CN113570257A - Index data evaluation method and device based on scoring model, medium and equipment - Google Patents
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Abstract
The embodiment of the disclosure discloses a scoring model-based index data evaluation method and device, a storage medium and electronic equipment, wherein the method comprises the following steps: obtaining at least one index data corresponding to a first menstrual person; determining a corresponding scoring model according to the data type corresponding to each index data in the at least one index data; wherein each data type corresponds to at least one scoring model; weighting and accumulating each index data in a set time period according to time by using a decay function to obtain accumulated index data in the set time period; scoring the at least one accumulated index data based on the at least one scoring model, and determining a corresponding index score for each index data; determining a composite score for the first broker based on the at least one metric score; the embodiment realizes automatic acquisition of the comprehensive score of the broker and avoids the problems caused by artificial evaluation.
Description
Technical Field
The disclosure relates to an index data evaluation method and device, medium and equipment based on a scoring model.
Background
Currently, most of the evaluation and assessment of the house brokers rely on evaluation data fed back by customers or evaluation data of managers on the governed brokers. However, if a customer is required to autonomously evaluate a broker, many customers are lazy to participate in the evaluation unless the customer is very satisfied or dissatisfied with the broker, which severely results in insufficient evaluation data and makes it difficult to accurately evaluate the broker.
Disclosure of Invention
The present disclosure is proposed to solve the above technical problems. The embodiment of the disclosure provides a grading model-based index data evaluation method, a grading model-based index data evaluation device, a grading model-based index data evaluation medium and a grading model-based index data evaluation device.
According to an aspect of the embodiments of the present disclosure, there is provided an index data evaluation method based on a scoring model, including:
obtaining at least one index data corresponding to a first menstrual person;
determining a corresponding scoring model according to the data type corresponding to each index data in the at least one index data; wherein each data type corresponds to at least one scoring model;
weighting and accumulating each index data in a set time period according to time by using a decay function to obtain accumulated index data in the set time period;
scoring the at least one accumulated index data based on the at least one scoring model, and determining a corresponding index score for each index data;
determining a composite score for the first broker based on the at least one metric score.
Optionally, before performing a scoring process on the at least one accumulated index data based on the at least one scoring model and determining a corresponding index score for each index data, the method further includes:
training the at least one scoring model based on a training data set; wherein the training dataset includes a plurality of sample broker groups, each sample broker group including a plurality of sample brokers of known rank order.
Optionally, the training the at least one scoring model based on a training data set comprises:
determining, based on the at least one scoring model, a predicted composite score for each of the sample brokers in the sample broker group;
determining a predicted ranking for a plurality of sample brokers in the sample broker group based on each of the sample broker corresponding predicted composite scores;
determining a model loss based on the predicted rankings and known rankings corresponding to the sample broker group;
adjusting model parameters of the at least one scoring model based on the model loss.
Optionally, the determining model loss based on the predicted rankings and the known rankings corresponding to the sample broker group comprises:
determining an accuracy factor for the at least one scoring model based on a number of sample brokers included in the set of sample brokers and a degree of match of each of the sample brokers in the predicted ranking and the known ranking;
determining the extremum accuracy rate based on the predicted composite score;
determining the model loss based on a difference between the accuracy coefficient and a set coefficient and a difference between the extremum accuracy rate and a set accuracy rate.
Optionally, each sample broker respectively corresponds to a known evaluation result;
said determining said extremum accuracy rate based on said predicted composite score comprises:
determining a predicted evaluation result of each sample broker in the sample broker group based on a comparison of the predicted composite score to a preset score threshold;
determining the extremum accuracy rate based on a difference between the predicted evaluation result and the known evaluation result.
Optionally, the determining a composite score for the first broker based on the at least one metric score comprises:
performing a weighted summation of the at least one metric score to obtain a composite score for the first broker.
According to another aspect of the embodiments of the present disclosure, there is provided an index data evaluation apparatus based on a scoring model, including:
the data acquisition module is used for acquiring at least one index data corresponding to a first menstrual person;
the model determining module is used for determining a corresponding grading model according to the data type corresponding to each index data in the at least one index data; wherein each data type corresponds to at least one scoring model;
the data attenuation module is used for performing weighted accumulation on each index data in a set time period according to time by using an attenuation function to obtain accumulated index data in the set time period;
the index scoring module is used for scoring the at least one accumulated index data based on the at least one scoring model and determining a corresponding index score for each index data;
a composite score module to determine a composite score for the first broker based on the at least one index score.
