CN106056231B - Automatic matching recommendation method and device based on multi-party position and time availability - Google Patents
Automatic matching recommendation method and device based on multi-party position and time availability Download PDFInfo
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Abstract
The invention provides an automatic matching recommendation method and device based on multi-party position and time availability, wherein the method comprises the following steps: acquiring position information and time information of a plurality of 1 st objects; acquiring position information and time information of a plurality of 2 nd objects; and so on until obtaining the position information and the time information of a plurality of Z-th objects; and target location information and target time information, ordering the plurality of 1 st objects, the plurality of 2 nd objects …, and the plurality of Z th objects; and combining the sorted plurality of objects No. 1 and the sorted plurality of objects No. 2 … according to a preset combination rule to generate a recommended combination list. Has the advantages that: by utilizing the relevant information of each transaction party to perform transaction recommendation matching and executing corresponding data processing, the problem of resource allocation optimization of three or more parties in a life electronic transaction scene is effectively solved, and the overall efficiency of transaction is improved.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to an automatic matching recommendation method and device based on multi-party location and time availability.
Background
There are many application scenarios in daily life that require store-to-store services, in which often three or even more parties are involved. Taking the beauty salon industry as an example, a participant to a store service may include a plurality of service providers, including but not limited to salons and beauty technicians, and a service recipient, which is a customer. The current conventional transaction is bound by the asymmetry of information between the customer and the technician, resulting in the customer not being able to enjoy the medium-high end hairdressing service well.
Therefore, there is a need for a scheme for recommending beauty parlors and beauty technicians to customers according to the customer's reservation information.
Disclosure of Invention
The invention provides an automatic matching recommendation method and device based on multi-party position and time availability, which are used for providing a recommendation combination about at least two different service providers for a service requester by collecting position information and time information of the at least two different service providers and the service requester.
The invention provides an automatic matching recommendation method based on multi-party position and time availability, which comprises the following steps:
acquiring position information and time information of a plurality of 1 st objects;
acquiring position information and time information of a plurality of 2 nd objects, wherein the 2 nd objects are different from the 1 st objects;
and so on until obtaining the position information and the time information of a plurality of Z-th objects; wherein the 1 st object, the 2 nd object … and the Z th object are different objects; z is a natural number, and Z is greater than or equal to 2;
acquiring target position information and target time information;
sorting the plurality of 1 st objects based on position information and time information of the plurality of 1 st objects and the target position information and target time information;
sorting the plurality of 2 nd objects based on the position information and time information of the plurality of 2 nd objects and the target position information and target time information;
and so on, ordering the plurality of Z-th objects based on the position information and the time information of the plurality of Z-th objects and the target position information and the target time information;
combining the sorted plurality of objects 1, the sorted plurality of objects 2 …, the sorted plurality of objects Z according to a predetermined combination rule to generate a recommended combination list, the recommended combination list including at least one recommended combination sorted according to priority, each recommended combination including one of the plurality of objects 1, one of the plurality of objects 2 …, and one of the plurality of objects Z.
Preferably, the sorting of the ith objects based on the position information and time information of the ith objects and the target position information and target time information, wherein i is 1 and 2 … Z, includes:
for each ith object in the plurality of ith objects, determining a difference between its position information and the target position information as a position difference, determining a difference between its time information and the target time information as a time difference, and determining an evaluation parameter of the ith object based on the position difference and the time difference;
and sorting the plurality of ith objects according to the evaluation parameter of each ith object.
Preferably, the determining the evaluation parameter of the ith object based on the position difference and the time difference comprises:
calculating an initial evaluation parameter of the ith object based on the position difference and the time difference; and
and adjusting the initial evaluation parameter of the ith object by utilizing the additional information of the ith object to determine the final evaluation parameter of the ith object.
Preferably, the additional information of the ith object includes: the service capability information of the ith object and/or the service level information of the ith object and/or the service preference information of a service receiver.
Preferably, the determining the evaluation parameter of the ith object based on the position difference and the time difference comprises:
weighting the position difference with a position difference weight, weighting the time difference with a time difference weight, and summing the position difference weighting and the time difference weighting to obtain a weighted sum of the position difference and the time difference;
and using the weighted sum of the position difference and the time difference as the evaluation parameter of the 1 st object.
Preferably, the determining the evaluation parameter of the ith object based on the position difference and the time difference comprises:
acquiring an ith position difference threshold and an ith time difference threshold;
calculating a difference between the ith position difference threshold and the position difference of the ith object as a position difference factor, and calculating a difference between the ith time difference threshold and the time difference of the ith object as a time difference factor;
determining an evaluation parameter of the ith object based on the position difference factor and the time difference factor.
Preferably, determining the evaluation parameter of the i-th object based on the position difference factor and the time difference factor comprises at least one of:
calculating the product of the position difference factor and the time difference factor as an evaluation parameter of the ith object;
calculating a weighted sum of the position difference factor and the position difference factor as an evaluation parameter of the ith object;
calculating the product of the m-th power of the position difference factor and the m-th power of the time difference factor as an evaluation parameter of the ith object, wherein m is a real number greater than 0;
and calculating a weighted sum of the k power of the position difference factor and the k power of the time difference factor as an evaluation parameter of the ith object, wherein k is a real number larger than 0.
