CN111768035A - Path recommendation information pushing method and device, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a method and a device for pushing path recommendation information, computer equipment and a storage medium, which relate to the technical field of navigation, and the method comprises the following steps: determining at least one movement path in a search area containing the m-subarea paths; acquiring the expected visit duration of each of the m sub-regions according to the region characteristics of each of the m sub-regions and the user characteristics corresponding to the terminal; acquiring the estimated occupation time of each of at least one moving path according to the estimated visit time of each of the m sub-areas; and pushing path recommendation information to the terminal according to the respective estimated occupation duration of at least one moving path. By the method, the estimated occupation time of the moving path can be calculated more accurately based on the user characteristics and the regional characteristics of the sub-region in the process of pushing the path recommendation information, so that the accuracy and flexibility of pushing the path recommendation information are improved.
Description
Technical Field
The embodiment of the application relates to the technical field of navigation, in particular to a method and a device for pushing path recommendation information, computer equipment and a storage medium.
Background
At present, with the increasing travel demand of people, people need to plan the journey before going out.
To meet the above requirement, in the related art, the platform plans the playing route in a specific area in advance, such as different playing routes provided by a travel agency to the user, and each playing route corresponds to a different playing area and playing duration for the user to select.
However, in the related art, the area and the occupied time length where the route planned in advance by the platform passes are all fixed, the user needs to adjust the time planning of the user according to the occupied time length required by the route, and the area where the route passes cannot be intelligently changed along with the change of the user's requirement, so that the flexibility of pushing the route recommendation information is low.
Disclosure of Invention
The embodiment of the application provides a method and a device for pushing path recommendation information, a computer device and a storage medium, which can improve the flexibility of pushing the path recommendation information, and the technical scheme is as follows:
in one aspect, a method for pushing path recommendation information is provided, where the method includes:
determining at least one moving path in a path searching area, wherein the path searching area comprises m sub-areas, each moving path passes through n sub-areas in the m sub-areas, m and n are positive integers, and m is more than or equal to n and is more than or equal to 1;
acquiring the expected visit duration of each of the m sub-areas according to the area characteristics of each of the m sub-areas and the user characteristics corresponding to the terminal; the estimated tour duration is estimated tour duration of a user corresponding to the terminal in a corresponding sub-area;
acquiring the estimated occupation time of each of the at least one moving path according to the estimated visit time of each of the m sub-areas;
and pushing path recommendation information to the terminal according to the respective estimated occupation duration of the at least one moving path.
In another aspect, a path recommendation information pushing device is provided, the device includes:
the determining module is used for determining at least one moving path in a path searching area, the path searching area comprises m sub-areas, each moving path passes through n sub-areas in the m sub-areas, m and n are positive integers, and m is more than or equal to n and is more than or equal to 1;
a first obtaining module, configured to obtain expected tour duration of each of the m sub-regions according to the region feature of each of the m sub-regions and a user feature corresponding to a terminal; the estimated tour duration is estimated tour duration of a user corresponding to the terminal in a corresponding sub-area;
a second obtaining module, configured to obtain respective estimated occupation durations of the at least one moving path according to respective estimated visit durations of the m sub-areas;
and the pushing module is used for pushing the path recommendation information to the terminal according to the respective estimated occupation duration of the at least one moving path.
In a possible implementation manner, the first obtaining module includes:
an extraction submodule for extracting a region feature of a target sub-region, the target sub-region being any one of the m sub-regions;
the first acquisition submodule is used for inputting the regional characteristics of the target sub-region and the user characteristics corresponding to the terminal into a tour duration estimation model to acquire the predicted tour duration of the target sub-region; the tour duration estimation model is obtained by training according to the regional characteristics of the sample sub-region, the user characteristics of the sample users and the historical tour duration of the sample users in the sample sub-region.
In one possible implementation, the tour duration estimation model is a bayesian network model;
the first obtaining sub-module includes:
the first obtaining subunit is configured to input the regional characteristics of the target sub-region and the user characteristics corresponding to the terminal into a tour duration estimation model, and obtain m sub-estimated tour durations output by the tour duration estimation model and probabilities corresponding to the m sub-estimated tour durations respectively;
and the first calculating subunit is configured to calculate the expected tour duration of the target sub-region according to the m sub-expected tour durations and probabilities corresponding to the m sub-expected tour durations, respectively.
In a possible implementation manner, the second obtaining module includes:
a second obtaining sub-module, configured to obtain the expected tour duration of each of the n sub-regions that the at least one moving path passes through, in the m sub-regions;
and the third obtaining sub-module is used for obtaining the estimated occupation time of each of the at least one moving path according to the estimated visit time of each of the n sub-regions through which the at least one moving path passes.
In one possible implementation, the pushing module includes:
the first determining submodule is used for determining each moving path of the at least one moving path, wherein the corresponding estimated occupation time is within a preset time range, as a recommendable path;
and the pushing submodule is used for pushing path recommendation information to the terminal according to each recommendable path.
In one possible implementation, the pushing submodule includes:
a second obtaining subunit, configured to obtain a predicted feedback result of each recommendable path according to a predicted tour duration of each recommendable path and a user characteristic corresponding to the terminal; the predicted feedback result is a predicted satisfaction feedback result of the user corresponding to the terminal on the recommendable path;
the sequencing subunit is used for sequencing each recommendable path according to the respective predicted feedback result of each recommendable path;
and the pushing subunit is used for pushing the recommendation information of the recommended path to the terminal according to the sequencing result of each recommendable path.
