CN113609278B - Data processing method, device, equipment and readable storage medium - Google Patents
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Abstract
The embodiment of the application provides a data processing method, a device, equipment and a readable storage medium, which can be applied to the field of artificial intelligence or the field of finance, and the method comprises the following steps: receiving a to-be-searched problem sent by a requesting party, acquiring a search score of each preset knowledge point, acquiring a service score of each knowledge point according to service information of each knowledge point, acquiring a score result of each knowledge point according to the search score and the service score of each knowledge point, and acquiring a knowledge point sequence according to the score result of each knowledge point. The method combines semantic dimension and service dimension, and the obtained score result of each knowledge point accords with the retrieval intention of the request party for sending the problem to be retrieved, and improves the accuracy of the score result, thereby improving the accuracy of the result knowledge point.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a data processing method, apparatus, device, and readable storage medium.
Background
At present, each large bank system establishes a counter knowledge base system, and knowledge points (namely result knowledge points) for solving the problems are fed back to solve the problems for the teller by responding to the problems of the teller system, so that the efficiency of handling the business by the bank teller is improved. How to improve the accuracy of the result knowledge points is a problem to be solved urgently at present.
Disclosure of Invention
The application provides a data processing method, a device, equipment and a readable storage medium, which are used for improving the accuracy of a result knowledge point, and are as follows:
receiving a to-be-searched problem sent by a requesting party, and acquiring a search score of each preset knowledge point, wherein the search score of a target knowledge point indicates the probability that the target knowledge point is used for solving the to-be-searched problem, and the target knowledge point is any knowledge point;
acquiring service scores of each knowledge point according to service information of each knowledge point, wherein the service information of the target knowledge point comprises the number of target services handled by the requesting party, and the target services are services to which the target knowledge point belongs;
obtaining a score result of each knowledge point according to the search score and the service score of each knowledge point;
according to the scoring result of each knowledge point, a knowledge point sequence is obtained, the knowledge point sequence comprises N result knowledge points which are ordered from big to small according to the scoring result, the result knowledge points are knowledge points meeting the preset result condition, the result condition comprises that the scoring result is not smaller than a first preset threshold, and N is the preset result number.
Optionally, receiving the to-be-retrieved problem sent by the requester, and obtaining a search score of the target knowledge point includes:
receiving the to-be-retrieved problem sent by the requester, and acquiring the similarity between the to-be-retrieved problem and a target problem, wherein the target problem comprises at least one preset standard problem corresponding to the target knowledge point;
and determining the search score of the target knowledge point according to the similarity, wherein the similarity and the search score of the target knowledge point form a positive correlation.
Optionally, the service information of the target knowledge point further includes a target proportion, where the target proportion is a ratio of a first transaction amount to a second transaction amount, the first transaction amount is a number of the target services handled by the requesting party, and the second transaction amount is a total number of all services handled by the requesting party.
Optionally, the process of obtaining the first transaction amount includes:
acquiring the number of transacted target services of the requesting party in each preset time period in a time period sequence, and taking the number of transacted target services as target transacted amount of each preset time period; the time period sequence comprises a plurality of time periods which are ordered according to time sequence;
acquiring the weight of each time period according to a preset weight corresponding relation, wherein the weight corresponding relation comprises the corresponding relation between each time period and each preset weight; wherein the earlier the time is, the smaller the corresponding weight of the time period is;
and adding the target transaction amount of each time period according to the weight of the time period to obtain the first transaction amount.
Optionally, obtaining the service score of the target knowledge point according to the service information of the target knowledge point includes:
acquiring service scores of the target knowledge points according to the service information of the target knowledge points;
wherein the business score of the target knowledge point is inversely related to the first transaction amount and the business score of the target knowledge point is inversely related to the target proportion.
Optionally, obtaining the score result of the target knowledge point according to the search score and the service score of the target knowledge point includes:
multiplying the search score and the business score of the target knowledge point to obtain a score result of the target knowledge point;
or, the search score and the business score of the target knowledge point are added in a weighted mode according to the preset score weight, and the score result of the target knowledge point is obtained.