Preferably, let M be present in the ordered list of objects 11M exists in the 1 st object and the 2 nd object in the ordered list2The existence of M in the ordered list of the Z-th object of 2 nd object …ZA Z-th object, wherein the predetermined combination rule is:
multiplying the order in the 1 st object ordered list with the 1 st combination weight respectively to obtain M1A 1 st order weighting value;
multiplying the order in the ordered list of objects 2 with the combination weight 2 to obtain M2A 2 nd order weighting value;
… and so on, multiplying the order in the sorted list of Z-th object by the Z-th combining weight to obtain MzA Z-th order weighting value;
adding each 1 st order weighting value in the ordered list of 1 st objects to each Z order weighting value in the ordered list of 2 nd objects, respectively, of each 2 nd order weighting value … Z th objects in the ordered list of 2 nd objects to obtain M1×M2…×MzCombining order values;
providing a reaction product of1×M2…×MzAnd the predetermined number of combinations with the highest ranking in the combination ranking values are used as the at least one recommended combination.
Preferably, the 1 st object, the 2 nd object … and the Z th object are different transaction service providers; the position information of each transaction service provider is: default geographic location information of the transaction service provider; the time information of each transaction service provider is: idle time information of the transaction service provider;
the target location information and the target time information refer to: desired location information and desired time information of a transaction service recipient.
The invention also provides an automatic matching recommendation device based on multi-party position and time availability, which comprises:
object information acquiring means for acquiring position information and time information of a plurality of 1 st objects, acquiring position information and time information … of a plurality of 2 nd objects, and so on until acquiring position information and time information of a plurality of Z-th objects; wherein the 1 st object, the 2 nd object … and the Z th object are different objects; z is a natural number, and Z is greater than or equal to 2;
target information acquiring means for acquiring target position information and target time information;
sorting means for sorting the plurality of 1 st objects based on position information and time information of the plurality of 1 st objects and the target position information and target time information; and sorting the plurality of 2 nd objects based on the position information and time information of the plurality of 2 nd objects and the target position information and target time information; … sorting the plurality of Z-th objects based on the position information and time information of the plurality of Z-th objects and the target position information and target time information;
combining and recommending means for combining the plurality of sorted 1 st objects and the plurality of sorted 2 nd objects … sorted according to a predetermined combination rule to generate a recommended combination list, the recommended combination list including at least one recommended combination sorted according to priority, each recommended combination including one of the plurality of 1 st objects and one of the plurality of 2 nd objects … and one of the plurality of Z th objects.
Preferably, for each ith object in the plurality of ith objects, the sorting means determines a difference between its position information and the target position information as a position difference, determines a difference between its time information and the target time information as a time difference, and determines an evaluation parameter of the ith object based on the position difference and the time difference; wherein i is 1, 2 … Z; and
the sorting device sorts the plurality of ith objects according to the evaluation parameter of each ith object.
Preferably, the ranking device calculates an initial evaluation parameter of the ith object based on the position difference and the time difference, and adjusts the initial evaluation parameter of the ith object by using additional information of the ith object to determine the evaluation parameter of the ith object.
Preferably, the additional information of the ith object includes: the service capability information of the ith object and/or the service level information of the ith object and/or the service preference information of a service receiver.
Preferably, let M be present in the ordered list of objects 11M exists in the 1 st object and the 2 nd object in the ordered list2The existence of M in the ordered list of the Z-th object of 2 nd object …ZA Z-th object, wherein the predetermined combination rule is:
multiplying the order in the 1 st object ordered list with the 1 st combination weight respectively to obtain M1A 1 st order weighting value;
multiplying the order in the ordered list of objects 2 with the combination weight 2 to obtain M2A 2 nd order weighting value;
… and so on, multiplying the order in the sorted list of Z-th object by the Z-th combining weight to obtain MzA Z-th order weighting value;
adding each 1 st order weighting value in the ordered list of 1 st objects to each Z order weighting value in the ordered list of 2 nd objects, respectively, of each 2 nd order weighting value … Z th objects in the ordered list of 2 nd objects to obtain M1×M2…×MzCombining order values;
providing a reaction product of1×M2…×MzAnd the predetermined number of combinations with the highest ranking in the combination ranking values are used as the at least one recommended combination.
Preferably, the 1 st object, the 2 nd object … and the Z th object are different transaction service providers; the position information of each transaction service provider is: default geographic location information of the transaction service provider; the time information of each transaction service provider is: idle time information of the transaction service provider;
the target location information and the target time information refer to: desired location information and desired time information of a transaction service recipient.
The automatic matching recommendation method and device based on multi-party position and time availability provided by the invention have the following advantages:
by utilizing the relevant information of each transaction party to perform transaction recommendation matching and executing corresponding data processing, the problem of resource allocation optimization of three or more parties in a life electronic transaction scene is effectively solved, and the overall efficiency of transaction is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart diagram of a method 100 for automatic matching recommendation based on multi-party location and time availability in accordance with an embodiment of the present invention;
FIG. 2 is a schematic flow chart diagram of the operation of steps S140 and S150 in the automatic matching recommendation method 100 based on multi-party location and time availability, according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart diagram illustrating the operation of step S230 in FIG. 2 of the method 100 for automatic matching recommendation based on multi-party location and time availability in accordance with an embodiment of the present invention; and
FIG. 4 is a schematic block diagram of an automatic matching recommender 400 based on multi-party location and time availability according to an embodiment of the present invention.
