium, which can enable a recommendation result to better meet the requirements of users.
In order to solve the above technical problem, an embodiment of the present invention provides a site scoring method, including:
acquiring data information of a target place; the data information comprises data information collected by the unmanned equipment and historical data information collected from a network;
classifying the data information by using a data classification model to obtain classified characteristic information;
determining a scoring vector of the target place according to the received user demand information and the initial feature value corresponding to each feature information;
and determining the score value of the target place according to the score vector of the target place and the score vector of the starting place.
Optionally, the determining, according to the received user demand information and the initial feature score corresponding to each piece of feature information, a score vector of the target location includes:
calculating a weight value corresponding to each characteristic information according to the received user demand information;
and obtaining a scoring vector of the target place according to the weight value and the initial feature score corresponding to each feature information.
Optionally, the calculating, according to the received user demand information, a weight value corresponding to each of the feature information includes:
processing the received user demand information according to the following formula to determine the weight value f corresponding to each characteristic informationDk，
Figure BDA0001728366590000021
Wherein, akAnd the value of the score of the kth characteristic information in the user requirement information is represented, and M represents the total number of the characteristic information.
Optionally, the determining the score value of the target location according to the score vector of the target location and the score vector of the starting location includes:
calculating the vector distance between the target place and the starting place according to the score vector of the target place and the score vector of the starting place;
processing the vector distance by using the following correlation formula to determine the correlation I between the target place and the starting placeD(ik,jk)，
Figure BDA0001728366590000022
Wherein d isD(ik, jk) represents the vector distance of the kth feature component of the target site i and the kth feature component of the starting site j, dmaxThe maximum distance of the vectors of the target place i and the starting place j is represented, and alpha represents a parameter for adjusting the weight of the relation;
processing the relevance by using a score updating formula to determine a score value Tr (D) of the target place iik)，
Figure BDA0001728366590000031
Wherein, Tr (D)jk) Represents the value of the credit of the starting place j.
The embodiment of the invention also provides a site scoring device, which comprises an acquisition unit, a classification unit, a first determination unit and a second determination unit;
the acquisition unit is used for acquiring data information of a target place; the data information comprises data information collected by the unmanned equipment and historical data information collected from a network;
the classification unit is used for classifying the data information by using a data classification model to obtain classified characteristic information;
the first determining unit is used for determining a scoring vector of the target place according to the received user demand information and the initial feature score corresponding to each feature information;
and the second determining unit is used for determining the score value of the target place according to the score vector of the target place and the score vector of the starting place.
Optionally, the first determining unit includes a calculating subunit and an obtaining subunit;
the calculating subunit is configured to calculate, according to the received user demand information, a weight value corresponding to each of the feature information;
and the obtaining subunit is configured to obtain a scoring vector of the target location according to the weight value and the initial feature score corresponding to each of the feature information.
Optionally, the computing subunit is specifically configured to process the received user demand information according to the following formula to determine a weight value f corresponding to each feature informationDk，
Figure BDA0001728366590000032
Wherein, akAnd the value of the score of the kth characteristic information in the user requirement information is represented, and M represents the total number of the characteristic information.
Optionally, the second determining unit includes a distance calculating subunit, a correlation calculating subunit, and a score calculating subunit;
the distance calculating subunit is configured to calculate a vector distance between the target location and the starting location according to the score vector of the target location and the score vector of the starting location;
the correlation operator unit is used for calculating the vector distance by using the following correlation formulaProcessing to determine the correlation I between the target location and the starting locationD(ik,jk)，
Figure BDA0001728366590000041
Wherein d isD(ik, jk) represents the vector distance of the kth feature component of the target site i and the kth feature component of the starting site j, dmaxThe maximum distance of the vectors of the target place i and the starting place j is represented, and alpha represents a parameter for adjusting the weight of the relation;
the score value operator unit is used for processing the correlation by using the score updating formula to determine the score value Tr (D) of the target place iik)，
Figure BDA0001728366590000042
Wherein, Tr (D)jk) Represents the value of the credit of the starting place j.