Optionally, the apparatus further comprises:
a model training module to train the at least one scoring model based on a training data set; wherein the training dataset includes a plurality of sample broker groups, each sample broker group including a plurality of sample brokers of known rank order.
Optionally, the model training module includes:
a score prediction unit to determine, based on the at least one scoring model, a predicted composite score for each of the sample brokers in the sample broker group;
a prediction ranking unit configured to determine a prediction ranking for a plurality of sample brokers in the sample broker group based on each of the sample broker corresponding prediction composite scores;
a loss determination unit for determining model losses based on the predicted rankings and known rankings corresponding to the sample broker group;
a parameter adjusting unit for adjusting the model parameters of the at least one scoring model based on the model loss.
Optionally, the loss determining unit is specifically configured to determine an accuracy factor of the at least one scoring model based on the number of sample brokers included in the sample broker group and a degree of matching of each of the sample brokers in the predicted ranking and the known ranking; determining the extremum accuracy rate based on the predicted composite score; determining the model loss based on a difference between the accuracy coefficient and a set coefficient and a difference between the extremum accuracy rate and a set accuracy rate.
Optionally, each sample broker respectively corresponds to a known evaluation result;
the loss determination unit is used for comparing the predicted comprehensive score with a preset score threshold value when determining the extreme value accuracy rate based on the predicted comprehensive score, and determining a predicted evaluation result of each sample broker in the sample broker group; determining the extremum accuracy rate based on a difference between the predicted evaluation result and the known evaluation result.
Optionally, the comprehensive scoring module is specifically configured to perform weighted summation on the at least one index score to obtain a comprehensive score of the first broker.
According to still another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium storing a computer program for executing the scoring model-based index data evaluation method according to any one of the embodiments.
According to still another aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory for storing the processor-executable instructions;
the processor is configured to read the executable instructions from the memory and execute the instructions to implement the scoring model-based index data evaluation method according to any of the above embodiments.
According to a further aspect of the embodiments of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of the scoring model-based index data evaluation method according to any one of the above.
Based on the index data evaluation method and device based on the scoring model, the storage medium, the electronic device and the computer program product, at least one index data corresponding to a first businessman is obtained; determining a corresponding scoring model according to the data type corresponding to each index data in the at least one index data; wherein each data type corresponds to at least one scoring model; weighting and accumulating each index data in a set time period according to time by using a decay function to obtain accumulated index data in the set time period; scoring the at least one accumulated index data based on the at least one scoring model, and determining a corresponding index score for each index data; determining a composite score for the first broker based on the at least one metric score; in the embodiment, at least one grading model is used for grading at least one index data, so that the comprehensive grading of the broker is automatically obtained, and the problem caused by artificial evaluation is avoided; in addition, due to the fact that at least one index score is integrated, the obtained integrated score can accurately achieve evaluation on the broker.
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in more detail embodiments of the present disclosure with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the principles of the disclosure and not to limit the disclosure. In the drawings, like reference numbers generally represent like parts or steps.
Fig. 1 is a schematic flowchart of a scoring model-based index data evaluation method according to an exemplary embodiment of the present disclosure.
Fig. 2 is a schematic flowchart illustrating training of at least one scoring model in the scoring model-based index data evaluation method according to an exemplary embodiment of the present disclosure.
Fig. 3 is a schematic flow chart of step 203 in the embodiment shown in fig. 2 of the present disclosure.
Fig. 4 is a schematic diagram of attenuation factors changing with time in an optional example of the score model-based index data evaluation method according to an exemplary embodiment of the disclosure.
Fig. 5 is a schematic structural diagram of an index data evaluation device based on a scoring model according to an exemplary embodiment of the present disclosure.
Fig. 6 is a block diagram of an electronic device provided in an exemplary embodiment of the present disclosure.
Detailed Description
Hereinafter, example embodiments according to the present disclosure will be described in detail with reference to the accompanying drawings. It is to be understood that the described embodiments are merely a subset of the embodiments of the present disclosure and not all embodiments of the present disclosure, with the understanding that the present disclosure is not limited to the example embodiments described herein.
It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present disclosure are used merely to distinguish one element from another, and are not intended to imply any particular technical meaning, nor is the necessary logical order between them.
It is also understood that in embodiments of the present disclosure, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.
It is also to be understood that any reference to any component, data, or structure in the embodiments of the disclosure, may be generally understood as one or more, unless explicitly defined otherwise or stated otherwise.