Detailed Description
Various embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Here, it is to be noted that, in the drawings, the same reference numerals are given to constituent parts having substantially the same or similar structures and functions, and repeated description thereof will be omitted.
The invention provides an automatic matching recommendation method and device based on multi-party position and time availability, which comprises the following steps:
acquiring position information and time information of a plurality of 1 st objects;
acquiring position information and time information of a plurality of 2 nd objects, wherein the 2 nd objects are different from the 1 st objects;
and so on until obtaining the position information and the time information of a plurality of Z-th objects; wherein the 1 st object, the 2 nd object … and the Z th object are different objects; z is a natural number, and Z is greater than or equal to 2;
acquiring target position information and target time information;
sorting the plurality of 1 st objects based on position information and time information of the plurality of 1 st objects and the target position information and target time information;
sorting the plurality of 2 nd objects based on the position information and time information of the plurality of 2 nd objects and the target position information and target time information;
and so on, ordering the plurality of Z-th objects based on the position information and the time information of the plurality of Z-th objects and the target position information and the target time information;
combining the sorted plurality of objects 1, the sorted plurality of objects 2 …, the sorted plurality of objects Z according to a predetermined combination rule to generate a recommended combination list, the recommended combination list including at least one recommended combination sorted according to priority, each recommended combination including one of the plurality of objects 1, one of the plurality of objects 2 …, and one of the plurality of objects Z.
According to the automatic matching recommendation method 100 based on multi-party location and time availability of the embodiment of the invention, in the case that three or more different objects participate in a certain service, for example, the 1 st object and the 2 nd object are required to provide services for a target object, by obtaining target location information and target time information of the target object, collecting location information and idle time information of a plurality of 1 st objects, and collecting location information and idle time information of a plurality of 2 nd objects, combined recommendations for the 1 st object and the 2 nd object are provided for the target object.
For example, the 1 st object may be a beauty shop, the 2 nd object may be a beauty parlor, and the target object may be a customer who is scheduled to make a beauty treatment, in which case, the automatic matching recommendation method based on multi-party location and time availability 100 according to the embodiment of the present invention recommends a combination of the beauty parlor and the beauty parlor to the customer, and the customer may select which beauty parlor to make a beauty treatment and may select which beauty parlor to provide a service thereto. It is to be understood that the invention is of course not limited to beauty salons and beauty therapists, but that the invention can be applied to different applications, such as restaurants and chefs, nail rooms and manists, gymnasiums (gymnasiums) and coaches, musicians and tutors, etc., depending on different service requirements.
The automatic matching recommendation method based on the multi-party position and the time availability is used for a scene related to a plurality of service providers, wherein the number of the service providers is Z, and Z is a natural number which is more than or equal to 2. For the convenience of understanding the present invention, in the following examples, Z equal to 2 is exemplified as follows:
as shown in FIG. 1, a schematic flow chart diagram of a method 100 for automatic matching recommendation based on multi-party location and time availability is shown, in accordance with an embodiment of the present invention. The automatic matching recommendation method based on multi-party position and time availability according to the embodiment of the invention can be realized on a client side or a server side. The client may be installed on devices such as personal computers, smart phones, tablet computers, personal digital assistants, and any other electronic device having network communication and user interaction capabilities.
In step S110, first, position information and time information of a plurality of 1 st objects are acquired. Still taking the above-mentioned beauty application as an example, the plurality of 1 st objects may include a plurality of beauty salons, for example, the plurality of beauty salons may include a pre-registered beauty salon, and the position information and the free time information of each beauty salon, which may be time information of a vacant beauty table (bed) of the beauty salon, are acquired at step S110.
In step S120, position information and time information of a plurality of 2 nd objects are acquired. The plurality of 2 nd objects may include a plurality of beauty therapists, for example, the plurality of beauty therapists may include pre-registered beauty therapists, and the current location information and the idle time information of each beauty therapist may be acquired at step S120, and optionally, a reservation table of each beauty therapist, in which reservation time information and reservation service location information of the beauty therapist may be included, may also be acquired. That is, in step S120, optionally, the position information of the 2 nd object may include different contents according to different situations.
In step S130, target position information and target time information are acquired. The target position information may be current position information of the target object, and the target time information may be desired time information of the target object. In this case, the current location information may be provided by the electronic device of the client used to implement the target object. Alternatively, the target location information may be target location information input by the target object through an electronic device in which the client is installed.
It should be appreciated that steps S110, S120, S130 may be performed in parallel, or may be performed sequentially in any order.
For example, in the case that the automatic matching recommendation method 100 based on multi-party location and time availability according to the embodiment of the present invention is implemented at the client, step S130 may be performed first, and then all the 1 st objects registered in advance may be filtered according to the target location information acquired at step S130 to obtain a plurality of selectable 1 st objects, and similarly all the 2 nd objects registered in advance may also be filtered according to the target location information acquired at step S130 to obtain a plurality of selectable 2 nd objects.