The embodiment of the invention also provides a site scoring device, which comprises:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the venue scoring method as described above.
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when being executed by a processor, the computer program realizes the steps of the above site scoring method.
According to the technical scheme, the data information of the target place is acquired; the data information can comprise data information collected by the unmanned equipment and historical data information collected from a network; classifying the data information by using a data classification model to obtain classified characteristic information; according to the received user demand information and the initial feature scores corresponding to the feature information, a scoring vector of the target place can be determined; and finally determining the score value of the target place according to the score vector of the target place and the score vector of the starting place. The score value of the target place is determined under the condition that the user requirements and the influence degree of the starting place on the target place are fully considered, and the higher the score value is, the higher the probability that the target place meets the actual requirements of the user is. When the place is recommended according to the score value, the recommended place can better meet the requirements of the user.
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In order to illustrate the embodiments of the present invention more clearly, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained by those skilled in the art without inventive effort.
Fig. 1 is a flowchart of a site scoring method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a site scoring device according to an embodiment of the present invention;
fig. 3 is a schematic hardware structure diagram of a location scoring device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention without any creative work belong to the protection scope of the present invention.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Next, a site scoring method according to an embodiment of the present invention will be described in detail. Fig. 1 is a flowchart of a venue scoring method according to an embodiment of the present invention, where the method includes:
s101: and acquiring data information of the target place.
In the embodiment of the present invention, a place where the user is currently located may be referred to as an origin place, and a next place where the user wants to reach may be referred to as a target place.
The target location may be an area of a mall, tourist attraction, park, hotel, restaurant, etc. The target places can be determined according to the requirements of users, and the number of the target places can be one or more.
In the embodiment of the invention, the target place with the highest score is recommended to the user or the score value of the target place is displayed to the user in a mode of scoring and pre-estimating the target place, so that the user can select the target place meeting the requirement according to the score value.
The estimation process of the score value of each target place is similar, and then, taking a target place as an example, the estimation process of the score value of the target place is introduced.
When the score value of the target place is estimated, firstly, data information of the target place needs to be acquired. The data information may include data information collected by the unmanned device and historical data information collected from the network.
The data information collected by the unmanned equipment can include information such as the people flow information of a target place, the current traffic running condition and the like.
The historical data information collected from the network may include geographic location information, demographic historical information, facility status information, size information, etc. of the target site.
In practical applications, there may be an intersection of data between the data information collected by the unmanned device and the historical data information collected from the network, that is, the two data information include the same type of data information.
For example, for some places of tourist attraction types, the people flow information of the place can be acquired through data information acquired by the unmanned device. Correspondingly, the network ticket selling condition of the place can be collected from the network, and the pedestrian volume of the place is preliminarily evaluated according to the network ticket selling condition to obtain the pedestrian volume information of the place.
In the embodiment of the invention, the data information collected by the unmanned equipment and the historical data information collected from the network are collected to be used as the data information of the target place, so that the data information is more comprehensive and accurate. Therefore, when the score value is estimated according to the data information, the score value can be more fit with the actual situation of the target place.
S102: and classifying the data information by using a data classification model to obtain classified characteristic information.
When the data information is classified, the data information may be classified according to the data type to which the data information belongs. And performing comprehensive analysis on the data information of the same type so as to determine corresponding characteristic information.
The same type of data information corresponds to one feature information. According to different types of the data information, a plurality of characteristic information can be obtained.
S103: and determining a scoring vector of the target place according to the received user demand information and the initial feature score corresponding to each feature information.
For example, in S102, there are many feature information corresponding to one target location.
In practical application, a user can set different scoring values for different characteristic information according to own requirements. For example, when the user is seriously aware of the traffic condition of the target location, the score value corresponding to the traffic condition may be set higher; when the user pays less attention to the human history information of the target place, the score value corresponding to the human history information may be set lower.
