CN109471973A - A kind of restaurant intelligent recommendation method, apparatus, equipment and storage medium - Google Patents
A kind of restaurant intelligent recommendation method, apparatus, equipment and storage medium Download PDFInfo
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- CN109471973A CN109471973A CN201811287504.0A CN201811287504A CN109471973A CN 109471973 A CN109471973 A CN 109471973A CN 201811287504 A CN201811287504 A CN 201811287504A CN 109471973 A CN109471973 A CN 109471973A
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- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000003860 storage Methods 0.000 title claims abstract description 30
- 238000012549 training Methods 0.000 claims description 88
- 238000011156 evaluation Methods 0.000 claims description 34
- 238000004590 computer program Methods 0.000 claims description 11
- 238000012216 screening Methods 0.000 claims description 11
- 238000001914 filtration Methods 0.000 claims description 9
- 230000000291 postprandial effect Effects 0.000 claims description 9
- 235000012054 meals Nutrition 0.000 claims description 5
- 230000008901 benefit Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 235000013311 vegetables Nutrition 0.000 description 9
- 230000006870 function Effects 0.000 description 6
- 238000007405 data analysis Methods 0.000 description 4
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- 241000219198 Brassica Species 0.000 description 1
- 235000003351 Brassica cretica Nutrition 0.000 description 1
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- 235000010460 mustard Nutrition 0.000 description 1
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- 238000007873 sieving Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
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Abstract
This application discloses a kind of restaurant intelligent recommendation method, apparatus, equipment and storage mediums, this method comprises: receiving the demand of having dinner of purpose customer, and the Customer Information of the purpose customer is obtained, the Customer Information includes the personal information, work financial information and history dining information of corresponding customer；Customer Information based on the purpose customer filters out corresponding restaurant as candidate restaurant, and the restaurant information in the candidate restaurant is recommended the purpose customer；The restaurant that the purpose customer selects is determined as purpose restaurant, the purpose customer is obtained and corresponds to the order placement information in the purpose restaurant and be sent to the purpose restaurant, the order placement information includes demand information of having dinner.The application is when realizing restaurant intelligent recommendation function, comprehensively consider personal information, the work more comprehensive factor such as financial information and history dining information of customer, to more acurrate, the comprehensive restaurant for recommending to meet customer demand out, to meet requirement of the customer to restaurant recommendation.
Description
Technical field
This application involves big data analysis technical fields, more specifically to a kind of restaurant intelligent recommendation method, dress
It sets, equipment and storage medium.
Background technique
With the extensive use of big data analysis, people also often utilize the restaurant realized based on big data analysis when having dinner
Recommender system realizes the selection in restaurant, and then has dinner.
Restaurant recommendation system in the prior art is usually to select distance to need to have dinner when realizing restaurant recommendation function
The nearest restaurant recommendation of customer to customer, have dinner in order to which customer can reach restaurant with faster speed.But with people
Improvement of living standard, people are increasingly demanding on the diet, and the economic condition of customer, physical qualification etc. it is many because
Element can all have an impact having dinner for customer, the above-mentioned mode for being based only on the distance between restaurant and customer and realizing restaurant recommendation
Consideration is single, can not accurately, comprehensively recommend the restaurant for meeting customer demand out, it is clear that be no longer satisfied customer
Requirement to restaurant recommendation.
It is current ability in conclusion how to provide a kind of technical solution that can accurately, comprehensively realize restaurant recommendation
Field technique personnel's urgent problem to be solved.
Summary of the invention
The purpose of the application is to provide a kind of restaurant intelligent recommendation method, apparatus, equipment and storage medium, can be calibrated
Really, restaurant recommendation is comprehensively realized.
To achieve the goals above, the application provides the following technical solutions:
A kind of restaurant intelligent recommendation method, comprising:
The demand of having dinner of purpose customer is received, and obtains the Customer Information of the purpose customer, the Customer Information includes
Personal information, work financial information and the history dining information of corresponding customer；
Customer Information based on the purpose customer filters out corresponding restaurant as candidate restaurant, and by the candidate restaurant
Restaurant information recommend the purpose customer；
The restaurant that the purpose customer selects is determined as purpose restaurant, the purpose customer is obtained and corresponds to the purpose restaurant
Order placement information and be sent to the purpose restaurant, the order placement information includes having dinner demand information.
Preferably, after the Customer Information for obtaining the purpose customer, and before filtering out the candidate restaurant, also
Include:
The significance level for the every terms of information for including in Customer Information based on the purpose customer, for every terms of information imparting pair
The weight answered, and the corresponding weight of every terms of information is added separately in the Customer Information of the purpose customer.
Preferably, the Customer Information based on the purpose customer filters out corresponding restaurant as candidate restaurant, comprising:
The Customer Information of the purpose customer is input in the restaurant intelligent recommendation model being pre-created, and described in determination
The restaurant information of restaurant intelligent recommendation model output corresponds to restaurant as candidate restaurant；
The process that the restaurant intelligent recommendation model is pre-created includes:
Acquisition includes the training information collection of multiple training informations, and based on the default classification mould of the training information collection training
Type obtains restaurant intelligent recommendation model, the training information include history have dinner customer Customer Information and corresponding restaurant of having dinner
Restaurant information.
Preferably, after determining the purpose restaurant, before the order placement information is sent to the purpose restaurant,
Further include:
Determine that the purpose customer is presently in the route information of position to purpose restaurant present position, and will be described
Route information is added into the order placement information.
Preferably, the order placement information is sent to after the purpose restaurant, further includes:
The purpose customer the purpose restaurant complete just it is postprandial, obtain the purpose customer for this have dinner into
The dining information that the evaluation information of row evaluation and the purpose customer this time have dinner；
The evaluation information is added into the restaurant information in the purpose restaurant, and the dining information is added to institute
It states in the history dining information of purpose customer.