For example, in the case that the automatic matching recommendation method 200 based on multi-party location and time availability according to an embodiment of the present invention is implemented on the server side, steps S110 and S120 may be performed first, and then step S130 may be performed.
In step S140, the plurality of 1 st objects are sorted based on the position information and the time information of the plurality of 1 st objects and the target position information and the target time information.
In step S150, the plurality of 2 nd objects are sorted based on the position information and the time information of the plurality of 2 nd objects and the target position information and the target time information.
Then, in step S160, the sorted plurality of 1 st objects and the sorted plurality of 2 nd objects are combined according to a predetermined combination rule to generate a recommended combination list, the recommended combination list including at least one recommended combination sorted according to priority, each recommended combination including one of the plurality of 1 st objects and one of the plurality of 2 nd objects.
Next, operations of steps S140 and S150 in the automatic matching recommendation method based on multi-party location and time availability according to an embodiment of the present invention will be described with reference to fig. 2. In the following description, the description is made taking the step S140 as an example.
As shown in fig. 2, in step S210, a difference between the position information of the 1 st object and the target position information is determined as a position difference.
In step S220, a difference between the time information of the first time and the target time information is determined as a time difference.
In step S230, an evaluation parameter of the 1 st object is determined based on the position difference and the time difference.
In step S240, it is determined whether the current 1 st object is the last 1 st object among the plurality of 1 st objects.
If the current 1 st object is the last 1 st object, in step S250, the 1 st objects are sorted based on their respective evaluation parameters.
It should be understood that, according to the actual situation, in the case that the higher the evaluation parameter is, the better the evaluation parameter is, the 1 st objects may be sorted in the order of the evaluation parameter from high to low; and in the case that a higher evaluation parameter indicates a worse, the 1 st objects may be sorted in order of the evaluation parameter from lower to higher.
If the current 1 st object is not the last 1 st object, the next 1 st object is determined at step S260.
Similarly, the method flow as shown in fig. 2 is also suitable for step S150, with the only difference that: determining a position difference and a time difference for the 2 nd object, determining an evaluation parameter for the 2 nd object, and ordering the plurality of 2 nd objects.
Similarly, according to the actual situation, in the case that the higher the evaluation parameter is, the better the evaluation parameter is, the plurality of 2 nd objects may be sorted with reference to the order of the evaluation parameter from high to low; and in the case that a higher evaluation parameter indicates a worse, the plurality of 2 nd objects may be sorted in the order of the evaluation parameter from lower to higher.
Next, an operation of determining the evaluation parameter of the 1 st object based on the position difference and the time difference in step S230 in fig. 2 will be described with reference to fig. 3.
In step S310, an initial evaluation parameter of the 1 st object is calculated based on the position difference and the time difference.
In step S320, using the additional information of the 1 st object, the initial evaluation parameter of the 1 st object is adjusted to determine the evaluation parameter of the 1 st object.
For example, the additional information of the 1 st object may include a service rating star rating of the 1 st object: one star may represent worst, two stars may represent worse, three stars may represent general, four stars may represent better, and five stars may represent very good. Specifically, a factor coefficient may be respectively assigned to each level of the additional information, and the initial evaluation parameter of the 1 st object may be corrected using the factor coefficients of the respective levels of the additional information to obtain the evaluation parameter of the 1 st object. Here, as an example, the factor coefficient of the level corresponding to the 1 st object may be multiplied by the initial evaluation parameter of the 1 st object to obtain the evaluation parameter of the 1 st object.
In addition, the additional information of the 1 st object may also include the preference of the service requester for the service, for example, in the hair salon industry, the preference of the service requester for the style of hair style may be provided.
Depending on the actual situation, higher evaluation parameters indicate better results, higher levels may be assigned higher factor coefficients, for example, a factor coefficient of 1 for five stars, a factor coefficient of 0.8 for four stars, a factor coefficient of 0.6 for three stars, a factor coefficient of 0.4 for two stars, and a factor coefficient of 0.2 for one star. For example, the initial evaluation parameter of each 1 st object may be multiplied by the factor coefficient of its corresponding grade to obtain the evaluation parameter of the 1 st object, and then the 1 st objects may be sorted in the order of the evaluation parameters from high to low.
Depending on the actual situation, higher evaluation parameters indicate worse, higher grades may be assigned lower factor coefficients, e.g. a factor coefficient of 0.2 for five stars, a factor coefficient of 0.4 for four stars, a factor coefficient of 0.6 for three stars, a factor coefficient of 0.8 for two stars and a factor coefficient of 1 for one star. For example, the initial evaluation parameter of each 1 st object may be multiplied by the factor coefficient of its corresponding grade to obtain the evaluation parameter of the 1 st object, and then the 1 st objects may be sorted in the order of the evaluation parameters from low to high.
In the following, several possible initial evaluation parameter calculation methods will be exemplified, however, it should be understood that the automatic matching recommendation method based on multi-party location and time availability according to the embodiments of the present invention is not limited to the initial evaluation parameter calculation methods listed herein.
First example
The position difference weight of the 1 st object is set as a first position difference weight, and the time difference weight of the 1 st object is set as a first time difference weight.
For each 1 st object, multiplying the position difference of the 1 st object by a first position difference weight to obtain a position difference weight of the 1 st object, multiplying the time difference of the 1 st object by a first time difference weight to obtain a time difference weight of the 1 st object, and then adding the position difference weight and the time difference weight to obtain an evaluation parameter or an initial evaluation parameter of the 1 st object.