The user can set corresponding fractional values for all the characteristic information of the target place, or only set corresponding fractional values for part of the characteristic information, or set a default fractional value for other characteristic information without the fractional value, wherein the default fractional value may be zero or a value with a small value.
The score value set by the user on the feature information reflects the influence degree of the feature information on the target place, and the higher the score value is, the larger the influence degree is. According to the score value set by the user, the weight occupied by each feature information can be determined.
In a specific implementation, the received user requirement information may be processed according to the following formula to determine the weight value f corresponding to each feature informationDk，
Figure BDA0001728366590000071
Wherein, akAnd the value of the score of the kth characteristic information in the user requirement information is represented, and M represents the total number of the characteristic information.
The weight value corresponding to each feature information is a specific gravity value determined under the condition that the actual requirements of the user are considered. Besides the weight value, each feature information has an initial feature score corresponding to the feature information.
Wherein the initial characteristic score is a score value determined when the characteristic information is rate-rated.
In practical application, a grade table may be set for each piece of feature information, the grade table stores the division ranges of different grades and corresponding score values, and the initial feature value of the feature information may be determined according to the division range in which the feature information is located.
For example, the geographic location information may be divided into 3 grades of downtown, suburban and remote suburban areas, and the corresponding score values are 3, 2 and 1 in sequence; the human historical information is divided into a first level, a second level and a third level according to the national culture protection level, and the corresponding score values are divided into 3, 2 and 1 in sequence.
After determining the weight value and the initial feature score corresponding to each feature information in the target place, the scoring vector of the target place can be determined according to the weight value and the initial feature score value.
Specifically, the weight value corresponding to each feature vector may be multiplied by the initial feature score value to obtain the score value of the feature vector, and the set of the score values of all feature vectors of the target location is the score vector a of the target locationi＝[fi1·gi1,…,fik·gik,…,fiM·giM]。
Wherein f isikWeight value g corresponding to kth feature information indicating target locationikAnd an initial feature point value corresponding to the kth feature information indicating the target location, wherein k is 1, M and M indicate the total number of the feature information of the target location.
S104: and determining the score value of the target place according to the score vector of the target place and the score vector of the starting place.
The starting location is the current location of the user, and the corresponding score vector can be determined by referring to the steps of S101-S103.
In the embodiment of the invention, on the basis of evaluating each characteristic information in the target place, the influence condition of the starting place on the target place is also fully considered, so that the estimated score value of the target place is more suitable for the actual requirement of the user.
In a specific implementation, the vector distance d between the target location and the starting location can be calculated according to the score vector of the target location and the score vector of the starting locationD(ik,jk)＝||Aik-Ajk||；
Wherein A isikScore value, A, of kth feature information in score vector representing target site ijkAnd the scoring value of the kth characteristic information in the scoring vector of the starting place j is represented.
The vector distance is processed by using the following correlation formula, and the correlation I between the target place and the starting place is determinedD(ik,jk)，
Figure BDA0001728366590000091
Wherein d isD(ik, jk) represents the vector distance of the kth feature component of the target site i and the kth feature component of the starting site j, dmaxThe maximum distance of the vectors of the target location i and the starting location j is shown, and alpha is a parameter for adjusting the weight of the relationship. The specific value of α may be set according to actual requirements, and is not limited herein.
After the correlation between the target location and the starting location is determined, the correlation may be processed using the score update formula to determine a score value Tr (D) for the target location iik)，
Figure BDA0001728366590000092
Wherein, Tr (D)jk) The score value, Tr (D), of the starting location jjk) Can be according to Aj＝[fj1·gj1,…,fjk·gjk,…,fjM·gjM]And (5) determining.
The higher the score value of the target location, the higher the probability that the target location meets the user's requirements.
When the place recommendation is carried out, when a plurality of target places exist, the target place with the highest scoring value can be recommended to the user; or the target places are sequentially displayed to the user according to the grade values from high to low for the user to select.
