CN106851033A - service recommendation method and system based on data mining - Google Patents
service recommendation method and system based on data mining Download PDFInfo
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- CN106851033A CN106851033A CN201710018544.4A CN201710018544A CN106851033A CN 106851033 A CN106851033 A CN 106851033A CN 201710018544 A CN201710018544 A CN 201710018544A CN 106851033 A CN106851033 A CN 106851033A
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- 238000007418 data mining Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000013075 data extraction Methods 0.000 claims abstract description 6
- 230000011218 segmentation Effects 0.000 claims description 16
- 238000006243 chemical reaction Methods 0.000 claims description 11
- 238000004140 cleaning Methods 0.000 claims description 6
- 238000007621 cluster analysis Methods 0.000 claims description 6
- 208000003028 Stuttering Diseases 0.000 claims description 5
- 230000009471 action Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 7
- 238000004458 analytical method Methods 0.000 abstract description 4
- 238000005065 mining Methods 0.000 abstract description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
- H04M3/527—Centralised call answering arrangements not requiring operator intervention
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/12—Hotels or restaurants
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M3/00—Automatic or semi-automatic exchanges
- H04M3/42—Systems providing special services or facilities to subscribers
- H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
- H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
- H04M3/5183—Call or contact centers with computer-telephony arrangements
- H04M3/5191—Call or contact centers with computer-telephony arrangements interacting with the Internet
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F40/00—Handling natural language data
- G06F40/20—Natural language analysis
- G06F40/279—Recognition of textual entities
- G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
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- Business, Economics & Management (AREA)
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- Marketing (AREA)
- Tourism & Hospitality (AREA)
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Abstract
The present invention provides a kind of service recommendation method and system based on data mining, and wherein method includes obtaining caller client information, and caller client information includes the telephone number of client;Telephone number according to client determines the type of client;The corresponding customer data of type-collection according to client;Data mining is carried out according to customer data, service recommendation menu is generated.System includes caller client data obtaining module, customer type determining module, customer data extraction module and data-mining module.The present invention is by obtaining caller client information, and the mining analysis treatment of system is carried out to caller client information, ultimately generate client's service recommendation menu interested, so as to reduce customer service flow, raising system operating efficiency, while simplifying the operating process of client, saves client's time, artificial customer service workload is reduced, improves the customer service experience of client.
Description
Technical field
The present invention relates to data analysis technique field, more particularly to a kind of service recommendation method based on data mining and it is
System.
Background technology
Existing customer service system is divided into manual service and voice Self-Service mostly, and voice Self-Service generally needs visitor
Family multi-pass operation multistage customer service menu, its complex operation in the phone, take it is tediously long, therefore, it is general last all to turn to people's work clothes
Business, so that manual service is frequently necessary to wait in line.It is this based on manual service, with self-assisted voice service supplemented by behaviour
Operation mode causes that system service efficiency is low, and cost of labor is high, and the service that client is needed will more consume the substantial amounts of time.
The content of the invention
It is an object of the invention to provide a kind of service recommendation method based on data mining and system, it is used to solve existing
The low problem of customer service system efficiency of service in technology.
To achieve these goals, the first aspect of the invention is to provide a kind of service recommendation side based on data mining
Method, comprises the following steps:
Caller client information is obtained, caller client information includes the telephone number of client;
Telephone number according to client determines the type of client;
The corresponding customer data of type-collection according to client;
Data mining is carried out according to customer data, service recommendation menu is generated.
Further, also included setting up customer data base before caller client information is obtained, customer data base includes
Type, the customer data of the telephone number of client client corresponding with the telephone number of client.
Further, the telephone number according to client determines that the operation of the type of client is specifically included:
Telephone number according to client is inquired about in customer data base, determines that the type of client is new client, has contacted visitor
Family or customer insured.
Further, data mining is carried out according to the customer data, the operation for forming service recommendation menu is specifically included:
Customer data is carried out into text-processing, to form text data;
Data cleansing treatment is carried out to text data;
By stammering, participle instrument carries out word segmentation processing to the data after cleaning;
Using word frequency-reverse document-frequency algorithm (term frequency-inverse document frequency,
Abbreviation TFIDF) numerical value conversion is carried out to the data after word segmentation processing, to obtain the corresponding numerical value sample of text data;
Clustered to obtain cluster result by K mean cluster algorithm logarithm value sample;
Cluster analysis is carried out to cluster result to generate service recommendation menu.