In a possible implementation manner, the second obtaining subunit is configured to input the estimated tour duration of the target recommendable path and the user characteristic corresponding to the terminal into a feedback result estimation model, and obtain an estimated feedback result of the target recommendable path; the target recommendable path is any one of the recommendable paths; the feedback result estimation model is obtained by training according to the estimated tour duration of the sample path, the user characteristics of the sample user and the satisfaction feedback result of the sample user to the sample path.
In one possible implementation, the feedback result prediction model is a bayesian network model;
the second obtaining subunit is configured to input the predicted tour duration of the target recommendable path and the user characteristic corresponding to the terminal into a feedback result prediction model, and obtain m sub-predicted feedback results output by the feedback result prediction model and probabilities corresponding to the m sub-predicted feedback results respectively;
and calculating the predicted feedback result of the target recommendable path according to the m sub-predicted feedback results and the probabilities corresponding to the m sub-predicted feedback results respectively.
In one possible implementation, before determining at least one moving path in the path search area, the apparatus further includes:
a third obtaining module, configured to obtain a starting point and an ending point of a user corresponding to the terminal;
and the building module is used for building the path searching area according to the starting point and the end point.
In another aspect, a computer device is provided, where the computer device includes a processor and a memory, where the memory stores at least one instruction, at least one program, a code set, or a set of instructions, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by the processor to implement the path recommendation information pushing method provided in the above various optional implementations.
In another aspect, a computer-readable storage medium is provided, where at least one instruction, at least one program, a code set, or a set of instructions is stored, and the at least one instruction, the at least one program, the code set, or the set of instructions is loaded and executed by a processor to implement the path recommendation information pushing method provided in the above-mentioned various optional implementations.
In another aspect, a computer program product or computer program is provided, the computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, so that the computer device executes the path recommendation information pushing method provided in the various optional implementation manners.
The technical scheme provided by the application can comprise the following beneficial effects:
the path recommendation information is pushed to the terminal according to the estimated occupation duration corresponding to each moving path, and the estimated occupation duration of the moving path can be calculated more accurately in the pushing process of the path recommendation information based on the user characteristics and the regional characteristics of the sub-regions, so that the accuracy and the flexibility of pushing the path recommendation information are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 illustrates a schematic diagram of a Bayesian network illustrated in an exemplary embodiment of the present application;
FIG. 2 illustrates a relationship diagram between nodes shown in an exemplary embodiment of the present application;
FIG. 3 illustrates a block diagram of a route planning system shown in an exemplary embodiment of the present application;
fig. 4 shows a flowchart of a method for pushing path recommendation information according to an exemplary embodiment of the present application;
FIG. 5 illustrates a schematic diagram of a path search area range shown in an exemplary embodiment of the present application;
fig. 6 shows a flowchart of a method for pushing path recommendation information according to an exemplary embodiment of the present application;
FIG. 7 illustrates a schematic diagram of the operation of the tour duration estimation model according to an exemplary embodiment of the present application;
FIG. 8 illustrates an operational diagram of a feedback result prediction model according to an exemplary embodiment of the present application;
fig. 9 is a block diagram illustrating a path recommendation information pushing apparatus according to an exemplary embodiment of the present application;
FIG. 10 is a block diagram illustrating the structure of a computer device in accordance with an exemplary embodiment;
FIG. 11 is a block diagram illustrating the structure of a computer device according to an example embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
It is to be understood that reference herein to "a number" means one or more and "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
First, the terms referred to in the embodiments of the present application will be briefly described:
bayesian networks (Bayesian Network)
A bayesian Network, also called Belief Network (Belief Network), or directed acyclic graph Model (directed cyclic graph Model), is a typical probabilistic graph Model. The method is an uncertainty processing model for simulating causal relationship in a thermal internal reasoning process, the network topology structure is a directed acyclic graph, and the overall joint distribution of the network is calculated as the product of condition distribution defined by each node in a framework. Referring to fig. 1, which shows a schematic diagram of a bayesian network according to an exemplary embodiment of the present application, as shown in fig. 1, there are several nodes in a directed acyclic graph of the bayesian network, and the nodes in the directed acyclic graph represent random variables { X }1，X2，…，XnWhich are observable variables, or hidden variables, location parameters, etc. Variables or propositions that are considered causal (or unconditional independent) are connected by arrows. If two nodes are connected together by a single arrow, which indicates that one of the nodes is "reasons" and the other is "fruit", the two nodes will generate a conditional probability value. For example, referring to fig. 2, which shows a schematic diagram of a relationship between nodes shown in an exemplary embodiment of the present application, as shown in fig. 2, assuming that a node E directly affects a node H, i.e., E → H, a directional arc (E, H) from the node E to the node H is established by an arrow pointing from E to H, and a weight (i.e., a connection strength) is represented by a conditional probability P (H | E). That is, the random variables involved in a research system are independently mapped in a directed way based on whether conditions existIn the figure, a bayesian network is formed, which is mainly used to describe the conditional dependence between random variables, with circles representing random variables (randommediables) and with arrows representing conditional dependencies (conditional dependencies). Furthermore, for any random variable, its joint probability can be obtained by multiplying the respective local conditional probability distributions:
P(x1，...，xk)＝P(xk|x1，...，xk-1)...P(x2|x1)P(x1)
referring to fig. 3, a block diagram of a route planning system according to an exemplary embodiment of the present application is shown, and as shown in fig. 3, the system includes a terminal 310 and a server 320.