Preferably, after being added the evaluation information and the dining information into corresponding information, further includes:
The restaurant information of the Customer Information of the purpose customer and the purpose restaurant is added to the training information collection
In, disaggregated model is preset using the training information collection training and obtains new restaurant intelligent recommendation model, and is based on when needed
The screening in the new restaurant intelligent recommendation model realization candidate restaurant.
Preferably, after the Customer Information for getting the purpose customer, further includes:
It is confirmed whether that there are customized demand information with the purpose customer, if it is, the customized demand information is added
Enter into the Customer Information of the purpose customer, and determines that the weight of the customized demand information is greater than its in the Customer Information
The weight of his every terms of information；If it is not, then determining without updating the Customer Information.
A kind of restaurant intelligent recommendation device, comprising:
Module is obtained, is used for: receiving the demand of having dinner of purpose customer, and obtains the Customer Information of the purpose customer, institute
State the personal information, work financial information and history dining information that Customer Information includes corresponding customer；
Recommending module is used for: the Customer Information based on the purpose customer filters out corresponding restaurant as candidate restaurant, and
The restaurant information in the candidate restaurant is recommended into the purpose customer；
Lower list module, is used for: determining that the restaurant that the purpose customer selects for purpose restaurant, obtains the purpose customer couple
It answers the order placement information in the purpose restaurant and is sent to the purpose restaurant, the order placement information includes demand information of having dinner.
Preferably, further includes:
Power module is assigned, is used for: after the Customer Information for obtaining the purpose customer, and filtering out the candidate restaurant
Before, the significance level for the every terms of information for including in the Customer Information based on the purpose customer is assigned for every terms of information and being corresponded to
Weight, and the corresponding weight of every terms of information is added separately in the Customer Information of the purpose customer.
Preferably, the recommending module includes:
Screening unit is used for: the Customer Information of the purpose customer is input to the restaurant intelligent recommendation mould being pre-created
In type, and determine that the restaurant information of the restaurant intelligent recommendation model output corresponds to restaurant as candidate restaurant；
It is corresponding, further includes:
First training module, is used for: acquisition includes the training information collection of multiple training informations, and based on the training letter
The default disaggregated model of breath collection training obtains restaurant intelligent recommendation model, and the training information includes that history is had dinner customer's letter of customer
Breath and the restaurant information in corresponding restaurant of having dinner.
Preferably, further includes:
First adding module, is used for: after determining the purpose restaurant, the order placement information being sent to the mesh
Restaurant before, determine that the purpose customer is presently in the route information of position to purpose restaurant present position, and will
The route information is added into the order placement information.
Preferably, further includes:
Second adding module, is used for: after the order placement information is sent to the purpose restaurant, in the purpose customer
It is completed in the purpose restaurant just postprandial, obtains the purpose customer for this evaluation information evaluated and described of having dinner
The dining information that purpose customer this time has dinner；The evaluation information is added into the restaurant information in the purpose restaurant, and will
The dining information is added into the history dining information of the purpose customer.
Preferably, further includes:
Second training module, is used for: it is being added into corresponding information in the evaluation information and the dining information
Afterwards, the restaurant information of the Customer Information of the purpose customer and the purpose restaurant is added to the training information and is concentrated, benefit
Disaggregated model, which is preset, with the training information collection training obtains new restaurant intelligent recommendation model, and new based on this when needed
The screening in restaurant intelligent recommendation model realization candidate restaurant.
Preferably, further includes:
Third adding module, is used for: true with the purpose customer after the Customer Information for getting the purpose customer
Recognize with the presence or absence of customized demand information, if it is, being added the customized demand information to customer's letter of the purpose customer
In breath, and determine that the weight of the customized demand information is greater than the weight of other every terms of information in the Customer Information；If not,
It then determines without updating the Customer Information.
A kind of restaurant intelligent recommendation equipment, comprising:
Memory, for storing computer program；
Processor, the step of intelligent recommendation method in restaurant as described above is realized when for executing the computer program.
Preferably, when the processor executes the computer subprogram stored in the memory, following step is implemented
It is rapid: after the Customer Information for obtaining the purpose customer, and before filtering out the candidate restaurant, to be cared for based on the purpose
The significance level for the every terms of information for including in the Customer Information of visitor assigns corresponding weight for every terms of information, and by every terms of information
Corresponding weight is added separately in the Customer Information of the purpose customer.
Preferably, when the processor executes the computer subprogram stored in the memory, following step is implemented
It is rapid: the Customer Information of the purpose customer being input in the restaurant intelligent recommendation model being pre-created, and determines the restaurant
The restaurant information of intelligent recommendation model output corresponds to restaurant as candidate restaurant；And acquisition includes the training of multiple training informations
Information collection, and disaggregated model is preset based on the training information collection training and obtains restaurant intelligent recommendation model, the training information
It has dinner including history the Customer Information of customer and the restaurant information in corresponding restaurant of having dinner.
Preferably, when the processor executes the computer subprogram stored in the memory, following step is implemented
It is rapid: after determining the purpose restaurant, before the order placement information is sent to the purpose restaurant, to determine the purpose
Customer is presently in the route information of position to purpose restaurant present position, and the route information is added under described
In single information.
Preferably, when the processor executes the computer subprogram stored in the memory, following step is implemented
It is rapid: after the order placement information is sent to the purpose restaurant, to complete to have dinner in the purpose restaurant in the purpose customer
Afterwards, obtain that the purpose customer has dinner the evaluation information evaluated for this and what the purpose customer this time had dinner has dinner
Information；The evaluation information is added into the restaurant information in the purpose restaurant, and the dining information is added to described
In the history dining information of purpose customer.
Preferably, when the processor executes the computer subprogram stored in the memory, following step is implemented
It is rapid: after being added the evaluation information and the dining information into corresponding information, by the customer of the purpose customer
The restaurant information in information and the purpose restaurant is added to the training information and concentrates, default using the training information collection training
Disaggregated model obtains new restaurant intelligent recommendation model, and candidate based on the new restaurant intelligent recommendation model realization when needed
The screening in restaurant.