Optionally, the preset result condition further includes: the search score is not less than a second preset threshold and/or the traffic score is not less than a third preset threshold.
A data processing apparatus comprising:
the first score acquisition unit is used for receiving the to-be-searched problem sent by the requesting party, acquiring the search score of each preset knowledge point, and indicating the probability that the target knowledge point is used for solving the to-be-searched problem by the search score of the target knowledge point, wherein the target knowledge point is any knowledge point;
the second score acquisition unit is used for acquiring the service score of each knowledge point according to the service information of each knowledge point, wherein the service information of the target knowledge point comprises the number of target service transacted by the requesting party, and the target service is the service to which the target knowledge point belongs;
the third score acquisition unit is used for acquiring a score result of each knowledge point according to the search score and the service score of each knowledge point;
the result acquisition unit is used for acquiring a knowledge point sequence according to the score result of each knowledge point, wherein the knowledge point sequence comprises N result knowledge points which are ordered from big to small according to the score result, the result knowledge points are knowledge points meeting the preset result condition, the result condition comprises that the score result is not smaller than a first preset threshold, and the N is the preset result number.
A data processing apparatus comprising: a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement each step of the data processing method.
A readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a data processing method.
As can be seen from the above technical solutions, the data processing method, apparatus, device, and readable storage medium provided in the embodiments of the present application receive a problem to be searched sent by a requester, obtain a search score of each preset knowledge point, and obtain a service score of each knowledge point according to service information of each knowledge point. And obtaining a score result of each knowledge point according to the search score and the service score of each knowledge point. According to the scoring result of each knowledge point, a knowledge point sequence is obtained, the knowledge point sequence comprises N result knowledge points which are ordered from big to small according to the scoring result, the result knowledge points are knowledge points meeting the preset result condition, the result condition comprises that the scoring result is not smaller than a first preset threshold, and N is the preset result quantity. The target knowledge point search score indicates the probability that the target knowledge point is used for solving the problem to be searched, namely the score of the target knowledge point in the semantic dimension, and the service score is obtained according to the number of services to which the target knowledge point belongs in the service dimension handled by the requester, namely the score of the target knowledge point in the service dimension handled by the requester, so that the method combines the semantic dimension and the service dimension, and the obtained score result of each knowledge point accords with the search intention of the problem to be searched sent by the requester, and improves the accuracy of the score result. And the knowledge point sequence comprises knowledge points with N score results which are not less than a first preset threshold value and are sequenced from big to small according to the score results, so that the accuracy of the result knowledge points is improved, and further, the service scores are obtained according to the number of services of the target processing knowledge points of the requesting party, so that personalized recommendation of the result knowledge points is realized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a specific implementation of a data processing method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a data processing method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a data processing device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The data processing method provided by the embodiment is particularly applied to, but not limited to, a counter knowledge base system, wherein the counter knowledge base system is connected with a teller system and is used for receiving a problem sent by the teller system, selecting a result knowledge point for solving the problem from a pre-built counter knowledge base and feeding back the result knowledge point to the teller system. It should be noted that the counter knowledge base includes a plurality of correspondence between preset standard questions and preset knowledge points. In the prior art, when a teller requests a result knowledge point from a counter knowledge base system through a teller system, the counter knowledge base system feeds back the same result knowledge point of different requesters corresponding to the same problem, that is, the result knowledge point is acquired without referring to the personalized requirements of different requesters, so that the result knowledge point is inaccurate, and secondary screening of the requesters is required. Therefore, the data processing method provided by the embodiment combines the service proficiency of the requesting party to realize personalized feedback result knowledge points.
It should be noted that the data processing method, apparatus, device and readable storage medium provided by the present invention may be used in the field of artificial intelligence or the field of finance. The foregoing is merely exemplary, and the application fields of the data processing method, apparatus, device and readable storage medium provided by the present invention are not limited.
Fig. 1 is a specific implementation method of a data processing method provided in an embodiment of the present application, where, as shown in fig. 1, the method includes:
s101, receiving the to-be-searched problem sent by the requester, and acquiring a search score of each preset knowledge point.