According to the technical scheme, the data information of the target place is acquired; the data information can comprise data information collected by the unmanned equipment and historical data information collected from a network; classifying the data information by using a data classification model to obtain classified characteristic information; according to the received user demand information and the initial feature scores corresponding to the feature information, a scoring vector of the target place can be determined; and finally determining the score value of the target place according to the score vector of the target place and the score vector of the starting place. The score value of the target place is determined under the condition that the user requirements and the influence degree of the starting place on the target place are fully considered, and the higher the score value is, the higher the probability that the target place meets the actual requirements of the user is. When the place is recommended according to the score value, the recommended place can better meet the requirements of the user.
Fig. 2 is a schematic structural diagram of a location scoring apparatus according to an embodiment of the present invention, where the apparatus includes an obtaining unit 21, a classifying unit 22, a first determining unit 23, and a second determining unit 24;
an acquisition unit 21 configured to acquire data information of a target location; the data information comprises data information collected by the unmanned equipment and historical data information collected from a network;
the classification unit 22 is configured to perform classification processing on the data information by using a data classification model to obtain classified feature information;
the first determining unit 23 is configured to determine a score vector of the target location according to the received user demand information and the initial feature scores corresponding to the feature information;
and the second determining unit 24 is configured to determine the score value of the target location according to the score vector of the target location and the score vector of the starting location.
Optionally, the first determining unit includes a calculating subunit and an obtaining subunit;
the calculating subunit is used for calculating the weight value corresponding to each characteristic information according to the received user demand information;
and the obtaining subunit is used for obtaining a scoring vector of the target place according to the weight value and the initial feature score corresponding to each feature information.
Optionally, the calculating subunit is specifically configured to process the received user requirement information according to the following formula to determine the weight value f corresponding to each feature informationDk，
Figure BDA0001728366590000101
Wherein the content of the first and second substances,akand the value of the score of the kth characteristic information in the user requirement information is represented, and M represents the total number of the characteristic information.
Optionally, the second determining unit includes a distance calculating subunit, a correlation calculating subunit, and a score calculating subunit;
the distance calculation subunit is used for calculating the vector distance between the target place and the starting place according to the score vector of the target place and the score vector of the starting place;
a correlation operator unit for processing the vector distance by using the following correlation formula to determine the correlation I between the target location and the starting locationD(ik,jk)，
Figure BDA0001728366590000111
Wherein d isD(ik, jk) represents the vector distance of the kth feature component of the target site i and the kth feature component of the starting site j, dmaxThe maximum distance of the vectors of the target place i and the starting place j is represented, and alpha represents a parameter for adjusting the weight of the relation;
a score value operator unit for processing the correlation by using the score updating formula to determine the score value Tr (D) of the target place iik)，
Figure BDA0001728366590000112
Wherein, Tr (D)jk) Indicating the value of the credit of the starting place j.
The description of the features in the embodiment corresponding to fig. 2 may refer to the related description of the embodiment corresponding to fig. 1, and is not repeated here.
According to the technical scheme, the data information of the target place is acquired; the data information can comprise data information collected by the unmanned equipment and historical data information collected from a network; classifying the data information by using a data classification model to obtain classified characteristic information; according to the received user demand information and the initial feature scores corresponding to the feature information, a scoring vector of the target place can be determined; and finally determining the score value of the target place according to the score vector of the target place and the score vector of the starting place. The score value of the target place is determined under the condition that the user requirements and the influence degree of the starting place on the target place are fully considered, and the higher the score value is, the higher the probability that the target place meets the actual requirements of the user is. When the place is recommended according to the score value, the recommended place can better meet the requirements of the user.
Fig. 3 is a schematic hardware configuration diagram of a location scoring device 30 according to an embodiment of the present invention, where the location scoring device 30 includes:
a memory 31 for storing a computer program;
a processor 32 for executing a computer program to implement the steps of the venue scoring method as described above.
The embodiment of the invention also provides a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when being executed by a processor, the computer program realizes the steps of the above site scoring method.