The terminal 310 may be a mobile phone, a tablet computer, an e-book reader, smart glasses, a smart watch, an MP3 player (Moving Picture Experts Group Audio Layer III, motion Picture Experts compression standard Audio Layer 3), an MP4 player (Moving Picture Experts Group Audio Layer IV, motion Picture Experts compression standard Audio Layer 4), or the like.
The terminals 310 may communicate with each other through a short-range wireless communication technology, for example, the terminals 310 may communicate with each other through at least one of a Wi-Fi technology, a bluetooth technology, and an NFC technology.
The terminal 310 may have an application program supporting route planning installed therein, and accordingly, the server 320 may be a server corresponding to the application program supporting route planning.
The terminal 310 and the server 320 are connected through a communication network. Optionally, the communication network is a wired network or a wireless network.
The server 320 is a server, or a plurality of servers, or a virtualization platform, or a cloud computing service center.
Optionally, the wireless network or wired network described above uses standard communication techniques and/or protocols. The Network is typically the Internet, but may be any Network including, but not limited to, a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a mobile, wireline or wireless Network, a private Network, or any combination of virtual private networks. In some embodiments, data exchanged over a network is represented using techniques and/or formats including Hypertext Mark-up Language (HTML), Extensible markup Language (XML), and the like. All or some of the links may also be encrypted using conventional encryption techniques such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Network (VPN), Internet protocol Security (IPsec). In other embodiments, custom and/or dedicated data communication techniques may also be used in place of, or in addition to, the data communication techniques described above.
Referring to fig. 4, a flowchart of a method for pushing path recommendation information according to an exemplary embodiment of the present application is shown. The path recommendation information pushing method may be executed by a computer device, where the computer device may be a server or a terminal, where the terminal may be the terminal shown in fig. 3, and the server may be the server shown in fig. 3. As shown in fig. 4, the method for pushing the path recommendation information includes the following steps:
In one possible implementation, the route search area range is a two-dimensional plane range, for example, the route search area range is a certain floor of a building, a scenic spot plane layout, and the like; alternatively, the route search area range is a three-dimensional stereo range, such as each floor of a business building.
In the embodiment of the present application, a path recommendation information pushing method provided by the present application is described by taking a path search area range as a two-dimensional plane range as an example.
In one possible implementation manner, m sub-regions are included in the path search region, and at least one moving path is determined in the path search region based on the difference of passing sub-regions during the moving process, for example, please refer to fig. 5, which shows a schematic diagram of the path search region shown in an exemplary embodiment of the present application, as shown in fig. 5, in the path search region 510, the moving route of the user may be S → a → E → F → G → O, or may also be S → a → B → C → D → O, or may also be S → a → E → C → G → O, and so on. Different movement paths can be formed by arranging and combining different sub-areas.
In a possible implementation manner, the user characteristics corresponding to the terminal include basic information of the user and additional requirement information of the user, where the basic information of the user includes basic user portrait information such as age and gender of the user, and the additional requirement information of the user includes information such as user disposable time, number of people in the same row, and form of the same row.
In a possible implementation manner, the basic information of the user is extracted from a user information base by the computer device, or is input into the computer device when the user has a path recommendation information pushing requirement; the additional requirement information of the user is actively input into the computer equipment by the user, or is obtained by the computer equipment according to the historical behavior habit prediction of the user, or is default additional requirement information preset by the computer equipment.
In a possible implementation manner, the number of the user features corresponding to the terminal is at least two, and different numbers and types of the user features may have different influences on the expected tour duration in the same sub-area. For example, for the estimated visit duration of a dining area, the time for one person to have a meal is shorter than the estimated visit duration for four persons to have a meal; alternatively, the predicted tour length for the game area may be longer for users with children in the peer than for users without children in the peer, and so on.
In one possible implementation, different sub-areas have different area characteristics, such as floors, floor space, related facilities, area attributes, and the like, wherein the area attributes may include different functions of education, dining, shopping, entertainment, and the like, and the different area characteristics may affect the duration of a user's visit within each sub-area. For example, for the region with the catering function, the visiting time of the user can be 1-2 hours, and the visiting time of the user in the region with the shopping function can be 10-30 minutes; for another example, different footprints may have unequal effects on the user's tour duration, generally speaking, the user's tour duration is shorter in a sub-area with a smaller footprint, and the user's tour duration is longer in a sub-area with a larger footprint.
And 430, acquiring the estimated occupation time of each of the at least one moving path according to the estimated visit time of each of the m sub-areas.
In a possible implementation manner, the path recommendation information pushed to the terminal by the computer device according to the respective estimated occupation duration of the at least one moving path includes: the information of each sub-area passed by the pushed moving path, the expected visit duration of the user in each sub-area, the introduction of the activity items of each sub-area, the historical user evaluation in each sub-area and the like.
To sum up, according to the path recommendation information pushing method provided by the embodiment of the application, at least one moving path including at least one sub-region is determined in the path search region, the tour duration of a user in each sub-region is estimated according to the region characteristics of each sub-region and the user characteristics corresponding to the terminal, the estimated occupation duration corresponding to each moving path is further determined, and the path recommendation information is pushed to the terminal according to the estimated occupation duration corresponding to each moving path.
Referring to fig. 6, a flowchart of a method for pushing path recommendation information according to an exemplary embodiment of the present application is shown. The path recommendation information pushing method may be executed by a computer device, where the computer device may be a server or a terminal, where the terminal may be the terminal shown in fig. 3, and the server may be the server shown in fig. 3. As shown in fig. 6, the method for pushing the path recommendation information may include the following steps:
In a possible implementation manner, the path search area is constructed based on the determined start point and end point of the user, so that the path search area needs to be constructed before at least one moving path is determined in the path search area, and the process may include:
acquiring a starting point and an end point of a user corresponding to a terminal;
and constructing a path search area according to the starting point and the end point.