In this embodiment, the preset knowledge points are knowledge points included in the counter knowledge base, and it is to be noted that the counter knowledge base includes a plurality of preset standard questions and corresponding relations between the preset knowledge points. The search score for the knowledge point indicates a probability that the knowledge point is a search result of the question to be retrieved.
In this embodiment, the process of obtaining the search score of the target knowledge point includes:
a1, receiving a to-be-searched problem sent by a requester, and obtaining the similarity between the to-be-searched problem and a target problem.
Wherein the target questions comprise at least one preset standard question corresponding to the target knowledge points.
A2, determining the search score of the target knowledge point according to the similarity.
Wherein the similarity is positively correlated with the search score of the target knowledge point. That is, the higher the similarity between the target problem and the problem to be searched, the higher the search score of the target knowledge point.
It should be noted that, if the target knowledge point corresponds to a plurality of standard questions, the embodiment obtains the similarity between each standard question and the question to be searched, and selects the maximum value.
It should be noted that, the similarity between the problem to be searched and the target problem includes semantic similarity, and the method for obtaining the semantic similarity refers to the prior art.
S102, acquiring the business handling capacity of each knowledge point.
In this embodiment, the service handling amount of each knowledge point is the number of services to which the request party handles the knowledge point in a preset time.
Taking the target knowledge points as an example, acquiring the service of the target knowledge points as target service according to the preset service corresponding relation, wherein the service handling capacity of the target knowledge points is the number of target services handled by the requesting party in the preset time
Specifically, the method for acquiring the business transaction amount (called a first transaction amount) of the target knowledge point includes:
b1, acquiring the number of target business handling in each preset time period in the time period sequence by the requester as the target handling amount of each preset time period.
Wherein the time period sequence comprises a plurality of time periods ordered in time sequence. For example, taking a period of one week (seven days) as an example of the preset period of time, the period of time sequence is { week n, week n-1, week …, week 2, week 1 }, wherein week 1 is the current week, and each week in the period of time sequence is ordered in time sequence.
And B2, acquiring the weight of each time period according to a preset weight corresponding relation.
The weight corresponding relation comprises the corresponding relation between each time period and each preset weight; wherein the earlier the time is, the smaller the corresponding weight of the time period is.
In the above example, the preset weight q1=1 corresponding to week 1, and the preset weights between week 1 and week n are decreased by 0.1 according to the preset weight difference between two adjacent weeks. It should be noted that, the weight correspondence is obtained according to the memory rule, and it can be understood that the longer the interval time of service transaction is, the greater the probability of forgetting the service is.
And B3, adding the target transaction amount of each time period according to the weight of the time period to obtain a first transaction amount.
In the above example, if the weight corresponding to the i th week of the time period is pi and the target transaction amount is xi, the first transaction amount is equal to
In this embodiment, the service transaction amount of each knowledge point is obtained in S21 to S23, and if the services to which the knowledge points belong are the same, repeated obtaining is not required.
S103, obtaining the total number of all the services handled by the requesting party.
In this embodiment, the total number of all services handled by the requester is equal to the total number of all services handled by the requester.
In this embodiment, the method for obtaining the number of tasks handled by the requesting party refers to S2, that is, adding the service handling amounts of knowledge points belonging to each service to obtain the total number of all services handled by the requesting party.
S104, obtaining the service score of each knowledge point according to the service information of each knowledge point.
In this implementation, the service information of each knowledge point includes: the business handling amount and the business handling proportion of the knowledge points. The service handling proportion of the knowledge point is the ratio of the service handling quantity of the knowledge point to the total quantity of all services handled by the requesting party.
Taking the target knowledge point as an example, the service information of the target knowledge point includes: the number of target business processed by the request party (recorded as a first processing amount) and a target proportion, wherein the target proportion is the ratio of the first processing amount to a second processing amount, and the second processing amount is the total number of all business processed by the request party.