In addition, the term "and/or" in the present disclosure is only one kind of association relationship describing an associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the former and latter associated objects are in an "or" relationship. The data referred to in this disclosure may include unstructured data, such as text, images, video, etc., as well as structured data.
It should also be understood that the description of the various embodiments of the present disclosure emphasizes the differences between the various embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The disclosed embodiments may be applied to electronic devices such as terminal devices, computer systems, servers, etc., which are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set top boxes, programmable consumer electronics, network pcs, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above systems, and the like.
Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.
Exemplary method
Fig. 1 is a schematic flowchart of a scoring model-based index data evaluation method according to an exemplary embodiment of the present disclosure. The embodiment can be applied to an electronic device, as shown in fig. 1, and includes the following steps:
Alternatively, metric data for the first epoch person may be obtained from a data storage location (e.g., a database), and the metric data may include at least one of, for example, academic data: the broker's highest scholastic calendar; the working time data: length of time (days) of the job company; post-related certificate data: beijing Broker, national Broker, Beijing treatise, national treatise, etc.; the representation form of the index data may include text, numerical values, and the like.
And 104, determining a corresponding grading model according to the data type corresponding to each index data in the at least one index data.
Wherein each data type corresponds to at least one scoring model.
In one embodiment, the corresponding scoring model may be determined according to the data type corresponding to the metric data, for example, the data type may include but is not limited to: discrete data type, continuous data type, attenuation data type, conversion rate data type, evaluation data type and the like; the scoring model may be a deep neural network, a computing function, or other computing model that can convert data into scores, for example, the scoring model corresponding to the discrete data type is hierarchical mapping, the scoring model corresponding to the continuous data type is a mathematical function model (for example, a mathematical function such as a sum of quantity attenuation), the scoring model corresponding to the attenuation data type is a normal distribution function model, the scoring model corresponding to the conversion data type is a rate function module, and the scoring model corresponding to the evaluation data type is a complex function model (for example, a function such as a log, a sigmod function, or the like).
And 106, performing weighted accumulation on each index data in the set time period according to time by using the attenuation function to obtain the accumulated index data in the set time period.
Since the broker index data is not the index data of the broker at a certain time, but has a certain time accumulation to be determined, and the index data at different times are calculated with different weights, for example, the index data at a time point farther from the current time point plays a smaller role at the current time point, the present embodiment performs weighted accumulation on each index data through a decay function to obtain at least one accumulated index data of a first parent in a set time period, wherein the decay factor in the decay function can be set according to an actual application scenario, and the decay factor varies with time, for example, as shown in fig. 4, the abscissa is time (representing the month gap before the current time point, for example, -5, representing 5 months before the current time point), and the ordinate is the decay factor, the larger the attenuation factor is, the smaller the attenuation is, when the attenuation factor is 1, the current data is not attenuated, and when the attenuation factor is 0, the weight attenuation of the current data is 0; the index score determined by the accumulated index data obtained based on the weight value determined by the attenuation function can more accurately express the behavior of the first businessman in the set time period for evaluation, and the accuracy of the index score is improved.
And 108, grading at least one accumulated index data based on at least one grading model, and determining a corresponding index score for each index data.
The corresponding index data are respectively graded through the grading models, and an index score can be determined for each index data, wherein each grading model can correspond to at least one index data; for example, mapping is performed on at least one index data of a discrete type to obtain a corresponding index score.
A composite score for the first menstruum is determined based on the at least one indicator score, step 110.
In order to realize the evaluation of the first person, at least one index score can be accumulated (or weighted and summed, etc.) to determine a comprehensive score, and the comprehensive score is used as the evaluation score of the first person; generally, a higher composite score indicates a better rating for the first broker.
According to the index data evaluation method based on the scoring model provided by the embodiment of the disclosure, at least one index data corresponding to a first businessman is obtained; determining a corresponding scoring model according to the data type corresponding to each index data in the at least one index data; wherein each data type corresponds to at least one scoring model; scoring the at least one index data based on the at least one scoring model, and determining a corresponding index score for each index data; determining a composite score for the first broker based on the at least one metric score; in the embodiment, at least one grading model is used for grading at least one index data, so that the comprehensive grading of the broker is automatically obtained, and the problem caused by artificial evaluation is avoided; in addition, due to the fact that at least one index score is integrated, the obtained integrated score can accurately achieve evaluation on the broker.
Before some optional embodiments, before performing step 108, the method may further include:
at least one scoring model is trained based on a training data set.