Similarly, the position difference weight of the 2 nd object is set as the second position difference weight, and the time difference weight of the 2 nd object is set as the second time difference weight.
For each 2 nd object, multiplying the position difference of the 2 nd object by a second position difference weight to obtain a position difference weight of the 2 nd object, multiplying the time difference of the 2 nd object by a second time difference weight to obtain a time difference weight of the 2 nd object, and then adding the position difference weight and the time difference weight to obtain an evaluation parameter or an initial evaluation parameter of the 2 nd object.
In this first example, a higher value of the evaluation parameter or the initial evaluation parameter of the 1 st object/2 nd object indicates that the ranking of the 1 st object/2 nd object is further back. In other words, in this first example, the plurality of 1 st objects/2 nd objects are sorted in the order of the evaluation parameter or the initial evaluation parameter from small to large.
It should be understood that the time difference here is the absolute value of the time difference, and the distance difference here is also the absolute value of the distance difference.
Second example
Setting the position difference threshold of the 1 st object as a first position difference threshold, and setting the time difference threshold of the 1 st object as a first time difference threshold.
Optionally, the plurality of 1 st objects includes only 1 st objects within a distance from the target location that is less than the first position difference threshold.
For each 1 st object, calculating a difference between the first position difference threshold and the position difference of the 1 st object as a position difference factor, calculating a difference between the first time difference threshold and the time difference of the 1 st object as a time difference factor, and then determining the evaluation parameter of the 1 st object based on the position difference factor and the time difference factor.
Alternatively, as shown in the first example, the position difference weight of the 1 st object is set as the first position difference weight, and the time difference weight of the 1 st object is set as the first time difference weight.
For each 1 st object, multiplying the calculated position difference factor by a first position difference weight to obtain a position difference weight of the 1 st object, and multiplying the calculated time difference factor by a first time difference weight to obtain a time difference weight of the 1 st object, and then adding the position difference weight and the time difference weight to obtain an evaluation parameter or an initial evaluation parameter of the 1 st object.
Similarly, the position difference threshold of the 2 nd object is set as a second position difference threshold, the time difference threshold of the 2 nd object is set as a second time difference threshold, the position difference weight of the 2 nd object is set as a second position difference weight, and the time difference weight of the 2 nd object is set as a second time difference weight.
For each 2 nd object, calculating a difference value between the second position difference threshold value and the position difference of the 2 nd object as a position difference factor, multiplying the calculated position difference factor by a second position difference weight to obtain a position difference weight of the 2 nd object, calculating a difference value between the second time difference threshold value and the time difference of the 2 nd object as a time difference factor, multiplying the calculated time difference factor by a second time difference weight to obtain a time difference weight of the 2 nd object, and then adding the position difference weight and the time difference weight to obtain an evaluation parameter or an initial evaluation parameter of the 2 nd object.
In this second example, a higher value of the evaluation parameter or the initial evaluation parameter of the 1 st object/2 nd object indicates that the ranking of the 1 st object/2 nd object is higher. In other words, in this second example, the plurality of 1 st objects/the plurality of 2 nd objects are sorted in the order of the evaluation parameter or the initial evaluation parameter from large to small.
Furthermore, in this second example, determining the evaluation parameter of each 1 st object based on the position difference factor and the time difference factor of the 1 st object may also be achieved by: calculating the product of the position difference factor and the time difference factor as the evaluation parameter of the 1 st object; calculating the product of the m-th power of the position difference factor and the m-th power of the time difference factor as the evaluation parameter of the 1 st object, wherein m is a real number larger than 0; or calculating a weighted sum of the k-th power of the position difference factor and the k-th power of the time difference factor as the evaluation parameter of the 1 st object, wherein k is a real number greater than 0.
It should be understood that, in the case where the position information of the 2 nd object is variable, for example, in the case where the 2 nd object is a cosmetologist, the position information of the 2 nd object may be the position information closest to the target time information in the schedule of the 2 nd object, or may also be default position information of the 2 nd object, or may also be current position information of the 2 nd object, as needed.
Returning to fig. 1, in step S160, the sorted plurality of 1 st objects and the sorted plurality of 2 nd objects are combined according to a predetermined combination rule to generate a recommended combination list, the recommended combination list including at least one recommended combination sorted according to priority, each recommended combination including one of the plurality of 1 st objects and one of the plurality of 2 nd objects.
The combination rule is described by taking as an example that there are M1 st objects in the sorted list of 1 st objects and there are N2 nd objects in the sorted list of 2 nd objects.
Alternatively, the predetermined combination rule may use, as one recommended combination, a combination of the 1 st object and the 2 nd object in the same order in the sorted list of the 1 st object and the sorted list of the 2 nd object, for example, a combination of the 1 st object in the sorted list of the 1 st object with an order i and the 2 nd object in the sorted list of the 2 nd object with the same order i, where i is an integer greater than or equal to 1, as the ith recommended combination.