And obtaining the business score of the target knowledge point according to the first transacted quantity and the target proportion, wherein the business score of the target knowledge point is inversely related to the first transacted quantity, and the business score of the target knowledge point is inversely related to the target proportion. That is, the greater the first transaction amount, the lower the business score for the target knowledge point and the higher the target proportion, the lower the business score for the target knowledge point.
S105, obtaining a score result of each knowledge point according to the search score and the service score of each knowledge point.
In this embodiment, the score of each knowledge point is proportional to the search score and proportional to the business score. That is, the higher the search score, the higher the score result of the knowledge point, and the higher the business score, the higher the score result of the knowledge point.
Specifically, the search score and the service score of each knowledge point are multiplied to obtain a score result of each knowledge point. For example, the search score and the business score of the target knowledge point are multiplied to obtain a score result of the target knowledge point.
S106, judging whether the knowledge points meet preset result conditions according to the score result of each knowledge point. If yes, the knowledge points are used as the result knowledge points.
In this embodiment, the result condition includes that the score result is not less than a first preset threshold, and the search score is not less than a second preset threshold.
And S107, ordering the result knowledge points with the search scores ranked at the top N according to the search scores to obtain a knowledge point sequence.
It can be understood that N is a preset number of results, and the knowledge point sequence includes a knowledge point sequence including N result knowledge points ordered from big to small according to the score result.
According to the data processing method, the score result of each knowledge point is obtained according to the search score and the service score of each knowledge point, wherein the target knowledge point search score indicates the probability that the target knowledge point is used for solving the problem to be searched, namely the score of the target knowledge point in the semantic dimension, and the service score is obtained according to the number of services of the target knowledge point handled by the requester, namely the score of the target knowledge point in the service dimension handled by the requester. And the knowledge point sequence comprises knowledge points with N score results which are not less than a first preset threshold value and are sequenced from big to small according to the score results, so that the accuracy of the result knowledge points is improved, and further, the service scores are obtained according to the number of services of the target processing knowledge points of the requesting party, so that personalized recommendation of the result knowledge points is realized.
Further, the service information of each knowledge point includes: the business handling amount and the business handling proportion of the knowledge points. When the business handling amount (first handling amount) of any knowledge point is obtained, the target handling amount of each time period is added according to the weight of the time period, and the probability of low proficiency is larger for businesses with front handling time and the probability of high proficiency is larger for businesses with larger handling amount, so that the obtained business handling amount can accurately indicate the proficiency of a requesting party to the business, namely the probability of high proficiency of the requesting party to the target task is larger for the larger business handling amount. Also because the target ratio is the ratio of the first transacted amount to the second transacted amount, it is understood that the greater the target ratio, the greater the probability that the request will be of high proficiency at the target task. Thus, the business score of the target knowledge point is inversely related to the first transaction amount and the business score of the target knowledge point is inversely related to the target proportion. That is, the higher the business score obtained by the method, the higher the probability that the proficiency of the requesting party to the target task is low.
It should be noted that, the flow shown in fig. 1 is only a specific implementation manner of one data processing method provided in the embodiment of the present application, and the present application further includes other specific implementation manners, for example, an optional another specific method for obtaining a score result of each knowledge point according to a search score and a service score of each knowledge point includes: and carrying out weighted addition on the search score and the business score of the knowledge point according to the preset score weight to obtain a score result of the knowledge point. As another example, the resulting conditions further include: the service score is not less than a third preset threshold.
Summarizing the embodiment of the present application into a flow shown in fig. 2, as shown in fig. 2, the method includes:
s201, receiving the to-be-searched problem sent by the requester, and acquiring a search score of each preset knowledge point.
In this embodiment, the preset knowledge points are knowledge points included in the counter knowledge base, and it is to be noted that the counter knowledge base includes a plurality of preset standard questions and corresponding relations between the preset knowledge points. The search score of a target knowledge point indicates a probability that the target knowledge point is used to solve the problem to be retrieved.
It should be noted that, the method for obtaining the search score of each preset knowledge point includes a plurality of methods, and an optional method for obtaining the search score is described in the above embodiments.