Preferably, when the processor executes the computer subprogram stored in the memory, following step is implemented
It is rapid: after the Customer Information for getting the purpose customer, it is confirmed whether with the purpose customer there are customized demand information,
If it is, being added the customized demand information into the Customer Information of the purpose customer, and determine the customized demand
The weight of information is greater than the weight of other every terms of information in the Customer Information；If it is not, then determining without updating the customer
Information.
A kind of computer readable storage medium is stored with computer program on the computer readable storage medium, described
The step of intelligent recommendation method in restaurant as described above is realized when computer program is executed by processor.
Preferably, when the computer subprogram saved in the computer readable storage medium is executed by processor, specifically
It performs the steps of after the Customer Information for obtaining the purpose customer, and before filtering out the candidate restaurant, is based on
The significance level for the every terms of information for including in the Customer Information of the purpose customer assigns corresponding weight for every terms of information, and
The corresponding weight of every terms of information is added separately in the Customer Information of the purpose customer.
Preferably, when the computer subprogram saved in the computer readable storage medium is executed by processor, specifically
It performs the steps of and the Customer Information of the purpose customer is input in the restaurant intelligent recommendation model being pre-created, and really
The restaurant information of the fixed restaurant intelligent recommendation model output corresponds to restaurant as candidate restaurant；And obtaining includes multiple training
The training information collection of information, and disaggregated model is preset based on the training information collection training and obtains restaurant intelligent recommendation model, institute
Stating training information includes that history is had dinner the Customer Information of customer and the restaurant information in corresponding restaurant of having dinner.
Preferably, when the computer subprogram saved in the computer readable storage medium is executed by processor, specifically
It performs the steps of after determining the purpose restaurant, before the order placement information is sent to the purpose restaurant, really
The fixed purpose customer is presently in the route information of position to purpose restaurant present position, and the route information is added
Enter into the order placement information.
Preferably, when the computer subprogram saved in the computer readable storage medium is executed by processor, specifically
It performs the steps of after the order placement information is sent to the purpose restaurant, eats in the purpose customer in the purpose
Shop is completed just postprandial, is obtained the purpose customer for this and is had dinner the evaluation information evaluated and the purpose customer this time
The dining information having dinner；The evaluation information is added into the restaurant information in the purpose restaurant, and by the dining information
It is added into the history dining information of the purpose customer.
Preferably, when the computer subprogram saved in the computer readable storage medium is executed by processor, specifically
It performs the steps of after being added the evaluation information and the dining information into corresponding information, by the purpose
The Customer Information of customer and the restaurant information in the purpose restaurant are added to the training information and concentrate, and utilize the training information
The default disaggregated model of collection training obtains new restaurant intelligent recommendation model, and when needed based on the new restaurant intelligent recommendation mould
Type realizes the screening in candidate restaurant.
Preferably, when the computer subprogram saved in the computer readable storage medium is executed by processor, specifically
It performs the steps of after the Customer Information for getting the purpose customer, it is fixed to be confirmed whether to exist with the purpose customer
Demand information processed if it is, being added the customized demand information into the Customer Information of the purpose customer, and determines institute
The weight for stating customized demand information is greater than the weight of other every terms of information in the Customer Information；If it is not, then determining without more
The new Customer Information.
In technical solution disclosed in the present application, after determining the customer for needing to have dinner, acquisition includes the personal letter of customer
The Customer Information of breath, work financial information and history dining information, to determine that corresponding candidate restaurant supplies based on Customer Information
Customer chooses；As it can be seen that being different from only considering between restaurant and customer in the prior art in the application when realizing restaurant recommendation
Distance, but comprehensively consider customer personal information, work the more comprehensive factor such as financial information and history dining information,
To more acurrate, the comprehensive restaurant for recommending to meet customer demand out, to meet requirement of the customer to restaurant recommendation.
Detailed description of the invention
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of application for those of ordinary skill in the art without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of flow chart of restaurant intelligent recommendation method provided by the embodiments of the present application；
Fig. 2 is a kind of structural schematic diagram of restaurant intelligent recommendation device provided by the embodiments of the present application；
Fig. 3 is a kind of structural schematic diagram of restaurant intelligent recommendation equipment provided by the embodiments of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present application, technical solutions in the embodiments of the present application carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.It is based on
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall in the protection scope of this application.
Big data analysis, which refers to, analyzes huge data, applies in people's lives increasingly wider
It is general.For restaurant recommendation function, need to get this huge data of each restaurant relevant information in advance, and then in base
Its restaurant that can have dinner of customer is recommended in the restaurant relevant information got.Restaurant and customer are only considered in the prior art
The distance between realize restaurant recommendation, Consideration is single, can not accurately, comprehensively recommend to meet customer demand out
Restaurant is unable to satisfy requirement of the customer to restaurant recommendation.For this purpose, the corresponding technical solution of the application offer is above-mentioned existing to solve
The problem of technology.
It, can be with referring to Fig. 1, it illustrates a kind of flow chart of restaurant intelligent recommendation method provided by the embodiments of the present application
Include:
S11: receiving the demand of having dinner of purpose customer, and obtain the Customer Information of purpose customer, and Customer Information includes corresponding to
Personal information, work financial information and the history dining information of customer.
It should be noted that a kind of execution subject of restaurant recommendation method provided by the embodiments of the present application can be corresponding
Restaurant recommendation device, and the device can be set in the clients such as server or mobile terminal, it specifically can be according to reality
It needs to be configured.And the operations that customer realizes in the application can be realized by clients such as the mobile terminals of customer
's.