Alternatively, the first combination weight R1 may be assigned to the sorted list of the 1 st object and the second combination weight R2 may be assigned to the sorted list of the 2 nd object, the orders in the sorted list of the 1 st object are respectively multiplied by the first combination weight R1 to obtain first order weights, i.e., the orders 1 to M in the sorted list of the 1 st object are respectively calculated as 1 × R1, 2 × R1, …, M × R1, and the orders in the sorted list of the 2 nd object are respectively multiplied by the second combination weight R2 to obtain second order weights, i.e., the orders 1 to N in the sorted list of the 2 nd object are respectively calculated as 1 × R2, 2 × R2, …, N × R2. Then, each first order weighted value in the 1 st object ordered list is added to each second order weighted value in the 2 nd object ordered list to obtain an M × N combined order value, so that M × N recommended combinations can be provided.
Alternatively, M, N or a maximum value of mxn may be set.
Alternatively, it may be set that only the top X recommended combinations of the M × N recommended combinations are presented.
Therefore, the automatic matching recommendation method and device based on multi-party location and time availability provided by the invention can be summarized as follows:
1. involving Z transaction service providers and 1 transaction service requester, e.g., a service floor provider, a service technician, and a customer; in the invention, geographic information based on position and idle time information based on service capacity of each transaction service provider need to be acquired; the expected time information and the expected position information of a transaction service requester are also required to be automatically collected in real time;
2. additional information needs to be obtained, including: capability and level information for different service contents for each transaction service provider; preference information of a transaction service requester for a service.
3. The system carries out matching calculation according to the information, carries out covariance calculation on basic items (time and place) based on multiple parties to obtain correlation, and carries out comprehensive calculation of different weights by using additional items (capability and preference) to carry out correction, thereby obtaining the final service recommendation combination.
Therefore, the automatic matching recommendation method and device based on the multi-party position and time availability can automatically match and recommend transactions involving multiple parties, thereby optimizing transaction resources on a platform, meeting the attention of transaction parties to time and place and improving the overall efficiency of the transactions.
Hereinafter, an automatic matching recommendation apparatus based on multi-party location and time availability according to an embodiment of the present invention will be briefly described with reference to fig. 4. The automatic matching recommendation device based on multi-party position and time availability can be implemented on a client side or a server side. The client may be installed on devices such as personal computers, smart phones, tablet computers, personal digital assistants, and any other electronic device having network communication and user interaction capabilities. Hereinafter, the device in which the client is installed is referred to as a personal electronic device.
As shown in fig. 4, an automatic matching recommendation apparatus 400 based on multi-party location and time availability according to an embodiment of the present invention, taking two transaction service providers and a transaction service requester as an example, may include: object information acquiring means 410, object information acquiring means 420, sorting means 430, and combination recommending means 440.
The object information acquiring means 410 acquires position information and time information of a plurality of 1 st objects, and acquires position information and time information of a plurality of 2 nd objects. The object information acquiring means 410 may perform step S110 and step S120.
The target information acquiring means 420 acquires target position information and target time information. The target position information may be current position information of the target object or position information input by the target object. The target information acquisition means 420 may perform step S130.
The sorting means 430 sorts the plurality of 1 st objects based on the position information and time information of the plurality of 1 st objects and the target position information and target time information; and sorting the plurality of 2 nd objects based on the position information and time information of the plurality of 2 nd objects and the target position information and target time information. The sorting means 430 may perform steps S140 and S150, in particular may perform steps S210-S260, and steps S310-320.
The combination recommending means 440 combines the sorted plurality of 1 st objects and the sorted plurality of 2 nd objects according to a predetermined combination rule to generate a recommended combination list including at least one recommended combination sorted according to priority, each recommended combination including one of the plurality of 1 st objects and one of the plurality of 2 nd objects. The combination recommending means 440 may perform step S160.
In the case that the automatic matching recommending apparatus 400 based on multi-party location and time availability is implemented by a personal electronic device, the object information acquiring apparatus 410 may be implemented by a network communication component on the personal electronic device, the target information acquiring apparatus 420 may be implemented by a positioning component, a human-computer interaction component, etc. on the personal electronic device, and the sorting apparatus 430 and the recommendation combining apparatus 440 may be implemented by a processor on the personal electronic device, specifically by the processor running appropriate program code. Then, the personal electronic device may output the recommended combination generated by the recommended combination means 400 through a human-machine interface.
In case the automatic matching recommendation device 400 based on multi-party location and time availability is implemented by a server, the object information obtaining device 410 and the target information obtaining device 420 may be implemented by network communication means on the server, and the sorting device 430 and the recommendation combining device 440 may be implemented by a processor on the server, in particular by a processor running suitable program code. The server may then communicate the generated recommended combination to the personal electronic device via the network communication component for output by the human-machine interface of the personal electronic device.
According to the embodiment of the invention, the 1 st object is a shop, specifically a beauty salon, a gymnasium and the like, which a target object (i.e. a customer) needs to go, and has fixed geographical location information; the 2 nd object is a person whose geographical location information is variable in real time, who the target object (i.e., customer) wishes to be served by the customer. In other words, the 1 st object providing field, the 2 nd object providing manual service, the target object (customer) needs to reserve the 1 st object and the 2 nd object, after the reservation is successful, the target object reaches the 1 st object at the reserved time point, and the 2 nd object also reaches the 1 st object at the reserved time point, and the 2 nd object provides service to the target object. Thus, according to the embodiment of the present invention, it is possible to provide a recommended combination of the 1 st object and the 2 nd object, which are relatively close to the location thereof and have high service ratings, to the customer, whereby the customer is likely to receive high-quality service provided by the 2 nd object at a high level at the location of the 1 st object, which is relatively close to the target location thereof.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention may be implemented by means of software plus a necessary hardware platform, and may also be implemented by software or hardware entirely. With this understanding in mind, all or part of the technical solutions of the present invention that contribute to the background can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments or some parts of the embodiments.