Wherein the training dataset includes a plurality of sample broker groups, each sample broker group including a plurality of sample brokers of known rank order.
Optionally, each scoring model includes multiple parameters, and in order to improve scoring accuracy of the scoring model, in this embodiment, before scoring with the scoring model, the parameters in the scoring model are adjusted to make each scoring model more suitable for broker scoring, where supervision information trained by the scoring model is a known high ranking of human scores and/or a good-and-bad evaluation result, for example, a manager ranks three brokers of 1, 2, and 3 (respectively representing 3 sample brokers with different numbers) included in a sample broker group according to the good-and-bad degree: 2. 3, 1; in addition to the manager, the known score ranking may be determined based on the evaluation of the sample broker by the collaborator (cooperation broker) and/or the client, or the evaluation of the sample broker by the manager, the collaborator, and the client may be integrated to obtain the known score ranking.
As shown in fig. 2, in an alternative embodiment, training at least one scoring model based on a training data set may comprise the steps of:
In the embodiment, the scoring prediction is firstly performed on each sample broker in the sample broker group based on the scoring model, and each predicted composite score can be determined based on the predicted scoring values corresponding to a plurality of scoring models, and the prediction process is similar to the prediction process applied in practice.
After the predicted composite score of each sample broker in the sample broker group is determined, the plurality of sample brokers may be ranked according to the predicted composite score from large to small or from small to large, and optionally, the predicted composite scores may be ranked according to the predicted composite scores from large to small, so as to determine the predicted ranking corresponding to the sample broker group.
And 204, adjusting the model parameters of at least one scoring model based on the model loss.
Each scoring model in the at least one scoring model in this embodiment may be a deep neural network or a computation function, and after a model loss is determined, network parameters in the deep neural network may be adjusted in a back gradient propagation manner to implement training of the deep neural network; when the scoring model is a calculation function, parameters in a formula corresponding to the calculation function can be adjusted based on model loss, so that the model loss corresponding to the logical operation meets the training requirement (for example, the model loss is smaller than a set value).
The scoring accuracy of at least one scoring model can be determined based on the difference between the predicted ranking and the known ranking, and parameters in each scoring model are adjusted through model loss, so that each scoring model can score more accurately, the accuracy of the comprehensive scoring determined based on the scoring models is improved, the predicted comprehensive scoring determined based on the scoring models is closer to real evaluation, human participation is reduced, and meanwhile, the evaluation accuracy is improved.
As shown in fig. 3, based on the embodiment shown in fig. 2, step 203 may include the following steps:
Alternatively, the accuracy coefficient of the scoring model may be determined based on the following formula (1):
wherein k represents an accuracy factor and n represents the number of sample brokers included in the sample broker group; p represents the degree of match of each sample broker in the predicted and known ranks, which represents the sum of the number of sample brokers included after each sample broker in the predicted rank that are also sample brokers after the sample broker in the known rank, e.g., 4 sample brokers are represented by 1, 2, 3, 4, respectively (i.e., the sample broker group in this example includes 4 sample brokers), with the predicted rank: 2. 1, 4, 3, known ordering: 1. 2, 3 and 4; at this point, for sample broker 2: after 2 in the prediction ordering is 1, 4, 3, after 2 in the known ordering is 3, 4, taking the union to get 3 and 4, i.e. the number is 2; after 1 is 4, 3 in the prediction ordering, after 1 is 2, 3, 4 in the known ordering, and the union results in 3 and 4, i.e., the number is 2; 3 after 4 in the predicted ranking, no broker after 4 in the known ranking, the union is empty, i.e., the number is 0; there is no broker after 3 in the predicted ranking, 4 after 3 in the known ranking, the union is empty, i.e., the number is 0; therefore, in this example, p is 2+2+0+0 is 4, and n is 4, and in this case, the calculated accuracy coefficient is 2/3.
Alternatively, the extremum may be a number of evaluations that are both good brokers and also bad brokers, the extremum accuracy may represent a probability that a sample broker whose predicted composite score determined to be a good broker is also a good broker among the known evaluations, and a sample broker whose predicted composite score determined to be a bad broker is also a bad broker among the known evaluations.
Alternatively, the model loss is calculated as a sum of a difference between the accuracy coefficient and the setting coefficient and a difference between the extremum accuracy and the setting accuracy, or a weighted sum (a weight value may be set according to an actual application scenario) of a difference between the accuracy coefficient and the setting coefficient and a difference between the extremum accuracy and the setting accuracy.