In the presence of the customer that the customer for the demand of having dinner has dinner, any customer that there is demand of having dinner is can be with
Customer as a purpose；Specifically, the demand of having dinner for receiving purpose customer may include: that determining customer has and has dinner after intention,
It is confirmed whether to need to carry out restaurant intelligent recommendation with customer, if it is, determining that customer is purpose customer, otherwise, it is determined that care for
Visitor is not purpose customer.It sends a telegram here when inquiring or by big data wherein it is determined that there is customer the intention of having dinner can be customer
Mode get customer have dinner on the net relevant information (restaurant information, dish information etc.) inquiry when, determine that customer has
It has dinner intention；And it is confirmed whether to need to carry out restaurant intelligent recommendation with customer and can be direct-dial telephone or join on the net with customer
Confirmation is linked up by system.Determine whether to carry out restaurant intelligent recommendation for customer in this way, it can be in customer need Shi Caijin
Row restaurant intelligent recommendation, avoids the dislike for causing customer.
Customer Information can include but is not limited to personal information, work financial information and history dining information；Wherein, a
People's information may include age, gender, physical condition, kinsfolk, number of having dinner, permanent residence and the journey of receiving an education of corresponding customer
Degree etc., work financial information may include the status of corresponding customer, job specification, income level, level of consumption idea and
Credit record etc., history dining information may include corresponding customer had dinner (before current time) in history restaurant, vegetable,
Taste and the level of consumption etc..Each customer may include the information of identical entry, and specifically, the acquisition of Customer Information can adopt
It is realized with the form of electric questionnaire.It should be noted that the item number that can influence the information that customer has dinner that Customer Information obtains
It is more, be more conducive to filter out the restaurant for meeting customer demand.For different customers, the restaurant for selection of finally having dinner and
Vegetable can all make a big difference, for example, young customer or customer women are usually more likely to, finishing environment is better, vegetable
A bit exquisite restaurant, and more older customer is then usually more likely to taste pretty good more economical restaurant；Customer's
Physical condition more limits its selection of having dinner, if has dieting (must exclude Chuan Xiangcaiguan etc. if the customer to avoid pungent food
Recommend etc.), whether there is uncomfortable situation (to need to exclude the affiliated restaurant of irritable food if the customer for having a stomach upset
Deng)；Then more embody fits up recreation aspect subsidiary in other words at the restaurant for influence of the job specification to selection of having dinner, as
The work that sale etc. is treated with courtesy throughout the year then needs to consider the playgrounds such as singing, bathing near restaurant, and party member, civil servant etc. are then
It would generally exclude these places；The influence of income level and consumption idea to restaurant then more embodies star at the restaurant and price
Aspect, there are also the taste of customer and the long influence for occupying ground, etc. no longer excessive explanation herein.
S12: the Customer Information based on purpose customer filters out corresponding restaurant as candidate restaurant, and by the meal in candidate restaurant
Shop information recommendation gives purpose customer.
Customer Information based on purpose customer can filter out the restaurant for meeting purpose customer demand, i.e., candidate restaurant；Into
And the restaurant information in candidate restaurant is fed back into purpose customer, so that purpose customer therefrom chooses the restaurant having dinner.Wherein, restaurant
Information can include but is not limited to restaurant type, restaurant specific location, restaurant and extremely eat at a distance from customer, by customer current location
Real-time vehicle condition, restaurant environment, dish information, payment method, evaluation information of history customer etc. on the road in shop；So that purpose
Customer can be comprehensively considered based on these information, and the restaurant for selecting the demand of being best suitable for is had dinner；In general, restaurant
The information item number that information includes is more, is more conducive to customer's selecting restaurants and has dinner, and certainly, restaurant can also only include its name
Title or other identifier, within the scope of protection of this application.
S13: determining the restaurant that purpose customer selects as purpose restaurant, obtains the letter that places an order that purpose customer corresponds to purpose restaurant
Purpose restaurant is ceased and is sent to, order placement information includes demand information of having dinner.
Purpose customer has chosen the generation for the demand information that can remotely carry out after restaurant having dinner, wherein demand of having dinner letter
Breath may include vegetable, taste, number etc., so that the demand information that will have dinner is added and is sent to purpose restaurant into order placement information,
Purpose restaurant can start to do the specific works such as the preparation of vegetable, the selection of dining table after receiving order placement information, to facilitate Gu
It is had dinner after visitor to shop with faster velocity interpolation.
In technical solution disclosed in the present application, after determining the customer for needing to have dinner, acquisition includes the personal letter of customer
The Customer Information of breath, work financial information and history dining information, to determine that corresponding candidate restaurant supplies based on Customer Information
Customer chooses；As it can be seen that being different from only considering between restaurant and customer in the prior art in the application when realizing restaurant recommendation
Distance, but comprehensively consider customer personal information, work the more comprehensive factor such as financial information and history dining information,
To more acurrate, the comprehensive restaurant for recommending to meet customer demand out, to meet requirement of the customer to restaurant recommendation.
In addition, the application is realizing restaurant intelligent recommendation function, and customer chooses restaurant and determines demand information of having dinner
Afterwards, the demand information that will have dinner is sent to corresponding restaurant, and restaurant is enabled to do based on demand information is had dinner for having dinner for customer
Preparation is realized faster after making things convenient for customers to shop and is had dinner.
A kind of restaurant intelligent recommendation method provided by the embodiments of the present application, the Customer Information for obtaining the purpose customer it
Afterwards, and before filtering out the candidate restaurant, can also include:
The significance level for the every terms of information for including in Customer Information based on the purpose customer, for every terms of information imparting pair
The weight answered, and the corresponding weight of every terms of information is added separately in the Customer Information of the purpose customer.
It should be noted that the significance level for the every terms of information for including in Customer Information can be it is corresponding by Customer Information
Customer's setting, it is also possible to carry out (the Gu of each customer at this time of unified setting in advance according to actual needs by staff
The significance level of same item information is identical in objective information), within the scope of protection of this application, and then by with every terms of information
The weighted value of significance level is added to corresponding Customer Information.Since for customer, different item information is important in Customer Information
Degree is different, as some customers may more value restaurant environment, some customers may more value restaurant locations and some care for
Visitor more values vegetable, thus it is this assign for different item information in Customer Information with its significance level to weight by way of,
The restaurant that can be further ensured that recommendation is the restaurant for utmostly meeting customer demand.