The method, the device and the computer-readable storage medium for rating a place provided by the embodiment of the invention are described in detail above. The embodiments are described in a progressive manner in the specification, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. 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 present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

Claims (4)

1. A venue scoring method, comprising:
acquiring data information of a target place; the data information comprises data information collected by the unmanned equipment and historical data information collected from a network;
classifying the data information by using a data classification model to obtain classified characteristic information;
calculating a weight value corresponding to each characteristic information according to the received user demand information; obtaining a scoring vector of the target place according to the weight value and the initial feature value corresponding to each feature information;
the calculating the weight value corresponding to each feature information according to the received user demand information includes:
processing the received user demand information according to the following formula to determine the weight value f corresponding to each characteristic informationDk，
Figure FDA0003200614260000011
Wherein, akThe value of the score of the kth characteristic information in the user demand information is represented, and M represents the total number of the characteristic information;
determining the score value of the target place according to the score vector of the target place and the score vector of the starting place; the determining the score value of the target place according to the score vector of the target place and the score vector of the starting place comprises:
calculating the vector distance between the target place and the starting place according to the score vector of the target place and the score vector of the starting place;
processing the vector distance by using the following correlation formula to determine the correlation I between the target place and the starting placeD(ik,jk)，
Figure FDA0003200614260000012
Wherein d isD(ik, jk) represents the vector distance of the kth feature component of the target site i and the kth feature component of the starting site j, dmaxThe maximum distance of the vectors of the target place i and the starting place j is represented, and alpha represents a parameter for adjusting the weight of the relation;
processing the relevance by using a score updating formula to determine a score value Tr (D) of the target place iik)，
Figure FDA0003200614260000021
Wherein, Tr (D)jk) Represents the value of the credit of the starting place j.
2. A site scoring device is characterized by comprising an acquisition unit, a classification unit, a first determination unit and a second determination unit;
the acquisition unit is used for acquiring data information of a target place; the data information comprises data information collected by the unmanned equipment and historical data information collected from a network;
the classification unit is used for classifying the data information by using a data classification model to obtain classified characteristic information;
the first determining unit is used for determining a scoring vector of the target place according to the received user demand information and the initial feature score corresponding to each feature information;
the first determining unit comprises a calculating subunit and an obtaining subunit;
the calculating subunit is configured to calculate, according to the received user demand information, a weight value corresponding to each of the feature information; the calculation subunit is specifically configured to process the received user demand information according to the following formula to determine a weight value f corresponding to each feature informationDk，
Figure FDA0003200614260000022
Wherein, akThe value of the score of the kth characteristic information in the user demand information is represented, and M represents the total number of the characteristic information;
the obtaining subunit is configured to obtain, according to the weight value and an initial feature score corresponding to each of the feature information, a scoring vector of the target location;
the second determining unit is used for determining the score value of the target place according to the score vector of the target place and the score vector of the starting place; the second determination unit comprises a distance calculation subunit, a correlation calculation subunit and a score value calculation subunit;
the distance calculating subunit is configured to calculate a vector distance between the target location and the starting location according to the score vector of the target location and the score vector of the starting location;
the correlation degree operator unit is used for processing the vector distance by using the following correlation degree formula to determine the correlation degree I between the target place and the starting placeD(ik,jk)，
Figure FDA0003200614260000031
Wherein d isD(ik, jk) represents the vector distance of the kth feature component of the target site i and the kth feature component of the starting site j, dmaxThe maximum distance of the vectors of the target place i and the starting place j is represented, and alpha represents a parameter for adjusting the weight of the relation;
the score value operator unit is used for processing the correlation by using the score updating formula to determine the score value Tr (D) of the target place iik)，
Figure FDA0003200614260000032
Wherein, Tr (D)jk) Represents the value of the credit of the starting place j.
3. A venue scoring device, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the venue scoring method of claim 1.
4. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of the venue scoring method of claim 1.
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