S202, obtaining the service score of each knowledge point according to the service information of each knowledge point.
In this embodiment, the service information of the target knowledge point includes the number of target services handled by the requesting party, where the target services are services to which the target knowledge point belongs, and the target knowledge point is any knowledge point.
S203, obtaining a score result of each knowledge point according to the search score and the service score of each knowledge point.
In this embodiment, the search score and the search result of each knowledge point are positively correlated, and the service score and the search result are positively correlated, that is, the higher the search score, the higher the score result of the knowledge point, and the higher the service score, the higher the score result of the knowledge point.
An alternative specific way of obtaining the scoring result for each knowledge point may be seen in the above embodiments.
S204, obtaining a knowledge point sequence according to the score result of each knowledge point.
In this embodiment, the knowledge point sequence includes N result knowledge points ordered from big to small according to the scoring result, the result knowledge points are knowledge points satisfying a preset result condition, the result condition includes that the scoring result is not less than a first preset threshold, and N is the preset number of results
According to the technical scheme, the data processing result is obtained according to the search score and the service score of each knowledge point, wherein the target knowledge point search score indicates the probability that the target knowledge point is used for solving the problem to be searched, namely the score of the target knowledge point in the semantic dimension, and the service score is obtained according to the number of services to which the target knowledge point belongs by the request party, namely the score of the target knowledge point in the service dimension handled by the request party. And the knowledge point sequence comprises knowledge points with N score results which are not less than a first preset threshold value and are sequenced from big to small according to the score results, so that the accuracy of the result knowledge points is improved, and further, the service scores are obtained according to the number of services of the target processing knowledge points of the requesting party, so that personalized recommendation of the result knowledge points is realized.
Fig. 3 shows a schematic structural diagram of a data processing apparatus according to an embodiment of the present application, where, as shown in fig. 3, the apparatus may include:
a first score obtaining unit 301, configured to receive a to-be-retrieved problem sent by a requester, obtain a search score of each preset knowledge point, where a search score of a target knowledge point indicates a probability that the target knowledge point is used to solve the to-be-retrieved problem, and the target knowledge point is any knowledge point;
a second score obtaining unit 302, configured to obtain a service score of each knowledge point according to service information of each knowledge point, where the service information of the target knowledge point includes the number of target services handled by the requesting party, and the target service is a service to which the target knowledge point belongs;
a third score obtaining unit 303, configured to obtain a score result of each knowledge point according to the search score and the service score of each knowledge point;
the result obtaining unit 304 is configured to obtain a knowledge point sequence according to the score result of each knowledge point, where the knowledge point sequence includes N result knowledge points ordered from big to small according to the score result, the result knowledge points are knowledge points that satisfy a preset result condition, the result condition includes that the score result is not less than a first preset threshold, and N is a preset result number.
Optionally, the first score obtaining unit is configured to receive the to-be-retrieved problem sent by the requester, obtain a search score of the target knowledge point, and include: the first score acquisition unit is specifically configured to:
receiving the to-be-retrieved problem sent by the requester, and acquiring the similarity between the to-be-retrieved problem and a target problem, wherein the target problem comprises at least one preset standard problem corresponding to the target knowledge point;
and determining the search score of the target knowledge point according to the similarity, wherein the similarity and the search score of the target knowledge point form a positive correlation.
Optionally, the service information of the target knowledge point further includes a target proportion, where the target proportion is a ratio of a first transaction amount to a second transaction amount, the first transaction amount is a number of the target services handled by the requesting party, and the second transaction amount is a total number of all services handled by the requesting party.
Optionally, the second score obtaining unit is further configured to obtain the first transaction amount, including: the second score acquisition unit is specifically configured to:
acquiring the number of transacted target services of the requesting party in each preset time period in a time period sequence, and taking the number of transacted target services as target transacted amount of each preset time period; the time period sequence comprises a plurality of time periods which are ordered according to time sequence;
acquiring the weight of each time period according to a preset weight corresponding relation, wherein the weight corresponding relation comprises the corresponding relation between each time period and each preset weight; wherein the earlier the time is, the smaller the corresponding weight of the time period is;
and adding the target transaction amount of each time period according to the weight of the time period to obtain the first transaction amount.