Further, customer data includes customer action data and call voice data.
Another aspect of the present invention is to provide a kind of service recommendation system based on data mining, including:
Caller client data obtaining module, for obtaining caller client information, caller client information includes the phone of client
Number;
Customer type determining module, the type for determining client according to the telephone number of client;
Customer data extraction module, for the corresponding customer data of type-collection according to client;
Data-mining module, for carrying out data mining according to customer data, generates service recommendation menu.
Further, also including user data library module, for storing the telephone number of client and the telephone number of client
The type of corresponding client, customer data.
Further, data-mining module includes:
Text-processing unit, for customer data to be carried out into text-processing, to form text data;
Data cleansing unit, for carrying out data cleansing treatment to text data;
Word segmentation processing unit, for carrying out word segmentation processing to the data after cleaning by participle instrument of stammering;
Date Conversion Unit, for carrying out numerical value conversion to the data after word segmentation processing using TFIDF algorithms, to obtain text
The corresponding numerical value sample of notebook data;
Cluster cell, for being clustered to obtain cluster result by K mean cluster algorithm logarithm value sample;
Service recommendation Menu generation unit, for carrying out cluster analysis to generate service recommendation menu to cluster result.
Beneficial effect using the invention described above technical scheme is:By obtaining caller client information, and to caller client
Information carries out the mining analysis treatment of system, ultimately generates client's service recommendation menu interested, so that customer service flow is reduced,
Raising system operating efficiency, while simplifying the operating process of client, saves client's time, reduces artificial customer service workload, improves
The customer service experience of client.
Brief description of the drawings
Fig. 1 is the structural representation of service recommendation system of the present invention based on data mining;
Fig. 2 is the structural representation of data-mining module in Fig. 1;
Fig. 3 is the flow chart of service recommendation method of the present invention based on data mining.
Specific embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
A part of embodiment of the present invention, rather than whole embodiments.
The embodiment of the invention discloses a kind of service recommendation system based on data mining, as shown in figure 1, the system bag
Include:Caller client data obtaining module 100, customer type determining module 200, customer data extraction module 300 and data are dug
Pick module 400, wherein,
Caller client data obtaining module, for obtaining caller client information, caller client information includes the phone of client
Number;
Customer type determining module, the type for determining client according to the telephone number of client;
Customer data extraction module, for the corresponding customer data of type-collection according to client;
Data-mining module, for carrying out data mining according to customer data, generates service recommendation menu.
In still another embodiment of the process, the system can also include user data library module 500, for storing client's
Type, customer data of telephone number client corresponding with the telephone number of client etc..
Specifically, as shown in Fig. 2 in the above embodiment of the present invention, data-mining module 400 can also include at text
Reason unit 401, data cleansing unit 402, word segmentation processing unit 403, Date Conversion Unit 404, cluster cell 405 and service
Recommend Menu generation unit 406, wherein,
Text-processing unit, for customer data to be carried out into text-processing, to form text data;
Data cleansing unit, for carrying out data cleansing treatment to text data;
Word segmentation processing unit, for carrying out word segmentation processing to the data after cleaning by participle instrument of stammering;
Date Conversion Unit, for carrying out numerical value conversion to the data after word segmentation processing using TFIDF algorithms, to obtain text
The corresponding numerical value sample of notebook data;
Cluster cell, for being clustered to obtain cluster result by K mean cluster algorithm logarithm value sample;
Service recommendation Menu generation unit, for carrying out cluster analysis to generate service recommendation menu to cluster result.
The embodiment of the present invention obtains caller client information by caller client data obtaining module, and by data mining mould
Block carries out the mining analysis treatment of system to caller client information, ultimately generates client's service recommendation menu interested, so that
Customer service flow is reduced, system operating efficiency is improved, while simplifying the operating process of client, client's time is saved, artificial visitor is reduced
Workload is taken, improves the customer service experience of client.
The embodiment of the invention also discloses a kind of service recommendation method based on data mining, as shown in figure 3, the method can
To comprise the following steps:
Step S301, obtains caller client information, and caller client information includes the telephone number of client;
Step S302, the telephone number according to client determines the type of client;
Step S303, the corresponding customer data of type-collection according to client;
Step S304, data mining is carried out according to customer data, generates service recommendation menu.