In addition, carry out weighted value imparting when, be also possible to be according to the significance level of every terms of information in Customer Information
It sorts, then the difference of the position obtained based on sequence is also with regard to the difference of respective weights value, so as to what is obtained according to sequence
Most important every terms of information screens restaurant, and the restaurant for not meeting most important every terms of information is screened out, if there is also big
In the restaurant of predetermined amount, then restaurant is screened further according to the second important every terms of information, and so on, finishing screen is selected most
The restaurant for being suitble to current customer to require.Certainly other modes set according to actual needs, also in the protection scope of the application
Within.
A kind of restaurant intelligent recommendation method provided by the embodiments of the present application, the Customer Information based on the purpose customer
Corresponding restaurant is filtered out as candidate restaurant, may include:
The Customer Information of the purpose customer is input in the restaurant intelligent recommendation model being pre-created, and described in determination
The restaurant information of restaurant intelligent recommendation model output corresponds to restaurant as candidate restaurant；
The process that the restaurant intelligent recommendation model is pre-created includes:
Acquisition includes the training information collection of multiple training informations, and based on the default classification mould of the training information collection training
Type obtains restaurant intelligent recommendation model, the training information include history have dinner customer Customer Information and corresponding restaurant of having dinner
Restaurant information.
Wherein, default classifier can be support vector machines, k nearest neighbor etc., can specifically choose according to actual needs.Its
In, for realizing the item for the information that the Customer Information of the customer of restaurant intelligent recommendation model and the restaurant information in restaurant of having dinner include
The mesh project for including corresponding with purpose customer is identical, smooth with the restaurant intelligent recommendation model got using training information training
Filter out candidate restaurant.It is further to note that determining candidate restaurant corresponding with the Customer Information of purpose customer in the application
Mode can there are many, corresponding informance such as can be manually chosen and inputted by staff, can also be by customer's in history
Customer Information and the restaurant information in corresponding restaurant of having dinner form corresponding relationship and then based on corresponding relationship determinations and purpose customer
Corresponding candidate restaurant etc., within the scope of protection of this application, is realized by the way of disaggregated model, Neng Gou in the application
After Customer Information is input to restaurant intelligent recommendation model, it is quickly obtained the restaurant information in candidate restaurant, improves method reality
Existing efficiency.
A kind of restaurant intelligent recommendation method provided by the embodiments of the present application, after determining the purpose restaurant, by institute
It states order placement information to be sent to before the purpose restaurant, can also include:
Determine that the purpose customer is presently in the route information of position to purpose restaurant present position, and will be described
Route information is added into the order placement information.
It should be noted that being also based on purpose customer after determining purpose restaurant and being presently in position and purpose
Restaurant present position is purpose customer planning department route information, which may include road of the purpose customer to purpose restaurant
On traffic information, Weather information, total distance, it is estimated need time etc., can specifically be set according to actual needs.It should
Route information returns to purpose customer, it can be made to go on a journey based on the route information, and the route information is added to the letter that places an order
In breath, enable to purpose restaurant to be based on route information and estimate purpose customer and reach the time, so be ready in advance eating surroundings,
Have dinner food, for parking environment of customer of driving etc., when thus customer reaches restaurant can cracking dining of serving, save objective
Family and restaurant both sides time.
The order placement information is sent to the purpose and eaten by a kind of restaurant intelligent recommendation method provided by the embodiments of the present application
After shop, can also include:
The purpose customer the purpose restaurant complete just it is postprandial, obtain the purpose customer for this have dinner into
The dining information that the evaluation information of row evaluation and the purpose customer this time have dinner；
The evaluation information is added into the restaurant information in the purpose restaurant, and the dining information is added to institute
It states in the history dining information of purpose customer.
Wherein, purpose customer for this evaluation information evaluated of having dinner may include to the evaluation information of vegetable,
Evaluation information to service and the evaluation information etc. to restaurant environment can be evaluated with modes such as grade, scores.To
It is just postprandial in customer's completion, evaluation information is added to the restaurant information in purpose restaurant, dining information is added to purpose customer
History dining information guarantee subsequent real using corresponding information to realize to the real-time update of Customer Information and restaurant information
Effectiveness of information and accuracy when existing restaurant recommendation function.
A kind of restaurant intelligent recommendation method provided by the embodiments of the present application, by the evaluation information and the dining information
After being added into corresponding information, can also include:
The restaurant information of the Customer Information of the purpose customer and the purpose restaurant is added to the training information collection
In, disaggregated model is preset using the training information collection training and obtains new restaurant intelligent recommendation model, and is based on when needed
The screening in the new restaurant intelligent recommendation model realization candidate restaurant.
It should be noted that being added the restaurant information of the Customer Information of purpose customer and purpose restaurant to training information collection
In, it is actually the real-time update to training information collection, to guarantee the validity of training information collection, and then guarantees by training information collection
The validity for the restaurant intelligent recommendation model that training obtains.Wherein, the restaurant got using the training of updated training information
Intelligent recommendation model, the restaurant intelligent recommendation model for replacing last time training to obtain, realizes corresponding restaurant intelligent recommendation function
Can, to realize the real-time update of restaurant intelligent recommendation model.
A kind of restaurant intelligent recommendation method provided by the embodiments of the present application, in the Customer Information for getting the purpose customer
Later, can also include:
It is confirmed whether that there are customized demand information with the purpose customer, if it is, the customized demand information is added
Enter into the Customer Information of the purpose customer, and determines that the weight of the customized demand information is greater than its in the Customer Information
The weight of his every terms of information；If it is not, then determining without updating the Customer Information.