Optionally, the second score obtaining unit obtains a service score of the target knowledge point according to the service information of the target knowledge point, including: the second score acquisition unit is specifically configured to:
acquiring service scores of the target knowledge points according to the service information of the target knowledge points;
wherein the business score of the target knowledge point is inversely related to the first transaction amount and the business score of the target knowledge point is inversely related to the target proportion.
Optionally, the third score obtaining unit is configured to obtain a score result of the target knowledge point according to the search score and the service score of the target knowledge point, and includes: the third score acquisition unit is specifically configured to:
multiplying the search score and the business score of the target knowledge point to obtain a score result of the target knowledge point;
or, the search score and the business score of the target knowledge point are added in a weighted mode according to the preset score weight, and the score result of the target knowledge point is obtained.
Optionally, the preset result condition further includes: the search score is not less than a second preset threshold and/or the traffic score is not less than a third preset threshold.
Fig. 4 shows a schematic structural diagram of the data processing apparatus, which may include: at least one processor 401, at least one communication interface 402, at least one memory 403, and at least one communication bus 404;
in the embodiment of the present application, the number of the processor 401, the communication interface 402, the memory 403 and the communication bus 404 is at least one, and the processor 401, the communication interface 402 and the memory 403 complete communication with each other through the communication bus 404;
processor 401 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention, etc.;
the memory 403 may include a high-speed RAM memory, and may further include a non-volatile memory (non-volatile memory), etc., such as at least one magnetic disk memory;
the memory stores a program, and the processor may execute the program stored in the memory to implement each step of a data processing method provided in the embodiment of the present application, as follows:
receiving a to-be-searched problem sent by a requesting party, and acquiring a search score of each preset knowledge point, wherein the search score of a target knowledge point indicates the probability that the target knowledge point is used for solving the to-be-searched problem, and the target knowledge point is any knowledge point;
acquiring service scores of each knowledge point according to service information of each knowledge point, wherein the service information of the target knowledge point comprises the number of target services handled by the requesting party, and the target services are services to which the target knowledge point belongs;
obtaining a score result of each knowledge point according to the search score and the service score of each knowledge point;
according to the scoring result of each knowledge point, a knowledge point sequence is obtained, the knowledge point sequence comprises N result knowledge points which are ordered from big to small according to the scoring result, the result knowledge points are knowledge points meeting the preset result condition, the result condition comprises that the scoring result is not smaller than a first preset threshold, and N is the preset result number.
Optionally, receiving the to-be-retrieved problem sent by the requester, and obtaining a search score of the target knowledge point includes:
receiving the to-be-retrieved problem sent by the requester, and acquiring the similarity between the to-be-retrieved problem and a target problem, wherein the target problem comprises at least one preset standard problem corresponding to the target knowledge point;
and determining the search score of the target knowledge point according to the similarity, wherein the similarity and the search score of the target knowledge point form a positive correlation.
Optionally, the service information of the target knowledge point further includes a target proportion, where the target proportion is a ratio of a first transaction amount to a second transaction amount, the first transaction amount is a number of the target services handled by the requesting party, and the second transaction amount is a total number of all services handled by the requesting party.
Optionally, the process of obtaining the first transaction amount includes:
acquiring the number of transacted target services of the requesting party in each preset time period in a time period sequence, and taking the number of transacted target services as target transacted amount of each preset time period; the time period sequence comprises a plurality of time periods which are ordered according to time sequence;
acquiring the weight of each time period according to a preset weight corresponding relation, wherein the weight corresponding relation comprises the corresponding relation between each time period and each preset weight; wherein the earlier the time is, the smaller the corresponding weight of the time period is;
and adding the target transaction amount of each time period according to the weight of the time period to obtain the first transaction amount.