In the present embodiment, the service recommendation system based on data mining accesses caller client information, caller client information
Acquisition module then obtains caller client information, specifically, caller client information includes the telephone number of client.System is according to client
Telephone number, customer information is transferred by customer data extraction module from the user data library module for pre-building, this
In embodiment, the class of the telephone number client corresponding with the telephone number of client of client is stored in user data library module
Type, customer data etc..Such that it is able to according to the customer information transferred determine the type of client be new client, contacted client or
Customer insured, specifically, determining the type of client by customer type determining module, and further will by data-mining module
The customer data transferred carries out data mining treatment.
Specifically, in the present embodiment, by text-processing unit to customer data such as customer action data, call voice
Data etc. carry out text-processing, to form text data;Then by data cleansing unit, data cleansing is carried out to text data
Treatment, the information removal useless so as to mistake, omission, punctuate etc. will be there may be in former data;Then to the text after cleaning
Notebook data carries out participle using word segmentation processing unit, will the text of section greatly be divided into a large amount of single vocabulary, it is main from comparing
Conventional stammerer participle instrument.Text data after participle is, it is necessary to be converted to the manageable numerical value of clustering algorithm one by one
Vector, therefore, it can by Date Conversion Unit using TFIDF algorithms to word segmentation processing after data carry out numerical value conversion so that
Be converted to numerical value sample one by one.
And these available numerical value samples are clustered using cluster cell, can be poly- using K averages in the present embodiment
Class algorithm.The initial cluster number K of the algorithm picks, and K initial cluster centre point.Sample is put into distance one by one
Cluster where nearest central point, updates the value of central point.Aforementioned process is repeated, until central point no longer changes, that is, is thought
The cluster result wanted.Process of cluster analysis uses silhouette coefficient, intra-cluster distance to evaluate cluster result as evaluation criterion.Adjustment K
Value, and repeatedly clustered, until finding the optimal K value parameters of evaluation criterion.The optimal cluster result of K values is to be wanted to obtain
Cluster result, by analyzing the initial data of text corresponding to cluster result, such that it is able to obtain significant clustering information,
Final service recommendation menu is generated by service recommendation Menu generation unit.If client can look in service recommendation menu
To desired business handling project, and handle successfully, then customer service end of conversation, otherwise, be given to artificial customer service.
The embodiment of the present invention is carried out at the mining analysis of system by obtaining caller client information to caller client information
Reason, ultimately generates client's service recommendation menu interested, so as to reduce customer service flow, system operating efficiency is improved, while simple
Change the operating process of client, save client's time, reduce artificial customer service workload, improve the customer service experience of client.
One of ordinary skill in the art will appreciate that:Realizing all or part of step of above-mentioned each method embodiment can lead to
The related hardware of programmed instruction is crossed to complete.Foregoing program can be stored in a computer read/write memory medium.The journey
Sequence upon execution, performs the step of including above-mentioned each method embodiment;And foregoing storage medium includes:ROM, RAM, magnetic disc or
Person's CD etc. is various can be with the medium of store program codes.
Finally it should be noted that:Various embodiments above is merely illustrative of the technical solution of the present invention, rather than its limitations;To the greatest extent
Pipe has been described in detail with reference to foregoing embodiments to the present invention, it will be understood by those within the art that:Its according to
The technical scheme described in foregoing embodiments can so be modified, or which part or all technical characteristic are entered
Row equivalent;And these modifications or replacement, the essence of appropriate technical solution is departed from various embodiments of the present invention technology
The scope of scheme.
Claims (8)
1. a kind of service recommendation method based on data mining, it is characterised in that comprise the following steps:
Caller client information is obtained, the caller client information includes the telephone number of client;
Telephone number according to client determines the type of client;
The corresponding customer data of type-collection according to client;
Data mining is carried out according to the customer data, service recommendation menu is generated.
2. the service recommendation method based on data mining according to claim 1, it is characterised in that obtaining caller client
Also include setting up customer data base before information, the customer data base includes the telephone number of client and the client
Type, the customer data of the corresponding client of telephone number.
3. the service recommendation method based on data mining according to claim 2, it is characterised in that described according to client's
Telephone number determines that the operation of the type of client is specifically included:
Telephone number according to client is inquired about in customer data base, determine the type of client for new client, contacted client or
Customer insured.