In a possible implementation manner, the starting point of the user corresponding to the terminal is a starting point which is input in the computer device by the user when the user has a requirement for pushing the path recommendation information, or is a position where the user corresponding to the terminal is currently located and obtained by the computer device through positioning; and the terminal point of the user corresponding to the terminal is the terminal point expected by the user and input in the computer device when the user has the requirement of pushing the path recommendation information, or the terminal point is the position determined by the computer device according to the historical movement information of the user.
After the starting point and the end point of the user are determined, a path search area is determined according to the starting point and the end point of the user, in a possible implementation manner, the path search area is constructed by setting a search step length and combining the starting point and the end point, wherein the search step length is used for indicating the distance between the edge of the search area and a connecting line between the starting point and the end point. As shown in fig. 5, a connection line between the starting point S and the end point O is taken as a center line, and an area formed by extending a preset search step length to both sides of the center line is taken as a path search area, where the length of the connection line between the starting point S and the end point O is long, 2x is wide, x is the preset search step length, and the connection line between the starting point S and the end point O is taken as the center line.
In one possible implementation, the path search area is a circular search area formed by taking a connecting line between the starting point and the end point as a diameter and taking a midpoint of the connecting line between the starting point and the end point as a center of a circle.
It should be noted that the above method for constructing the route search area by the start point and the end point of the user is only exemplary, and the method for constructing the route search area range is not limited in the present application.
And step 620, extracting the region characteristics of the target sub-region, wherein the target sub-region is any one of the m sub-regions.
In one possible implementation, the same sub-region corresponds to different regional features, for example, for a restaurant in second floor of a certain shopping mall, the "certain shopping mall", "second floor", "restaurant" and "Chinese food" belong to different regional features of the restaurant.
Generally speaking, the regional characteristics of the same sub-region are not changed, but the user characteristics can be changed according to the different user requirements, for example, when the user goes out for the first time, the user has a dinner together with a company, when the user goes out for the second time, the user has a lovers appointment, and in the two occasions, the number of people who have the same row, the form of the same row, and the like are different.
In a possible implementation manner, the tour duration estimation model is obtained by training according to the area characteristics of the sample sub-area, the user characteristics of the sample user and the historical tour duration of the sample user in the sample sub-area.
Referring to fig. 7, which illustrates a working diagram of a tour duration prediction model according to an exemplary embodiment of the present application, as shown in fig. 7, when a predicted tour duration of a sub-region needs to be calculated, a region feature 710 corresponding to the sub-region and a user feature 720 of a terminal in the current situation need to be input into a tour duration prediction model 730 to obtain the predicted tour duration of the sub-region.
In one possible implementation, the tour duration estimation model is a bayesian network model.
When the preview duration estimation model is a bayesian network model, the output result of the preview duration estimation model is composed of a plurality of possible tour durations and probabilities, that is to say:
inputting the regional characteristics of the target sub-region and the user characteristics corresponding to the terminal into the tour duration estimation model, and obtaining m sub-estimated tour durations output by the tour duration estimation model and probabilities corresponding to the m sub-estimated tour durations respectively.
For example, for the sub-region a, the output result obtained by the preview duration prediction model prediction is as follows:
| estimated duration of visit | Probability of |
| 0 minute | 0 |
| 0 to 20 minutes | 40％ |
| 20 to 40 minutes | 50％ |
| 40 to 60 minutes | 10％ |
| … | 0 |
That is, under the current user characteristics of the terminal, the expected visit duration of the user in the sub-area a is 40% possible, and the visit duration of the user in the sub-area a is 0-20 minutes; the possibility is 50%, and the visiting time of the user in the subarea A is 20-40 minutes; there is a 10% chance that the user will have a visit in sub-area a for 40-60 minutes.
And calculating the expected visit duration of the target sub-area according to the m sub-expected visit durations and the probabilities corresponding to the m sub-expected visit durations respectively.
In a possible implementation manner, the highest probability of obtaining the sub-estimated tour duration is the estimated tour duration of the target sub-region, for example, the estimated tour duration of the sub-region a is obtained within 20-40 minutes with a probability of 50%.
Or, in another possible implementation manner, the m sub-estimated tour duration are weighted and averaged respectively, the weighting is taken as the probability corresponding to each of the m sub-estimated tour duration, the results of the weighted and averaged sub-estimated tour duration are accumulated, and the accumulated result is obtained as the estimated tour duration of the target sub-region. For example, for the sub-area a, the expected tour duration T is:
T＝[0，20]×40％+[20，40]×50％+[40，60]×10％＝[14，34]
that is, the expected visit duration for subregion A is 14-34 minutes.
And step 640, obtaining the estimated occupation time of each of the at least one moving path according to the estimated visit time of each of the m sub-areas.
In one possible implementation manner, the steps are implemented as follows:
and acquiring the expected visit duration of each of the n sub-regions passed by at least one moving path in the m sub-regions.
And acquiring the estimated occupation time of each of the at least one moving path according to the estimated visit time of each of the n sub-regions passed by the at least one moving path.
On the basis of obtaining the expected visit duration corresponding to each of m sub-regions in the path search region, calculating the estimated occupation duration of the moving path passing through a part of sub-regions in the m sub-regions, namely summing the expected visit durations of the sub-regions passed by the moving path. For example, the movement path a passes through the sub-area a, the sub-area C, and the sub-area D, and then the estimated occupation duration of the movement path a is equal to the sum of the estimated visit duration a, the estimated visit duration B, and the estimated visit duration C corresponding to the sub-area a, the sub-area C, and the sub-area D, respectively.