In this embodiment, it is desirable that the accuracy coefficient approaches the setting coefficient, and the value of the setting coefficient may be set according to the actual situation, for example, the setting coefficient is set to 1, and at this time, it is desirable that the accuracy coefficient approaches 1; similarly, the value of the set accuracy rate can also be set according to the actual situation, for example, the set accuracy rate is set to 1, and at this time, the extremum accuracy rate is expected to approach 1; optionally, when the scoring model is a deep neural network, training of at least one scoring model may be implemented by using a back gradient propagation method based on model loss, and when the model loss is smaller than a set loss value, it indicates that the accuracy of at least one scoring model meets the requirement, at this time, the training may be stopped, and the trained at least one scoring model is used to score the broker index data.
Optionally, each sample broker respectively corresponds to a known evaluation result; wherein the known evaluation result indicates whether the sample broker is a good broker;
comparing the predicted comprehensive score with a preset score threshold value, and determining a predicted evaluation result of each sample broker in the sample broker group;
an extremum accuracy is determined based on a difference between the predicted evaluation result and the known evaluation result.
Optionally, the present embodiment determines whether each sample broker is a good broker by using a preset score threshold, for example, the predicted evaluation result of the sample broker whose predicted composite score is greater than or equal to the preset score threshold is determined as a good broker, and the predicted evaluation result of the sample broker whose predicted composite score is less than the preset score threshold is determined as a bad broker; alternatively, determining the extremum accuracy rate may be accomplished based on the following equation (2):
wherein jz represents the extremum accuracy; n represents the number of sample brokers included in the sample broker group; s1 represents the number of broker-like agents that are simultaneously represented as good brokers in the predicted and known outcomes of the evaluations; s2 represents the number of brokers alike represented as bad brokers in both the predicted and known evaluations; for example, in one example, 1, 2, 3, and 4 respectively represent 4 sample brokers (i.e., the sample broker group in this example includes 4 sample brokers), the known evaluation result represents that all 4 sample brokers are good brokers, and the predicted evaluation result represents that 3 sample brokers are good brokers and one sample broker is bad broker, where n is 4, s1 is 3, s2 is 0, and the extreme accuracy is calculated to be 75%.
Optionally, on the basis of the foregoing embodiment, the step 110 may include:
and performing weighted summation on at least one index score to obtain a comprehensive score of the first person.
In this embodiment, weighted summation is performed on at least one index data, a weight value corresponding to each index data may be preset, or each weight value may be adjusted while training a scoring model, and the weight value corresponding to each index data is more reasonable by adjustment, so that an obtained comprehensive score can reflect evaluation of a broker more.
Optionally, for index data of a discrete data type, mapping of different discrete values can be performed according to a certain service value, a service star level and service traction to obtain a mapping value; for index data of continuous data types, mapping from values to scores can be carried out through different algorithm functions; for example, for the index data of the house source rate of business, the scoring may be implemented by the following formula (3), in this case, the scoring model corresponding to the index data of the discrete data type may be a deep neural network implementing the operation of the formula (3), or a calculation function implementing the operation of the formula (3):
wherein score1 represents index score corresponding to the house source transaction rate, wiDenotes the attenuation factor, xdssRepresents the second hand volume, xdbsRepresenting the amount of second hand cross-shop deals, xdsrRepresenting the volume of the same-shop rental, xdbrRepresenting cross-store rental volume, ysRepresenting second hand maintenance source quantity, yrAnd s represents the full point value corresponding to the configured index.
For another example, the index data of the broker complaint data may be scored by the following formula (4), and in this case, the scoring model corresponding to the index data of the complaint data may be a deep neural network that implements the operation of the formula (4), or a calculation function that implements the operation of the formula (4):
score2 indicates the index score corresponding to the complaint data, months indicates the time (in months), level0 indicates the number of major complaints, level1 indicates the number of primary complaints, level2 indicates the number of secondary complaints, and level3 indicates the number of tertiary complaints.
For another example, the scoring may be realized by the following formula (5) for the index data of the broker volume, and in this case, the scoring model corresponding to the index data of the volume may be a deep neural network that realizes the operation of the formula (5), or a calculation function that realizes the operation of the formula (5):
wherein score3 represents the index score, w, corresponding to the volume of transactioniRepresenting the attenuation factor, sg representing the same composition volume, cg representing the cross composition volume, cs representing the cross store volume, cb representing the cross brand volume.