It should be noted that customer, there may be customized demand information, customized demand information is to preset customer
When each project that information includes, the information that is not included in each project.In simple terms, every letter that Customer Information includes
Breath may be considered essential information, and customizing demand information is additional information, needs dining table as customized demand information can be
It is near the window or food is needed to put the information of the special requirements such as mustard.Confirm in purpose customer there are when customized demand information, needs
The customized demand information is added into Customer Information, and setting maximum for its weight is because usual customized demand information is
The information that customer takes notice of the most, therefore set maximum for its weight and enable to the restaurant selected that can utmostly meet
Customer demand.In addition, corresponding, in customer, there are after customized demand information, need the information project by customized demand information to add
Enter into each training information of training restaurant intelligent recommendation model, to guarantee that it can be effectively for there are customized demand information
Customer provides restaurant recommendation function.
It, can meeting according to candidate restaurant and Customer Information it is further to note that after determining candidate restaurant
Degree on earth shows corresponding restaurant information by height, so that customer checks and chooses restaurant of having dinner.And it is waited filtering out
After selecting restaurant, recommend purpose customer restaurant information can also include trip information (route information, travel time selection etc.),
Vegetable recommends and subsequent scheme of having dinner etc., and vegetable recommendation can be when specific, such as to care for when customer's time is tighter
Visitor recommends several combined set meals, saves customer and selects the meal time；And it is it that the subsequent scheme of having dinner, which can be according to the destination of customer,
The restaurant on route for recommending customer that need to pass through is for selection；To further promote the experience of customer.
The embodiment of the present application also provides a kind of restaurant intelligent recommendation devices, as shown in Fig. 2, may include:
Module 11 is obtained, is used for: receiving the demand of having dinner of purpose customer, and obtains the Customer Information of the purpose customer,
The Customer Information includes the personal information, work financial information and history dining information of corresponding customer；
Recommending module 12, is used for: it is candidate restaurant that the Customer Information based on the purpose customer, which filters out corresponding restaurant,
And the restaurant information in the candidate restaurant is recommended into the purpose customer；
Lower list module 13, is used for: determining that the restaurant that the purpose customer selects for purpose restaurant, obtains the purpose customer
The order placement information in the corresponding purpose restaurant is simultaneously sent to the purpose restaurant, and the order placement information includes demand information of having dinner.
A kind of restaurant intelligent recommendation device provided by the embodiments of the present application can also include:
Power module is assigned, is used for: after the Customer Information for obtaining the purpose customer, and filtering out the candidate restaurant
Before, the significance level for the every terms of information for including in the Customer Information based on the purpose customer is assigned for every terms of information and being corresponded to
Weight, and the corresponding weight of every terms of information is added separately in the Customer Information of the purpose customer.
A kind of restaurant intelligent recommendation device provided by the embodiments of the present application, the recommending module may include:
Screening unit is used for: the Customer Information of the purpose customer is input to the restaurant intelligent recommendation mould being pre-created
In type, and determine that the restaurant information of the restaurant intelligent recommendation model output corresponds to restaurant as candidate restaurant；
It is corresponding, can also include:
First training module, is used for: acquisition includes the training information collection of multiple training informations, and based on the training letter
The default disaggregated model of breath collection training obtains restaurant intelligent recommendation model, and the training information includes that history is had dinner customer's letter of customer
Breath and the restaurant information in corresponding restaurant of having dinner.
A kind of restaurant intelligent recommendation device provided by the embodiments of the present application can also include:
First adding module, is used for: after determining the purpose restaurant, the order placement information being sent to the mesh
Restaurant before, determine that the purpose customer is presently in the route information of position to purpose restaurant present position, and will
The route information is added into the order placement information.
A kind of restaurant intelligent recommendation device provided by the embodiments of the present application can also include:
Second adding module, is used for: after the order placement information is sent to the purpose restaurant, in the purpose customer
It is completed in the purpose restaurant just postprandial, obtains the purpose customer for this evaluation information evaluated and described of having dinner
The dining information that purpose customer this time has dinner；The evaluation information is added into the restaurant information in the purpose restaurant, and will
The dining information is added into the history dining information of the purpose customer.
A kind of restaurant intelligent recommendation device provided by the embodiments of the present application can also include:
Second training module, is used for: it is being added into corresponding information in the evaluation information and the dining information
Afterwards, the restaurant information of the Customer Information of the purpose customer and the purpose restaurant is added to the training information and is concentrated, benefit
Disaggregated model, which is preset, with the training information collection training obtains new restaurant intelligent recommendation model, and new based on this when needed
The screening in restaurant intelligent recommendation model realization candidate restaurant.
A kind of restaurant intelligent recommendation device provided by the embodiments of the present application can also include:
Third adding module, is used for: true with the purpose customer after the Customer Information for getting the purpose customer
Recognize with the presence or absence of customized demand information, if it is, being added the customized demand information to customer's letter of the purpose customer
In breath, and determine that the weight of the customized demand information is greater than the weight of other every terms of information in the Customer Information；If not,
It then determines without updating the Customer Information.
The embodiment of the present application also provides a kind of restaurant intelligent recommendation equipment, as shown in figure 3, may include:
Memory 22, for storing computer program；
Processor 21, performs the steps of when for executing the computer program
The demand of having dinner of purpose customer is received, and obtains the Customer Information of purpose customer, Customer Information includes corresponding customer
Personal information, work financial information and history dining information；Customer Information based on purpose customer filters out corresponding restaurant
For candidate restaurant, and the restaurant information in candidate restaurant is recommended into purpose customer；For the purpose of the restaurant for determining purpose customer selection
Restaurant obtains purpose customer and corresponds to the order placement information in purpose restaurant and be sent to purpose restaurant, and order placement information includes demand of having dinner
Information.
A kind of restaurant intelligent recommendation equipment provided by the embodiments of the present application, the processor executes to be stored in the memory
Computer subprogram when, following steps specifically may be implemented: after the Customer Information for obtaining the purpose customer, and sieving
Before selecting the candidate restaurant, the significance level for the every terms of information for including in the Customer Information based on the purpose customer is
Every terms of information assigns corresponding weight, and the corresponding weight of every terms of information is added separately to the Customer Information of the purpose customer
In.