Optionally, obtaining the service score of the target knowledge point according to the service information of the target knowledge point includes:
acquiring service scores of the target knowledge points according to the service information of the target knowledge points;
wherein the business score of the target knowledge point is inversely related to the first transaction amount and the business score of the target knowledge point is inversely related to the target proportion.
Optionally, obtaining the score result of the target knowledge point according to the search score and the service score of the target knowledge point includes:
multiplying the search score and the business score of the target knowledge point to obtain a score result of the target knowledge point;
or, the search score and the business score of the target knowledge point are added in a weighted mode according to the preset score weight, and the score result of the target knowledge point is obtained.
Optionally, the preset result condition further includes: the search score is not less than a second preset threshold and/or the traffic score is not less than a third preset threshold.
The embodiment of the application also provides a readable storage medium, which may store a computer program adapted to be executed by a processor, where the computer program implements the steps of a data processing method provided by the embodiment of the application, as follows:
receiving a to-be-searched problem sent by a requesting party, and acquiring a search score of each preset knowledge point, wherein the search score of a target knowledge point indicates the probability that the target knowledge point is used for solving the to-be-searched problem, and the target knowledge point is any knowledge point;
acquiring service scores of each knowledge point according to service information of each knowledge point, wherein the service information of the target knowledge point comprises the number of target services handled by the requesting party, and the target services are services to which the target knowledge point belongs;
obtaining a score result of each knowledge point according to the search score and the service score of each knowledge point;
according to the scoring result of each knowledge point, a knowledge point sequence is obtained, the knowledge point sequence comprises N result knowledge points which are ordered from big to small according to the scoring result, the result knowledge points are knowledge points meeting the preset result condition, the result condition comprises that the scoring result is not smaller than a first preset threshold, and N is the preset result number.
Optionally, receiving the to-be-retrieved problem sent by the requester, and obtaining a search score of the target knowledge point includes:
receiving the to-be-retrieved problem sent by the requester, and acquiring the similarity between the to-be-retrieved problem and a target problem, wherein the target problem comprises at least one preset standard problem corresponding to the target knowledge point;
and determining the search score of the target knowledge point according to the similarity, wherein the similarity and the search score of the target knowledge point form a positive correlation.
Optionally, the service information of the target knowledge point further includes a target proportion, where the target proportion is a ratio of a first transaction amount to a second transaction amount, the first transaction amount is a number of the target services handled by the requesting party, and the second transaction amount is a total number of all services handled by the requesting party.
Optionally, the process of obtaining the first transaction amount includes:
acquiring the number of transacted target services of the requesting party in each preset time period in a time period sequence, and taking the number of transacted target services as target transacted amount of each preset time period; the time period sequence comprises a plurality of time periods which are ordered according to time sequence;
acquiring the weight of each time period according to a preset weight corresponding relation, wherein the weight corresponding relation comprises the corresponding relation between each time period and each preset weight; wherein the earlier the time is, the smaller the corresponding weight of the time period is;
and adding the target transaction amount of each time period according to the weight of the time period to obtain the first transaction amount.
Optionally, obtaining the service score of the target knowledge point according to the service information of the target knowledge point includes:
acquiring service scores of the target knowledge points according to the service information of the target knowledge points;
wherein the business score of the target knowledge point is inversely related to the first transaction amount and the business score of the target knowledge point is inversely related to the target proportion.
Optionally, obtaining the score result of the target knowledge point according to the search score and the service score of the target knowledge point includes:
multiplying the search score and the business score of the target knowledge point to obtain a score result of the target knowledge point;
or, the search score and the business score of the target knowledge point are added in a weighted mode according to the preset score weight, and the score result of the target knowledge point is obtained.