In one possible implementation, the preset time range is determined based on a desired duration input by a user, and the fault-tolerant duration is shortened based on the desired duration input by the user to be lengthened based on the desired duration input by the user. For example, the expected time period input by the user is 4 hours, the fault-tolerant time period is 30 minutes, and the preset time range is 3 hours and 30 minutes to 4 hours and 30 minutes. The movement route with the estimated occupying time within the preset time range is the recommendable path, and at least one recommendable path exists in at least one movement path.
And 660, pushing path recommendation information to the terminal according to the recommendable paths.
In one possible implementation manner, the above process may be implemented as:
step 661, obtaining respective estimated feedback results of each recommendable path according to respective estimated tour duration of each recommendable path and user characteristics corresponding to the terminal; the expected feedback result is a feedback result of the satisfaction degree of the user corresponding to the predicted terminal to the recommendable path.
In one possible case, the process of obtaining the expected feedback result may be implemented as:
inputting the estimated tour duration of the target recommendable path and the user characteristics corresponding to the terminal into a feedback result estimation model to obtain an estimated feedback result of the target recommendable path; the target recommendable path is any one of recommendable paths; the feedback result estimation model is obtained by training according to the estimated tour duration of the sample path, the user characteristics of the sample user and the satisfaction feedback result of the sample user to the sample path.
The satisfaction feedback result reflects the reasonability of the sample path and the satisfaction degree of the user on the sample path.
Referring to fig. 8, which illustrates a working diagram of a feedback result prediction model according to an exemplary embodiment of the present application, as shown in fig. 8, when a predicted feedback result of a recommendable path needs to be predicted, a predicted occupied time 810 corresponding to the recommendable path and a user characteristic 820 of a terminal in a current situation need to be input into a feedback result prediction model 830, so as to obtain the predicted feedback result of the recommendable path.
In one possible implementation, the feedback result prediction model is a bayesian network model.
When the feedback result prediction model is a bayesian network model, the output result of the feedback result prediction model is composed of a plurality of possible feedback results and probabilities, that is:
and inputting the expected tour duration of the target recommendable path and the user characteristics corresponding to the terminal into the feedback result estimation model, and obtaining m sub-expected feedback results output by the feedback result estimation model and probabilities corresponding to the m sub-expected feedback results respectively.
In one possible case, the predicted feedback result of the target recommendable path is represented by a user score, for example, for recommendable path a, the output result predicted by the feedback result prediction model is:
that is, under the current user characteristics of the terminal, the expected feedback result of the user on the recommendable path a is: the probability is 2%, and the feedback result of the user to the recommendable path A is 0 point; the probability is 10%, and the feedback result of the user to the recommendable path A is 1 point; the probability is 30%, and the feedback result of the user to the recommendable path A is 2 points; the probability is 40%, and the feedback result of the user to the recommendable path A is 3 points; the probability is 10%, and the feedback result of the user to the recommendable path A is 4 points; there is 8% probability that the user's feedback result on the recommendable path a is 5 points.
And calculating the predicted feedback result of the target recommendable path according to the m sub-predicted feedback results and the probabilities corresponding to the m sub-predicted feedback results respectively.
In a possible implementation manner, the highest probability of obtaining the sub-predicted feedback results is the predicted feedback result of the target recommendable path, for example, the predicted feedback result of obtaining the recommendable path a is 3 minutes with a probability of 40%.
Or, in another possible implementation manner, the m sub-predicted feedback results are respectively subjected to weighted averaging, the probabilities with the weights corresponding to the m sub-predicted feedback results are taken, the results of the weighted averaging of the m sub-predicted feedback results are accumulated, and the accumulated result is obtained as the predicted feedback result of the target recommendable path. For example, for the recommendable path a, the expected feedback result Z is:
Z＝0×2％+1×10％+2×30％+3×40％+4×10％+5×8％＝2.7
that is, the predicted feedback result of the recommendable path a is 2.7 points.
Step 662, sorting the recommendable paths according to their respective predicted feedback results.
In one possible implementation, the recommendable paths are ranked in order of their expected feedback results from high to low.
And 663, pushing recommendation information of the recommended path to the terminal according to the sequencing result of each recommendable path.
In a possible situation, after sorting of all recommendable paths is completed, selecting n recommendable paths before sorting as recommending paths, and pushing recommendation information of the recommending paths to a terminal, wherein n is a positive integer.
In one possible case, the value of n is preset by the computer device, or the value of n is set by the user.
In a possible situation, after sorting of the recommendable paths is completed, the recommendable path with the estimated feedback result larger than the feedback result threshold is selected as the recommended path, and recommendation information of the recommended path is pushed to the terminal.
In one possible case, the feedback result threshold is preset by the computer device, or the feedback result threshold is set by the user.
Sequencing the recommendable paths according to the respective predicted feedback results of the at least one recommendable path;
and determining n recommendable paths before sequencing as recommended paths, and pushing recommendation information of the recommended paths to the terminal, wherein n is a positive integer.
To sum up, according to the path recommendation information pushing method provided by the embodiment of the application, at least one moving path including at least one sub-region is determined in the path search region, the tour duration of a user in each sub-region is estimated according to the region characteristics of each sub-region and the user characteristics corresponding to the terminal, the estimated occupation duration corresponding to each moving path is further determined, and the path recommendation information is pushed to the terminal according to the estimated occupation duration corresponding to each moving path.