For another example, according to the confidence degrees of different business indexes on business behavior, the conversion rate data (such as the 1-minute response rate of IM, 400 call-in rate, cross-store company viewing by maintainers, and the like) is subjected to wilson conversion, so that the confidence degree of the conversion data meets business acceptance.
For another example, for the index data of the client evaluation of the broker, the scoring may be implemented by the following formula (6), in this case, the scoring model corresponding to the index data of the client evaluation may be a deep neural network implementing the operation of the formula (6), or a calculation function implementing the operation of the formula (6):
score4＝Scorebasic+Scorecomment+Scorenum+Scorestableformula (6)
Wherein score4 represents the index score corresponding to the customer evaluation; scorebasicThe basic score is represented, which can be 30 in the example, and represents that the customer evaluation exists, namely the basic score exists; scorecommentThe star rating score is expressed as a star rating score,
wherein x represents the accumulation of the evaluation star rating (which may be a normalized value); scorenumScore of evaluation amount, Scorenum＝log3num, wherein num represents the number of evaluations; scorestableShows a stable score (for suppressing cheating such as brushing),
var represents the standard deviation of the evaluation star rating.
In some embodiments, the various metric data may be further divided into 5 dimensions, and the 5 dimensions may include: basic quality, service quality, platform cooperation, platform participation and industry influence; the numerical data is represented by numerical values, and the enumeration data can be encoded into vector representation. Optionally, in some examples, the base prime dimension includes, but is not limited to: a) learning a calendar: the broker's highest scholastic calendar; b) the working time is as follows: length of time (days) of the job company; c) the post correlation certificate: beijing Broker, national Broker, Beijing treatise, national treatise, etc.; d) b, examination in a bosch: a broker's annual check of learning.
Quality of service dimensions include, but are not limited to: a) customer evaluation: customer service evaluation of brokers; b) complaint volume and grade: customer complaints and complaint ratings (major, primary, secondary, tertiary, etc.) received by the broker; c) and (4) house maintenance: the broker maintains the amount of the house resources belonging to the good house; d) and (3) viewing through a closed loop: the amount of views made by the broker with both customer and broker evaluations; e) the maintainer accompanies across shops: the maintainers can watch the watch while the maintainers are in a shop-crossing state and also take part in the watch-taking amount of accompanying watching; f)400 call completing rate: 400 phone turn-on ratio; g) 1 minute response rate of IM: the proportion of IM messages that respond within 1 minute; h) and (3) commercial opportunity conversion: the 7-day business opportunity of the broker goes to commission and the 15-day business opportunity goes to watch.
Platform collaboration dimensions include, but are not limited to: a) credit points are: loyalty points maintained by the broker; b) the passing rate of the verification is as follows: the proportion of the broker house source task for completing the truth test; c) the other stores in the house sell: the volume of the brokerage-maintained house source in other stores; d) the guest recommends a deal: the amount of the deals the broker maintains that the customer recommends to others; e) and (3) cooperation and transaction: brokerage as the partner's volume; f) broker evaluation: broker evaluation of broker.
Platform engagement dimensions include, but are not limited to: a) and (5) carrying out a co-audition group: the broker is used as a member of the accompanying party; b) reporting amount of violation: the amount of violation successfully reported by the broker; c) contribution of the building dictionary: building dictionary contribution made by the broker; d) the red star task: the amount and proportion of the red star tasks served by the platform completed by the broker.
Industry impact dimensions include, but are not limited to: a) teaching: the broker is used as a brother amount of master bands; b) value sighting rod: the broker acts as a value sighting bar (alternatively, 0,1 may be used to indicate whether or not).
Optionally, the dimension type may further include a score card model dimension, where the score card model corresponds to a label (label) value, the label value is 0 or 1, the label value corresponding to the broker whose score is greater than the score threshold may be set to 1, and the label value corresponding to the broker whose score is less than the score threshold may be set to 0, where the score in the score card model may be obtained according to human score or other scoring methods, and the method for obtaining the score is not limited in this embodiment.
The reasonability of the evaluation model is comprehensively measured in the aspects of completeness, accuracy and stability through the index system; wherein, completeness represents complete exhaustion without omission and mutual independence without overlapping; the accuracy indicates that the individual score accords with the actual performance, the overall result accords with the common sense of business and the group difference accords with the common sense of business; stability indicates that the score change is consistent with the expected solution result.