Various embodiments of the present invention are described in detail above. However, those skilled in the art will appreciate that various modifications, combinations, or sub-combinations of the embodiments may be made without departing from the spirit and principle of the invention, and such modifications are intended to be within the scope of the invention.
Claims (2)
1. An automatic matching recommendation method based on multi-party position and time availability is characterized in that Z transaction service providers and 1 transaction service requester are involved, and position-based geographic information and service capacity-based idle time information of each transaction service provider need to be acquired; the expected time information and the expected position information of a transaction service requester are also required to be automatically collected in real time;
additional information needs to be obtained, including: capability and level information for different service contents for each transaction service provider; preference information of a transaction service requester for a service;
the system carries out matching calculation according to the information, carries out covariance calculation on basic items based on multiple parties to obtain correlation, wherein the basic items comprise time and places, and carries out comprehensive calculation of different weights by using additional items for correction, and the additional items comprise capability and preference, so that a final service recommendation combination is obtained;
the method specifically comprises the following steps:
acquiring position information and time information of a plurality of 1 st objects;
acquiring position information and time information of a plurality of 2 nd objects, wherein the 2 nd objects are different from the 1 st objects;
and so on until obtaining the position information and the time information of a plurality of Z-th objects; wherein the 1 st object, the 2 nd object … and the Z th object are different objects; z is a natural number, and Z is greater than or equal to 2;
acquiring target position information and target time information;
sorting the plurality of 1 st objects based on position information and time information of the plurality of 1 st objects and the target position information and target time information;
sorting the plurality of 2 nd objects based on the position information and time information of the plurality of 2 nd objects and the target position information and target time information;
and so on, ordering the plurality of Z-th objects based on the position information and the time information of the plurality of Z-th objects and the target position information and the target time information;
combining the sorted plurality of objects 1, the sorted plurality of objects 2 …, the sorted plurality of Z-th objects according to a predetermined combination rule to generate a recommended combination list, the recommended combination list including at least one recommended combination sorted according to priority, each recommended combination including one of the plurality of objects 1, one of the plurality of objects 2 …, and one of the plurality of Z-th objects;
wherein, let M be present in the ordered list of 1 st object1M exists in the 1 st object and the 2 nd object in the ordered list2The existence of M in the ordered list of the Z-th object of 2 nd object …ZA Z-th object, wherein the predetermined combination rule is:
multiplying the order in the 1 st object ordered list with the 1 st combination weight respectively to obtain M1A 1 st order weighting value;
multiplying the order in the ordered list of objects 2 with the combination weight 2 to obtain M2A 2 nd order weighting value;
… and so on, multiplying the order in the sorted list of Z-th object by the Z-th combining weight to obtain MzA Z-th order weighting value;
adding each 1 st order weighting value in the ordered list of 1 st objects to each Z order weighting value in the ordered list of 2 nd objects, respectively, of each 2 nd order weighting value … Z th objects in the ordered list of 2 nd objects to obtain M1×M2…×MzCombining order values;
providing a reaction product of1×M2…×MzA predetermined number of combinations ranked the top in the combination ranking values are taken as the at least one recommended combination;
wherein the plurality of ith objects are sorted based on the position information and time information of the plurality of ith objects and the target position information and target time information, wherein i is 1, 2 … Z, comprising:
for each ith object in the plurality of ith objects, determining a difference between its position information and the target position information as a position difference, determining a difference between its time information and the target time information as a time difference, and determining an evaluation parameter of the ith object based on the position difference and the time difference;
sorting the ith objects according to the evaluation parameters of each ith object;
wherein determining an evaluation parameter of the ith object based on the position difference and the time difference comprises:
calculating an initial evaluation parameter of the ith object based on the position difference and the time difference; and
adjusting the initial evaluation parameter of the ith object by utilizing the additional information of the ith object to determine the final evaluation parameter of the ith object;
wherein the additional information of the ith object includes: service capability information of the ith object and/or service level information of the ith object and/or service preference information of a service receiver;
alternatively, determining the evaluation parameter of the ith object based on the position difference and the time difference comprises:
weighting the position difference with a position difference weight, weighting the time difference with a time difference weight, and summing the position difference weighting and the time difference weighting to obtain a weighted sum of the position difference and the time difference;
using the weighted sum of the position difference and the time difference as an evaluation parameter of the ith object;
alternatively, determining the evaluation parameter of the ith object based on the position difference and the time difference comprises:
acquiring an ith position difference threshold and an ith time difference threshold;
calculating a difference between the ith position difference threshold and the position difference of the ith object as a position difference factor, and calculating a difference between the ith time difference threshold and the time difference of the ith object as a time difference factor;
determining an evaluation parameter of the ith object based on the position difference factor and the time difference factor;
wherein determining an evaluation parameter of the i-th object based on the position difference factor and the time difference factor comprises at least one of:
calculating the product of the position difference factor and the time difference factor as an evaluation parameter of the ith object;
calculating a weighted sum of the position difference factor and the position difference factor as an evaluation parameter of the ith object;
calculating the product of the m-th power of the position difference factor and the m-th power of the time difference factor as an evaluation parameter of the ith object, wherein m is a real number greater than 0;
calculating a weighted sum of a k-th power of the position difference factor and a k-th power of the time difference factor as an evaluation parameter of the ith object, wherein k is a real number greater than 0;
wherein the 1 st object, the 2 nd object … and the Z < th > object are different transaction service providers; the position information of each transaction service provider is: default geographic location information of the transaction service provider; the time information of each transaction service provider is: idle time information of the transaction service provider;
the target location information and the target time information refer to: desired location information and desired time information of a transaction service recipient.