Referring to fig. 9, a block diagram of a path recommendation information pushing apparatus according to an exemplary embodiment of the present application is shown. The path recommendation information pushing device can be applied to a computer device, which can be a server or a terminal, wherein the terminal can be the terminal shown in fig. 3, and the server can be the server shown in fig. 3. As shown in fig. 9, the route recommendation information pushing apparatus includes:
a determining module 910, configured to determine at least one moving path in a path search area, where the path search area includes m sub-areas, each moving path passes through n sub-areas of the m sub-areas, m and n are positive integers, and m is greater than or equal to n and is greater than or equal to 1;
a first obtaining module 920, configured to obtain expected tour duration of each of the m sub-areas according to the area characteristic of each of the m sub-areas and the user characteristic corresponding to the terminal; the estimated tour duration is the estimated tour duration of a user corresponding to the terminal in a corresponding sub-area;
a second obtaining module 930, configured to obtain estimated occupation durations of the at least one moving path according to the estimated tour durations of the m sub-areas;
the pushing module 940 is configured to push path recommendation information to the terminal according to the respective estimated occupied time of the at least one moving path.
In a possible implementation manner, the first obtaining module 920 includes:
the extraction sub-module is used for extracting the region characteristics of a target sub-region, and the target sub-region is any one of the m sub-regions;
the first acquisition submodule is used for inputting the regional characteristics of the target sub-region and the user characteristics corresponding to the terminal into the tour duration estimation model to obtain the estimated tour duration of the target sub-region; the tour duration estimation model is obtained by training according to the regional characteristics of the sample sub-region, the user characteristics of the sample users and the historical tour duration of the sample users in the sample sub-region.
In one possible implementation, the tour duration estimation model is a bayesian network model;
the first obtaining submodule includes:
the first obtaining subunit is configured to input the regional characteristics of the target sub-region and the user characteristics corresponding to the terminal into the tour duration estimation model, and obtain m sub-estimated tour durations output by the tour duration estimation model and probabilities corresponding to the m sub-estimated tour durations respectively;
and the first calculating subunit is used for calculating the expected visit duration of the target sub-area according to the m sub-expected visit durations and the probabilities corresponding to the m sub-expected visit durations respectively.
In a possible implementation manner, the second obtaining module 930 includes:
the second obtaining sub-module is used for obtaining the expected tour duration of each of the n sub-areas which the at least one moving path passes through in the m sub-areas;
and the third obtaining sub-module is used for obtaining the estimated occupation time of each of the at least one moving path according to the estimated visit time of each of the n sub-regions through which the at least one moving path passes.
In one possible implementation, the pushing module 940 includes:
the first determining submodule is used for determining each moving path of the at least one moving path, wherein the corresponding estimated occupied time is within a preset time range, as a recommendable path;
and the pushing submodule is used for pushing the path recommendation information to the terminal according to each recommendable path.
In one possible implementation, the pushing submodule includes:
the second obtaining subunit is configured to obtain a respective predicted feedback result of each recommendable path according to a respective predicted tour duration of each recommendable path and a user characteristic corresponding to the terminal; the estimated feedback result is a satisfaction feedback result of a user corresponding to the predicted terminal on the recommendable path;
the sorting subunit is used for sorting the recommendable paths according to their respective predicted feedback results;
and the pushing subunit is used for pushing the recommendation information of the recommended paths to the terminal according to the sequencing result of each recommendable path.
In a possible implementation manner, the second obtaining subunit is configured to input the estimated tour duration of the target recommendable path and the user characteristics corresponding to the terminal into the feedback result estimation model, and obtain an estimated feedback result of the target recommendable path; the target recommendable path is any one of recommendable paths; the feedback result estimation model is obtained by training according to the estimated tour duration of the sample path, the user characteristics of the sample user and the satisfaction feedback result of the sample user on the sample path.
In one possible implementation, the feedback result prediction model is a bayesian network model;
the second obtaining subunit is configured to input the predicted tour duration of the target recommendable path and the user characteristics corresponding to the terminal into the feedback result prediction model, and obtain m sub-predicted feedback results output by the feedback result prediction model and probabilities corresponding to the m sub-predicted feedback results respectively;
and calculating the predicted feedback result of the target recommendable path according to the m sub-predicted feedback results and the probabilities corresponding to the m sub-predicted feedback results respectively.
In one possible implementation, before determining at least one moving path within the path search area, the apparatus further includes:
the third acquisition module is used for acquiring a starting point and an end point of a user corresponding to the terminal;
and the building module is used for building a path searching area according to the starting point and the end point.
To sum up, the path recommendation information pushing device provided in the embodiment of the present application is applied to a computer device, and determines at least one moving path including at least one sub-region in a path search region, and pre-estimates a tour duration of a user in each sub-region according to a region characteristic of each sub-region and a user characteristic corresponding to a terminal, so as to determine a pre-estimated occupation duration corresponding to each moving path, and pushes path recommendation information to the terminal according to the pre-estimated occupation duration corresponding to each moving path, so that the pre-estimated occupation duration of the moving path can be more accurately calculated based on the user characteristic and the region characteristic of the sub-region in the pushing process of the path recommendation information, thereby improving the accuracy and flexibility of pushing the path recommendation information.
Fig. 10 is a block diagram illustrating the structure of a computer device 1000 according to an example embodiment. The computer device 1000 may be a terminal such as a smartphone, tablet computer or desktop computer. The computer device 1000 may also be referred to by other names such as user equipment, portable terminal, laptop terminal, desktop terminal, and the like.
Generally, the computer device 1000 includes: a processor 1001 and a memory 1002.