Any one of the score model-based index data evaluation methods provided by the embodiments of the present disclosure may be performed by any suitable device having data processing capabilities, including but not limited to: terminal equipment, a server and the like. Alternatively, any one of the score model-based index data evaluation methods provided by the embodiments of the present disclosure may be executed by a processor, for example, the processor may execute any one of the score model-based index data evaluation methods mentioned by the embodiments of the present disclosure by calling a corresponding instruction stored in a memory. And will not be described in detail below.
Exemplary devices
Fig. 5 is a schematic structural diagram of an index data evaluation device based on a scoring model according to an exemplary embodiment of the present disclosure. As shown in fig. 5, the apparatus provided in this embodiment includes:
the data obtaining module 51 is configured to obtain at least one index data corresponding to a first person.
And the model determining module 52 is configured to determine a corresponding scoring model according to a data type corresponding to each index data in the at least one index data.
Wherein each data type corresponds to at least one scoring model.
And the data attenuation module 53 is configured to perform weighted accumulation on each index data in the set time period according to time by using an attenuation function to obtain the accumulated index data in the set time period.
And the index scoring module 54 is configured to perform scoring processing on the at least one accumulated index data based on the at least one scoring model, and determine a corresponding index score for each index data.
A composite score module 55 for determining a composite score for the first menstruum based on the at least one indicator score.
The index data evaluation device based on the scoring model provided by the embodiment of the disclosure obtains at least one index data corresponding to a first person; determining a corresponding scoring model according to the data type corresponding to each index data in the at least one index data; wherein each data type corresponds to at least one scoring model; scoring the at least one index data based on the at least one scoring model, and determining a corresponding index score for each index data; determining a composite score for the first broker based on the at least one metric score; in the embodiment, at least one grading model is used for grading at least one index data, so that the comprehensive grading of the broker is automatically obtained, and the problem caused by artificial evaluation is avoided; in addition, due to the fact that at least one index score is integrated, the obtained integrated score can accurately achieve evaluation on the broker.
Optionally, the apparatus provided in this embodiment further includes:
a model training module for training at least one scoring model based on a training data set.
Wherein the training dataset includes a plurality of sample broker groups, each sample broker group including a plurality of sample brokers of known rank order.
Optionally, the model training module comprises:
the score prediction unit is used for determining a corresponding prediction comprehensive score of each sample broker in the sample broker group based on at least one score model;
the prediction ranking unit is used for determining the prediction ranking of a plurality of sample brokers in the sample broker group based on the corresponding prediction comprehensive score of each sample broker;
a loss determination unit for determining model losses based on the predicted rankings and the known rankings corresponding to the sample broker groups;
and the parameter adjusting unit is used for adjusting the model parameters of at least one grading model based on the model loss.
Optionally, the loss determining unit is specifically configured to determine an accuracy factor of the at least one scoring model based on the number of sample brokers included in the sample broker group and a degree of matching of each sample broker in the predicted ranking and the known ranking; determining an extremum accuracy rate based on the predicted composite score; the model loss is determined based on the difference between the accuracy coefficient and the set coefficient and the difference between the extremum accuracy rate and the set accuracy rate.
Optionally, each sample broker respectively corresponds to a known evaluation result; wherein the known evaluation result indicates whether the sample broker is a good broker;
the loss determining unit is used for comparing the predicted comprehensive score with a preset score threshold value when determining the accuracy of the extreme value based on the predicted comprehensive score, and determining the predicted evaluation result of each sample broker in the sample broker group; an extremum accuracy is determined based on a difference between the predicted evaluation result and the known evaluation result.
Optionally, the comprehensive scoring module 55 is specifically configured to perform weighted summation on at least one index score to obtain a comprehensive score of the first broker.
Exemplary electronic device
Next, an electronic apparatus according to an embodiment of the present disclosure is described with reference to fig. 6. The electronic device may be either or both of the first device 100 and the second device 200, or a stand-alone device separate from them that may communicate with the first device and the second device to receive the collected input signals therefrom.
FIG. 6 illustrates a block diagram of an electronic device in accordance with an embodiment of the disclosure.
As shown in fig. 6, the electronic device 60 includes one or more processors 61 and a memory 62.
The processor 61 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 60 to perform desired functions.
The memory 62 may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by processor 61 to implement the scoring model-based metric data evaluation methods of the various embodiments of the present disclosure described above and/or other desired functions. Various contents such as an input signal, a signal component, a noise component, etc. may also be stored in the computer-readable storage medium.