A kind of restaurant intelligent recommendation equipment provided by the embodiments of the present application, the processor executes to be stored in the memory
Computer subprogram when, following steps specifically may be implemented: the Customer Information of the purpose customer being input to and is pre-created
Restaurant intelligent recommendation model in, and determine that the restaurant information of restaurant intelligent recommendation model output correspond to restaurant and eats to be candidate
Shop；And acquisition includes the training information collection of multiple training informations, and based on the default classification mould of the training information collection training
Type obtains restaurant intelligent recommendation model, the training information include history have dinner customer Customer Information and corresponding restaurant of having dinner
Restaurant information.
A kind of restaurant intelligent recommendation equipment provided by the embodiments of the present application, the processor executes to be stored in the memory
Computer subprogram when, following steps specifically may be implemented: after determining the purpose restaurant, by the order placement information
It is sent to before the purpose restaurant, determines that the purpose customer is presently in the road of position to purpose restaurant present position
Line information, and the route information is added into the order placement information.
A kind of restaurant intelligent recommendation equipment provided by the embodiments of the present application, the processor executes to be stored in the memory
Computer subprogram when, following steps specifically may be implemented: after the order placement information is sent to the purpose restaurant,
The purpose customer completes just postprandial in the purpose restaurant, obtains the purpose customer for this commenting of being evaluated of having dinner
The dining information that valence information and the purpose customer this time have dinner；The evaluation information is added to the restaurant in the purpose restaurant
In information, and the dining information is added into the history dining information of the purpose customer.
A kind of restaurant intelligent recommendation equipment provided by the embodiments of the present application, the processor executes to be stored in the memory
Computer subprogram when, following steps specifically may be implemented: being added by the evaluation information and the dining information to right
After in the information answered, the restaurant information of the Customer Information of the purpose customer and the purpose restaurant is added to the training
Information is concentrated, and is preset disaggregated model using the training information collection training and is obtained new restaurant intelligent recommendation model, and is needing
When the screening based on the new restaurant intelligent recommendation model realization candidate restaurant.
A kind of restaurant intelligent recommendation equipment provided by the embodiments of the present application, the processor executes to be stored in the memory
Computer subprogram when, following steps specifically may be implemented: after the Customer Information for getting the purpose customer, with institute
It states purpose customer and is confirmed whether that there are customized demand information, if it is, being added the customized demand information to the purpose
In the Customer Information of customer, and determine that the weight of the customized demand information is greater than other every terms of information in the Customer Information
Weight；If it is not, then determining without updating the Customer Information.
The embodiment of the present application also provides a kind of computer readable storage medium, deposited on the computer readable storage medium
Computer program is contained, the computer program performs the steps of when being executed by processor
The demand of having dinner of purpose customer is received, and obtains the Customer Information of purpose customer, Customer Information includes corresponding customer
Personal information, work financial information and history dining information；Customer Information based on purpose customer filters out corresponding restaurant
For candidate restaurant, and the restaurant information in candidate restaurant is recommended into purpose customer；For the purpose of the restaurant for determining purpose customer selection
Restaurant obtains purpose customer and corresponds to the order placement information in purpose restaurant and be sent to purpose restaurant, and order placement information includes demand of having dinner
Information.
A kind of computer readable storage medium provided by the embodiments of the present application saves in the computer readable storage medium
Computer subprogram when being executed by processor, following steps specifically may be implemented: in the customer's letter for obtaining the purpose customer
After breath, and before filtering out the candidate restaurant, the every terms of information that includes in the Customer Information based on the purpose customer
Significance level, assign corresponding weight for every terms of information, and the corresponding weight of every terms of information is added separately to the purpose
In the Customer Information of customer.
A kind of computer readable storage medium provided by the embodiments of the present application saves in the computer readable storage medium
Computer subprogram when being executed by processor, following steps specifically may be implemented: the Customer Information of the purpose customer is defeated
Enter into the restaurant intelligent recommendation model being pre-created, and determines that the restaurant information of the restaurant intelligent recommendation model output is corresponding
Restaurant is candidate restaurant；And acquisition includes the training information collection of multiple training informations, and is assembled for training based on the training information
Practice default disaggregated model and obtain restaurant intelligent recommendation model, the training information includes that history is had dinner the Customer Information of customer and right
Should have dinner the restaurant information in restaurant.
A kind of computer readable storage medium provided by the embodiments of the present application saves in the computer readable storage medium
Computer subprogram when being executed by processor, following steps specifically may be implemented: after determining the purpose restaurant, will
The order placement information is sent to before the purpose restaurant, determines that the purpose customer is presently in position to the purpose restaurant
The route information of present position, and the route information is added into the order placement information.
A kind of computer readable storage medium provided by the embodiments of the present application saves in the computer readable storage medium
Computer subprogram when being executed by processor, following steps specifically may be implemented: the order placement information is sent to the mesh
Restaurant after, the purpose customer the purpose restaurant complete just it is postprandial, obtain the purpose customer for this just
The dining information that the evaluation information and the purpose customer that meal is evaluated this time are had dinner；The evaluation information is added to described
In the restaurant information in purpose restaurant, and the dining information is added into the history dining information of the purpose customer.
A kind of computer readable storage medium provided by the embodiments of the present application saves in the computer readable storage medium
Computer subprogram when being executed by processor, following steps specifically may be implemented: by the evaluation information and described having dinner
After information is added into corresponding information, the restaurant information of the Customer Information of the purpose customer and the purpose restaurant is added
Enter to the training information and concentrate, presets disaggregated model using the training information collection training and obtain new restaurant intelligent recommendation mould
Type, and the screening when needed based on the new restaurant intelligent recommendation model realization candidate restaurant.