In some embodiments, a non-transitory computer readable storage medium in the memory 1002 is used to store at least one instruction for execution by the processor 1001 to implement the methods provided by the method embodiments herein.
In some embodiments, the computer device 1000 may further optionally include: a peripheral interface 1003 and at least one peripheral. The processor 1001, memory 1002 and peripheral interface 1003 may be connected by a bus or signal line. Various peripheral devices may be connected to peripheral interface 1003 via a bus, signal line, or circuit board. Specifically, the peripheral device includes: at least one of radio frequency circuitry 1004, touch screen display 1005, camera 1006, audio circuitry 1007, positioning components 1008, and power supply 1009.
In some embodiments, the computer device 1000 also includes one or more sensors 1010. The one or more sensors 1010 include, but are not limited to: acceleration sensor 1011, gyro sensor 1015, pressure sensor 1013, fingerprint sensor 1014, optical sensor 1015, and proximity sensor 1016.
Those skilled in the art will appreciate that the configuration shown in FIG. 10 is not intended to be limiting of the computer device 1000, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components may be used.
FIG. 11 is a block diagram illustrating the architecture of a computer device 1100 in accordance with an exemplary embodiment. The computer device 1100 may be implemented as a server in the above-described arrangement. The computer device 1100 includes a Central Processing Unit (CPU) 1101, a system Memory 1104 including a Random Access Memory (RAM) 1102 and a Read-Only Memory (ROM) 1103, and a system bus 1105 connecting the system Memory 1104 and the CPU 1101. The computer device 1100 also includes a basic Input/Output system (I/O system) 1106, which facilitates transfer of information between devices within the computer, and a mass storage device 1107 for storing an operating system 1113, application programs 1114, and other program modules 1115.
The basic input/output system 1106 includes a display 1108 for displaying information and an input device 1109 such as a mouse, keyboard, etc. for user input of information. Wherein the display 1108 and input device 1109 are connected to the central processing unit 1101 through an input output controller 1110 connected to the system bus 1105. The basic input/output system 1106 may also include an input/output controller 1110 for receiving and processing input from a number of other devices, such as a keyboard, mouse, or electronic stylus. Similarly, input-output controller 1110 also provides output to a display screen, a printer, or other type of output device.
The mass storage device 1107 is connected to the central processing unit 1101 through a mass storage controller (not shown) that is connected to the system bus 1105. The mass storage device 1107 and its associated computer-readable media provide non-volatile storage for the computer device 1100. That is, the mass storage device 1107 may include a computer-readable medium (not shown) such as a hard disk or Compact disk-Only Memory (CD-ROM) drive.
Without loss of generality, the computer-readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, CD-ROM, Digital Versatile Disks (DVD), or other optical, magnetic, or other magnetic storage devices. Of course, those skilled in the art will appreciate that the computer storage media is not limited to the foregoing. The system memory 1104 and mass storage device 1107 described above may be collectively referred to as memory.
According to various embodiments of the present application, the computer device 1100 may also operate as a remote computer connected to a network via a network, such as the Internet. That is, the computer device 1100 may connect to the network 1112 through the network interface unit 1111 that is coupled to the system bus 1105, or may connect to other types of networks or remote computer systems (not shown) using the network interface unit 1111.
The memory further includes one or more programs, the one or more programs are stored in the memory, and the central processing unit 1101 implements all or part of the steps of the method shown in fig. 4 or fig. 6 by executing the one or more programs.
Those skilled in the art will appreciate that in one or more of the examples described above, the functions described in the embodiments of the present application may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The embodiment of the present application further provides a computer-readable storage medium, configured to store at least one instruction, at least one program, a code set, or an instruction set, where the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the above path recommendation information pushing method. For example, the computer readable storage medium may be a ROM, a RAM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, so that the computer device executes all or part of the steps of the path recommendation information pushing method shown in the above embodiments.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (12)
1. A method for pushing path recommendation information is characterized by comprising the following steps:
determining at least one moving path in a path searching area, wherein the path searching area comprises m sub-areas, each moving path passes through n sub-areas in the m sub-areas, m and n are positive integers, and m is more than or equal to n and is more than or equal to 1;
acquiring the expected visit duration of each of the m sub-areas according to the area characteristics of each of the m sub-areas and the user characteristics corresponding to the terminal; the estimated tour duration is estimated tour duration of a user corresponding to the terminal in a corresponding sub-area;
acquiring the estimated occupation time of each of the at least one moving path according to the estimated visit time of each of the m sub-areas;
and pushing path recommendation information to the terminal according to the respective estimated occupation duration of the at least one moving path.
2. The method according to claim 1, wherein the obtaining the expected visit duration for each of the m sub-areas according to the area characteristic for each of the m sub-areas and the user characteristic corresponding to the terminal comprises:
extracting region features of a target sub-region, wherein the target sub-region is any one of the m sub-regions;
inputting the regional characteristics of the target sub-region and the user characteristics corresponding to the terminal into a tour duration estimation model to obtain the estimated tour duration of the target sub-region; the tour duration estimation model is obtained by training according to the regional characteristics of the sample sub-region, the user characteristics of the sample users and the historical tour duration of the sample users in the sample sub-region.
3. The method of claim 2, wherein the tour duration prediction model is a bayesian network model;
inputting the regional characteristics of the target sub-region and the user characteristics corresponding to the terminal into a tour duration estimation model to obtain the estimated tour duration of the target sub-region, wherein the method comprises the following steps:
inputting the regional characteristics of the target sub-region and the user characteristics corresponding to the terminal into a tour duration estimation model, and obtaining m sub-estimated tour durations output by the tour duration estimation model and probabilities corresponding to the m sub-estimated tour durations respectively;
and calculating the expected visit duration of the target sub-area according to the m sub-expected visit durations and the probabilities corresponding to the m sub-expected visit durations respectively.