In one example, the electronic device 60 may further include: an input device 63 and an output device 64, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
For example, when the electronic device is the first device 100 or the second device 200, the input device 63 may be a microphone or a microphone array as described above for capturing an input signal of a sound source. When the electronic device is a stand-alone device, the input means 63 may be a communication network connector for receiving the acquired input signals from the first device 100 and the second device 200.
The input device 63 may also include, for example, a keyboard, a mouse, and the like.
The output device 64 may output various information including the determined distance information, direction information, and the like to the outside. The output devices 64 may include, for example, a display, speakers, a printer, and a communication network and its connected remote output devices, among others.
Of course, for simplicity, only some of the components of the electronic device 60 relevant to the present disclosure are shown in fig. 6, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device 60 may include any other suitable components depending on the particular application.
Exemplary computer program product and computer-readable storage Medium
In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in a scoring model-based metric data evaluation method according to various embodiments of the present disclosure described in the "exemplary methods" section above of this specification.
The computer program product may write program code for carrying out operations for embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.
Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in the scoring model-based index data evaluation method according to various embodiments of the present disclosure described in the above-mentioned "exemplary methods" section of this specification.
The computer-readable storage medium may take any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The block diagrams of devices, apparatuses, systems referred to in this disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably therewith. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
It is also noted that in the devices, apparatuses, and methods of the present disclosure, each component or step can be decomposed and/or recombined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (10)
1. A scoring model-based index data evaluation method is characterized by comprising the following steps:
obtaining at least one index data corresponding to a first menstrual person;
determining a corresponding scoring model according to the data type corresponding to each index data in the at least one index data; wherein each data type corresponds to at least one scoring model;
weighting and accumulating each index data in a set time period according to time by using a decay function to obtain accumulated index data in the set time period;
scoring the at least one accumulated index data based on the at least one scoring model, and determining a corresponding index score for each index data;
determining a composite score for the first broker based on the at least one metric score.
2. The method of claim 1, further comprising, prior to scoring the at least one cumulative metric data based on the at least one scoring model, determining a corresponding metric score for each of the metric data:
training the at least one scoring model based on a training data set; wherein the training dataset includes a plurality of sample broker groups, each sample broker group including a plurality of sample brokers of known rank order.
3. The method of claim 2, wherein the training the at least one scoring model based on a training data set comprises:
determining, based on the at least one scoring model, a predicted composite score for each of the sample brokers in the sample broker group;
determining a predicted ranking for a plurality of sample brokers in the sample broker group based on each of the sample broker corresponding predicted composite scores;
determining a model loss based on the predicted rankings and known rankings corresponding to the sample broker group;
adjusting model parameters of the at least one scoring model based on the model loss.
4. The method of claim 3, wherein determining a model loss based on the predicted ranking and a known ranking corresponding to the sample broker group comprises:
determining an accuracy factor for the at least one scoring model based on a number of sample brokers included in the set of sample brokers and a degree of match of each of the sample brokers in the predicted ranking and the known ranking;
determining the extremum accuracy rate based on the predicted composite score;
determining the model loss based on a difference between the accuracy coefficient and a set coefficient and a difference between the extremum accuracy rate and a set accuracy rate.
5. The method of claim 4, wherein each of the sample brokers respectively corresponds to a known evaluation result;
said determining said extremum accuracy rate based on said predicted composite score comprises:
determining a predicted evaluation result of each sample broker in the sample broker group based on a comparison of the predicted composite score to a preset score threshold;
determining the extremum accuracy rate based on a difference between the predicted evaluation result and the known evaluation result.
6. The method of any of claims 1-5, wherein determining a composite score for the first broker based on the at least one metric score comprises:
performing a weighted summation of the at least one metric score to obtain a composite score for the first broker.
7. An index data evaluation device based on a scoring model, comprising:
the data acquisition module is used for acquiring at least one index data corresponding to a first menstrual person;
the model determining module is used for determining a corresponding grading model according to the data type corresponding to each index data in the at least one index data; wherein each data type corresponds to at least one scoring model;
the data attenuation module is used for performing weighted accumulation on each index data in a set time period according to time by using an attenuation function to obtain accumulated index data in the set time period;
the index scoring module is used for scoring the at least one accumulated index data based on the at least one scoring model and determining a corresponding index score for each index data;
a composite score module to determine a composite score for the first broker based on the at least one index score.
8. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the scoring model-based index data evaluation method according to any one of claims 1 to 6.
9. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for reading the executable instructions from the memory and executing the instructions to realize the scoring model-based index data evaluation method of any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the scoring model-based index data evaluation method according to any one of claims 1-6.
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