2. An automatic matching recommendation device based on multi-party location and time availability, comprising:
the method comprises the following steps that Z transaction service providers and 1 transaction service requester are involved, and position-based geographic information and service capacity-based idle time information of each transaction service provider need to be acquired; the expected time information and the expected position information of a transaction service requester are also required to be automatically collected in real time;
additional information needs to be obtained, including: capability and level information for different service contents for each transaction service provider; preference information of a transaction service requester for a service;
the system carries out matching calculation according to the information, carries out covariance calculation on basic items based on multiple parties to obtain correlation, wherein the basic items comprise time and places, and carries out comprehensive calculation of different weights by using additional items for correction, and the additional items comprise capability and preference, so that a final service recommendation combination is obtained;
the method specifically comprises the following steps:
object information acquiring means for acquiring position information and time information of a plurality of 1 st objects, acquiring position information and time information … of a plurality of 2 nd objects, and so on until acquiring position information and time information of a plurality of Z-th objects; wherein the 1 st object, the 2 nd object … and the Z th object are different objects; z is a natural number, and Z is greater than or equal to 2;
target information acquiring means for acquiring target position information and target time information;
sorting means for sorting the plurality of 1 st objects based on position information and time information of the plurality of 1 st objects and the target position information and target time information; and sorting the plurality of 2 nd objects based on the position information and time information of the plurality of 2 nd objects and the target position information and target time information; … sorting the plurality of Z-th objects based on the position information and time information of the plurality of Z-th objects and the target position information and target time information;
combining recommending means for combining the plurality of sorted 1 st objects and the plurality of sorted 2 nd objects … sorted according to a predetermined combining rule to generate a recommended combination list, the recommended combination list including at least one recommended combination sorted according to priority, each recommended combination including one of the plurality of 1 st objects and one of the plurality of 2 nd objects … and one of the plurality of Z th objects;
wherein, for each ith object in the plurality of ith objects, the sorting apparatus determines a difference between its position information and the target position information as a position difference, determines a difference between its time information and the target time information as a time difference, and determines an evaluation parameter of the ith object based on the position difference and the time difference; wherein i is 1, 2 … Z; and
the sorting device sorts the plurality of ith objects according to the evaluation parameter of each ith object;
the sorting device calculates an initial evaluation parameter of the ith object based on the position difference and the time difference, and adjusts the initial evaluation parameter of the ith object by utilizing additional information of the ith object to determine the evaluation parameter of the ith object;
wherein the additional information of the ith object includes: service capability information of the ith object and/or service level information of the ith object and/or service preference information of a service receiver;
wherein, let M be present in the ordered list of 1 st object1M exists in the 1 st object and the 2 nd object in the ordered list2The existence of M in the ordered list of the Z-th object of 2 nd object …ZA Z-th object, wherein the predetermined combination rule is:
multiplying the order in the 1 st object ordered list with the 1 st combination weight respectively to obtain M1A 1 st order weighting value;
multiplying the order in the ordered list of objects 2 with the combination weight 2 to obtain M2A 2 nd order weighting value;
… and so on, multiplying the order in the sorted list of Z-th object by the Z-th combining weight to obtain MzA Z-th order weighting value;
adding each 1 st order weighting value in the ordered list of 1 st objects to each Z order weighting value in the ordered list of 2 nd objects, respectively, of each 2 nd order weighting value … Z th objects in the ordered list of 2 nd objects to obtain M1×M2…×MzCombining order values;
providing a reaction product of1×M2…×MzA predetermined number of combinations ranked the top in the combination ranking values are taken as the at least one recommended combination;
wherein the 1 st object, the 2 nd object … and the Z < th > object are different transaction service providers; the position information of each transaction service provider is: default geographic location information of the transaction service provider; the time information of each transaction service provider is: idle time information of the transaction service provider;
the target location information and the target time information refer to: desired location information and desired time information of a transaction service recipient.
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| CN101140651A (en) * | 2006-09-04 | 2008-03-12 | 腾讯科技（深圳）有限公司 | Display objects ordering method and system |
| CN101739461A (en) * | 2009-12-24 | 2010-06-16 | 丘雷 | Multidimensional sequencing method set by user |
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| CN101807186A (en) * | 2009-02-17 | 2010-08-18 | 李铭 | Multidimensional ranking method |
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