4. The method according to claim 1, wherein the obtaining the estimated duration of occupation of each of the at least one moving path according to the estimated duration of the tour for each of the m sub-regions comprises:
acquiring the expected visit duration of each of the n sub-regions passed by the at least one moving path in the m sub-regions;
and acquiring the estimated occupation time of the at least one moving path according to the estimated visit time of the n sub-regions passed by the at least one moving path.
5. The method according to claim 1, wherein the pushing path recommendation information to the terminal according to the respective estimated duration of occupation of the at least one moving path comprises:
determining each moving path of the at least one moving path, wherein the corresponding estimated occupation time is within a preset time range, as a recommendable path;
and pushing path recommendation information to the terminal according to each recommendable path.
6. The method according to claim 5, wherein the pushing path recommendation information to the terminal according to each of the recommendable paths comprises:
acquiring respective predicted feedback results of the recommendable paths according to respective predicted tour durations of the recommendable paths and user characteristics corresponding to the terminal; the predicted feedback result is a predicted satisfaction feedback result of the user corresponding to the terminal on the recommendable path;
sequencing the recommendable paths according to the respective predicted feedback results of the recommendable paths;
and pushing recommendation information of the recommended paths to the terminal according to the sequencing result of each recommendable path.
7. The method according to claim 6, wherein the obtaining of the predicted feedback result of each recommendable path according to the predicted tour duration of each recommendable path and the user characteristics corresponding to the terminal comprises:
inputting the estimated tour duration of the target recommendable path and the user characteristics corresponding to the terminal into a feedback result estimation model to obtain an estimated feedback result of the target recommendable path; the target recommendable path is any one of the recommendable paths; the feedback result estimation model is obtained by training according to the estimated tour duration of the sample path, the user characteristics of the sample user and the satisfaction feedback result of the sample user to the sample path.
8. The method of claim 7, wherein the feedback result prediction model is a bayesian network model;
the step of inputting the estimated tour duration of the target recommendable path and the user characteristics corresponding to the terminal into a feedback result estimation model to obtain the estimated feedback result of the target recommendable path includes:
inputting the estimated tour duration of the target recommendable path and the user characteristics corresponding to the terminal into a feedback result estimation model, and obtaining m sub-estimated feedback results output by the feedback result estimation model and probabilities corresponding to the m sub-estimated feedback results respectively;
and calculating the predicted feedback result of the target recommendable path according to the m sub-predicted feedback results and the probabilities corresponding to the m sub-predicted feedback results respectively.
9. The method of claim 1, wherein prior to determining at least one movement path within the path search area, the method further comprises:
acquiring a starting point and an end point of a user corresponding to the terminal;
and constructing the path search area according to the starting point and the end point.
10. A path recommendation information pushing apparatus, the apparatus comprising:
the determining module is used for determining at least one moving path in a path searching area, the path searching area comprises m sub-areas, each moving path passes through n sub-areas in the m sub-areas, m and n are positive integers, and m is more than or equal to n and is more than or equal to 1;
a first obtaining module, configured to obtain expected tour duration of each of the m sub-regions according to the region feature of each of the m sub-regions and a user feature corresponding to a terminal; the estimated tour duration is estimated tour duration of a user corresponding to the terminal in a corresponding sub-area;
a second module, configured to obtain respective estimated occupation durations of the at least one movement path according to respective estimated visit durations of the m sub-areas;
and the pushing module is used for pushing the path recommendation information to the terminal according to the respective estimated occupation duration of the at least one moving path.
11. A computer device comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by the processor to implement the path recommendation information pushing method according to any one of claims 1 to 9.
12. A computer-readable storage medium, wherein at least one instruction, at least one program, a set of codes, or a set of instructions is stored in the storage medium, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by a processor to implement the path recommendation information pushing method according to any one of claims 1 to 9.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112541021A (en) * | 2020-12-10 | 2021-03-23 | 北京百度网讯科技有限公司 | Route evaluation method, and scenic spot tour estimation duration calculation method and device |
| CN112559891A (en) * | 2020-12-24 | 2021-03-26 | 北京百度网讯科技有限公司 | Route recommendation method and device, electronic equipment and storage medium |
| CN114510651A (en) * | 2022-04-19 | 2022-05-17 | 深圳本地宝新媒体技术有限公司 | Local region characteristic-based tourism strategy pushing method and system |
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2020
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112541021A (en) * | 2020-12-10 | 2021-03-23 | 北京百度网讯科技有限公司 | Route evaluation method, and scenic spot tour estimation duration calculation method and device |
| CN112541021B (en) * | 2020-12-10 | 2024-05-03 | 北京百度网讯科技有限公司 | Route evaluation method, scenic spot tour estimated time length calculation method and device |
| CN112559891A (en) * | 2020-12-24 | 2021-03-26 | 北京百度网讯科技有限公司 | Route recommendation method and device, electronic equipment and storage medium |
| WO2022134479A1 (en) * | 2020-12-24 | 2022-06-30 | 北京百度网讯科技有限公司 | Route recommendation method and apparatus, electronic device, and storage medium |
| CN114510651A (en) * | 2022-04-19 | 2022-05-17 | 深圳本地宝新媒体技术有限公司 | Local region characteristic-based tourism strategy pushing method and system |
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