A kind of computer readable storage medium provided by the embodiments of the present application saves in the computer readable storage medium
Computer subprogram when being executed by processor, following steps specifically may be implemented: in the customer for getting the purpose customer
After information, it is confirmed whether that there are customized demand information with the purpose customer, if it is, the customized demand information is added
Enter into the Customer Information of the purpose customer, and determines that the weight of the customized demand information is greater than its in the Customer Information
The weight of his every terms of information；If it is not, then determining without updating the Customer Information.
It should be noted that a kind of restaurant intelligent recommendation device provided by the embodiments of the present application, equipment and computer-readable
The explanation of relevant portion refers in a kind of restaurant intelligent recommendation method provided by the embodiments of the present application and corresponds to portion in storage medium
The detailed description divided, details are not described herein.In addition in above-mentioned technical proposal provided by the embodiments of the present application with it is right in the prior art
The consistent part of technical solution realization principle and unspecified is answered, in order to avoid excessively repeat.
The foregoing description of the disclosed embodiments makes those skilled in the art can be realized or use the application.To this
A variety of modifications of a little embodiments will be apparent for a person skilled in the art, and the general principles defined herein can
Without departing from the spirit or scope of the application, to realize in other embodiments.Therefore, the application will not be limited
It is formed on the embodiments shown herein, and is to fit to consistent with the principles and novel features disclosed in this article widest
Range.
Claims (10)
1. a kind of restaurant intelligent recommendation method characterized by comprising
The demand of having dinner of purpose customer is received, and obtains the Customer Information of the purpose customer, the Customer Information includes corresponding to
Personal information, work financial information and the history dining information of customer；
Customer Information based on the purpose customer filters out corresponding restaurant as candidate restaurant, and by the meal in the candidate restaurant
Shop information recommendation gives the purpose customer；
The restaurant that the purpose customer selects is determined as purpose restaurant, the purpose customer is obtained and corresponds under the purpose restaurant
Single information is simultaneously sent to the purpose restaurant, and the order placement information includes demand information of having dinner.
2. the method according to claim 1, wherein after the Customer Information for obtaining the purpose customer, and
Before filtering out the candidate restaurant, further includes:
The significance level for the every terms of information for including in Customer Information based on the purpose customer assigns corresponding for every terms of information
Weight, and the corresponding weight of every terms of information is added separately in the Customer Information of the purpose customer.
3. according to the method described in claim 2, it is characterized in that, the Customer Information based on the purpose customer filters out
Corresponding restaurant is candidate restaurant, comprising:
The Customer Information of the purpose customer is input in the restaurant intelligent recommendation model being pre-created, and determines the restaurant
The restaurant information of intelligent recommendation model output corresponds to restaurant as candidate restaurant；
The process that the restaurant intelligent recommendation model is pre-created includes:
Acquisition includes the training information collection of multiple training informations, and presets disaggregated model based on the training information collection training and obtain
To restaurant intelligent recommendation model, the training information includes that history is had dinner the Customer Information of customer and the restaurant in corresponding restaurant of having dinner
Information.
4. according to the method described in claim 3, it is characterized in that, placing an order after determining the purpose restaurant by described
Information is sent to before the purpose restaurant, further includes:
Determine that the purpose customer is presently in the route information of position to purpose restaurant present position, and by the route
Information is added into the order placement information.
5. according to the method described in claim 4, it is characterized in that, by the order placement information be sent to the purpose restaurant it
Afterwards, further includes:
It completes in the purpose customer in the purpose restaurant just postprandial, obtains the purpose customer and have dinner and comment for this
The dining information that the evaluation information of valence and the purpose customer this time have dinner；
The evaluation information is added into the restaurant information in the purpose restaurant, and the dining information is added to the mesh
Customer history dining information in.
6. according to the method described in claim 5, it is characterized in that, by the evaluation information and the dining information be added to
After in corresponding information, further includes:
The restaurant information of the Customer Information of the purpose customer and the purpose restaurant is added to the training information and is concentrated, benefit
Disaggregated model, which is preset, with the training information collection training obtains new restaurant intelligent recommendation model, and new based on this when needed
The screening in restaurant intelligent recommendation model realization candidate restaurant.
7. according to the described in any item methods of claim 2 to 6, which is characterized in that in the customer for getting the purpose customer
After information, further includes:
Be confirmed whether that there are customized demand information with the purpose customer, if it is, by the customized demand information be added to
In the Customer Information of the purpose customer, and it is each to determine that the weight of the customized demand information is greater than other in the Customer Information
The weight of item information；If it is not, then determining without updating the Customer Information.
8. a kind of restaurant intelligent recommendation device characterized by comprising
Module is obtained, is used for: receiving the demand of having dinner of purpose customer, and obtains the Customer Information of the purpose customer, the Gu
Objective information includes the personal information, work financial information and history dining information of corresponding customer；
Recommending module is used for: the Customer Information based on the purpose customer filters out corresponding restaurant as candidate restaurant, and by institute
The restaurant information for stating candidate restaurant recommends the purpose customer；
Lower list module, is used for: determining the restaurant that the purpose customer selects as purpose restaurant, obtain the purpose customer and correspond to institute
It states the order placement information in purpose restaurant and is sent to the purpose restaurant, the order placement information includes demand information of having dinner.
9. a kind of restaurant intelligent recommendation equipment characterized by comprising
Memory, for storing computer program；
Processor realizes the restaurant intelligent recommendation side as described in any one of claim 1 to 7 when for executing the computer program
The step of method.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program realizes the restaurant intelligent recommendation method as described in any one of claim 1 to 7 when the computer program is executed by processor
The step of.
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| CN201811287504.0A CN109471973A (en) | 2018-10-31 | 2018-10-31 | A kind of restaurant intelligent recommendation method, apparatus, equipment and storage medium |
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| CN110809024A (en) * | 2019-09-27 | 2020-02-18 | 口碑(上海)信息技术有限公司 | Service resource providing method and device and subscription package providing method and device |
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Application publication date: 20190315 |