Optionally, the preset result condition further includes: the search score is not less than a second preset threshold and/or the traffic score is not less than a third preset threshold.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of data processing, comprising:
receiving a to-be-searched problem sent by a requesting party, and acquiring a search score of each preset knowledge point, wherein the search score of a target knowledge point indicates the probability that the target knowledge point is used for solving the to-be-searched problem, and the target knowledge point is any knowledge point;
acquiring service scores of each knowledge point according to service information of each knowledge point, wherein the service information of the target knowledge point comprises the number of target services handled by the requesting party, and the target services are services to which the target knowledge point belongs;
obtaining a score result of each knowledge point according to the search score and the service score of each knowledge point;
according to the scoring result of each knowledge point, a knowledge point sequence is obtained, the knowledge point sequence comprises N result knowledge points which are ordered from big to small according to the scoring result, the result knowledge points are knowledge points meeting the preset result condition, the result condition comprises that the scoring result is not smaller than a first preset threshold, and N is the preset result number.
2. The method of claim 1, wherein receiving the question to be retrieved sent by the requestor, obtaining a search score for the target knowledge point, comprises:
receiving the to-be-retrieved problem sent by the requester, and acquiring the similarity between the to-be-retrieved problem and a target problem, wherein the target problem comprises at least one preset standard problem corresponding to the target knowledge point;
and determining the search score of the target knowledge point according to the similarity, wherein the similarity and the search score of the target knowledge point form a positive correlation.
3. The method of claim 1, wherein the traffic information of the target knowledge point further comprises a target proportion, the target proportion being a ratio of a first amount of traffic handled by the requesting party to a second amount of traffic handled by the requesting party, the second amount of traffic handled being a total amount of all traffic handled by the requesting party.
4. The method of claim 3, wherein obtaining the first transaction amount comprises:
acquiring the number of transacted target services of the requesting party in each preset time period in a time period sequence, and taking the number of transacted target services as target transacted amount of each preset time period; the time period sequence comprises a plurality of time periods which are ordered according to time sequence;
acquiring the weight of each time period according to a preset weight corresponding relation, wherein the weight corresponding relation comprises the corresponding relation between each time period and each preset weight; wherein the earlier the time is, the smaller the corresponding weight of the time period is;
and adding the target transaction amount of each time period according to the weight of the time period to obtain the first transaction amount.
5. The method according to claim 3 or 4, wherein obtaining the business score of the target knowledge point according to the business information of the target knowledge point comprises:
acquiring service scores of the target knowledge points according to the service information of the target knowledge points;
wherein the business score of the target knowledge point is inversely related to the first transaction amount and the business score of the target knowledge point is inversely related to the target proportion.
6. The method of claim 1, wherein obtaining the score result of the target knowledge point based on the search score and the business score of the target knowledge point comprises:
multiplying the search score and the business score of the target knowledge point to obtain a score result of the target knowledge point;
or, the search score and the business score of the target knowledge point are added in a weighted mode according to the preset score weight, and the score result of the target knowledge point is obtained.
7. The method of claim 1, wherein the pre-set outcome condition further comprises: the search score is not less than a second preset threshold and/or the traffic score is not less than a third preset threshold.
8. A data processing apparatus, comprising:
the first score acquisition unit is used for receiving the to-be-searched problem sent by the requesting party, acquiring the search score of each preset knowledge point, and indicating the probability that the target knowledge point is used for solving the to-be-searched problem by the search score of the target knowledge point, wherein the target knowledge point is any knowledge point;
the second score acquisition unit is used for acquiring the service score of each knowledge point according to the service information of each knowledge point, wherein the service information of the target knowledge point comprises the number of target service transacted by the requesting party, and the target service is the service to which the target knowledge point belongs;
the third score acquisition unit is used for acquiring a score result of each knowledge point according to the search score and the service score of each knowledge point;
the result acquisition unit is used for acquiring a knowledge point sequence according to the score result of each knowledge point, wherein the knowledge point sequence comprises N result knowledge points which are ordered from big to small according to the score result, the result knowledge points are knowledge points meeting the preset result condition, the result condition comprises that the score result is not smaller than a first preset threshold, and the N is the preset result number.
9. A data processing apparatus, comprising: a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the respective steps of the data processing method according to any one of claims 1 to 7.
10. A readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the data processing method according to any one of claims 1 to 7.
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| CN110020181A (en) * | 2018-01-02 | 2019-07-16 | 中国移动通信有限公司研究院 | A kind of processing method of recommendation information, device and computer readable storage medium |
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