4. the service recommendation method based on data mining according to claim 3, it is characterised in that according to client's number
According to data mining is carried out, the operation for forming service recommendation menu is specifically included:
Customer data is carried out into text-processing, to form text data;
Data cleansing treatment is carried out to the text data;
By stammering, participle instrument carries out word segmentation processing to the data after cleaning;
Numerical value conversion is carried out to the data after word segmentation processing using TFIDF algorithms, to obtain the corresponding numerical value of the text data
Sample;
The numerical value sample is clustered to obtain cluster result by K mean cluster algorithm;
Cluster analysis is carried out to the cluster result to generate service recommendation menu.
5. the service recommendation method based on data mining according to any one of Claims 1 to 4, it is characterised in that described
Customer data includes customer action data and call voice data.
6. a kind of service recommendation system based on data mining, it is characterised in that including:
Caller client data obtaining module, for obtaining caller client information, the caller client information includes the phone of client
Number;
Customer type determining module, the type for determining client according to the telephone number of client;
Customer data extraction module, for the corresponding customer data of type-collection according to client;
Data-mining module, for carrying out data mining according to customer data, generates service recommendation menu.
7. the service recommendation system based on data mining according to claim 6, it is characterised in that also including user data
Library module, the user data library module is used to store the telephone number visitor corresponding with the telephone number of the client of client
The type at family, customer data.
8. the service recommendation system based on data mining according to claim 6 or 7, it is characterised in that the data are dug
Pick module includes:
Text-processing unit, for customer data to be carried out into text-processing, to form text data;
Data cleansing unit, for carrying out data cleansing treatment to the text data;
Word segmentation processing unit, for carrying out word segmentation processing to the data after cleaning by participle instrument of stammering;
Date Conversion Unit, for carrying out numerical value conversion to the data after word segmentation processing using TFIDF algorithms, to obtain the text
The corresponding numerical value sample of notebook data;
Cluster cell, for being clustered the numerical value sample to obtain cluster result by K mean cluster algorithm;
Service recommendation Menu generation unit, for carrying out cluster analysis to generate service recommendation menu to the cluster result.
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Cited By (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107993133A (en) * | 2018-01-23 | 2018-05-04 | 北京知行信科技有限公司 | A kind of intellectual analysis based on natural language recommends method and system |
CN108038765A (en) * | 2017-12-23 | 2018-05-15 | 临泉县烜槿餐饮管理有限公司 | A kind of Catering Management formula order dishes system caught based on video |
CN108876452A (en) * | 2018-06-12 | 2018-11-23 | 广东电网有限责任公司 | Method and device for acquiring demand information of electricity customer and electronic equipment |
WO2019041774A1 (en) * | 2017-08-28 | 2019-03-07 | 平安科技(深圳)有限公司 | Customer information screening method and apparatus, electronic device, and medium |
CN109815251A (en) * | 2019-02-26 | 2019-05-28 | 江西师范大学 | A kind of multi-function service digging system |
CN109829810A (en) * | 2018-12-13 | 2019-05-31 | 平安科技(深圳)有限公司 | Business recommended method, apparatus, computer equipment and storage medium |
CN112150197A (en) * | 2020-09-17 | 2020-12-29 | 杭州云徙科技有限公司 | Online customer service system based on business middlebox and management method thereof |
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WO2019041774A1 (en) * | 2017-08-28 | 2019-03-07 | 平安科技(深圳)有限公司 | Customer information screening method and apparatus, electronic device, and medium |
CN108038765A (en) * | 2017-12-23 | 2018-05-15 | 临泉县烜槿餐饮管理有限公司 | A kind of Catering Management formula order dishes system caught based on video |
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CN107993133A (en) * | 2018-01-23 | 2018-05-04 | 北京知行信科技有限公司 | A kind of intellectual analysis based on natural language recommends method and system |
CN108876452A (en) * | 2018-06-12 | 2018-11-23 | 广东电网有限责任公司 | Method and device for acquiring demand information of electricity customer and electronic equipment |
CN109829810A (en) * | 2018-12-13 | 2019-05-31 | 平安科技(深圳)有限公司 | Business recommended method, apparatus, computer equipment and storage medium |
CN109829810B (en) * | 2018-12-13 | 2024-02-20 | 平安科技(深圳)有限公司 | Service recommendation method, device, computer equipment and storage medium |
CN109815251A (en) * | 2019-02-26 | 2019-05-28 | 江西师范大学 | A kind of multi-function service digging system |
CN112150197A (en) * | 2020-09-17 | 2020-12-29 | 杭州云徙科技有限公司 | Online customer service system based on business middlebox and management method thereof |
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