CN108415970A - Retrieval result sort method, device, electronic equipment and storage medium - Google Patents
Retrieval result sort method, device, electronic equipment and storage medium Download PDFInfo
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Abstract
The present invention provides a kind of retrieval result sort method, device, electronic equipment and storage mediums, are related to search technique field.The method includes：Receive searching request, user identifier and end article information are carried in described search request, multiple search results are obtained according to the end article information search, determine the product features of corresponding goods in each search result, user characteristics are obtained according to the user identifier, according to the product features of corresponding goods in the user characteristics and each search result, determine consumption intention data of the user for corresponding goods in each search result, according to the consumption intention data, the multiple search result is ranked up.The present invention can be according to user to the different commodity searched consumption demand, the search result that targetedly the opposite direction user provides is ranked up, the accuracy of the accuracy and the search result provided that are ranked up to search result is provided, and then also improves the accuracy for providing a user commodity.
Description
Technical field
The present invention relates to search technique field, more particularly to a kind of retrieval result sort method, device, electronic equipment and
Storage medium.
Background technology
With the development of electronic technology, the application of e-commerce is also more and more extensive.Businessman can pass through the form of webpage
Commodity are provided a user, user can search for required commodity by accessing webpage.Due to that may be wrapped in search result
Include multiple commodity, and would generally be variant between each commodity, for example, commodity quality etc., so in order to improve the standard for providing commodity
True property, there is an urgent need for a kind of search result ordering methods.
It in the prior art, can be by counting the sales volume of corresponding goods in each search result, user for each search result
The number of reviews and collection number of middle corresponding goods.It, can be according to the sales volume, evaluation number or collection number when providing commodity
Mesh is ranked up corresponding goods in each search result, and the commodity after sequence are supplied to user.
Although the prior art can be ranked up search result, improves to a certain extent and the accurate of commodity is provided
Property, but due to also usually there will be difference, such as taste, hobby, purchasing power etc., therefore different user between different users
Demand to commodity is also different, and in the prior art, for any user, the sortord of the commodity provided is identical
, also it is difficult to meet without user the needs of to commodity certainly, the accuracy for providing commodity is fairly limited.
Invention content
In view of the above problems, it is proposed that the present invention overcoming the above problem in order to provide one kind or solves at least partly
State retrieval result sort method, device, electronic equipment and the storage medium of problem.
On the one hand, the present invention provides a kind of search result ordering methods, including：
Searching request is received, user identifier and end article information are carried in described search request；
Multiple search results are obtained according to the end article information search, determine the quotient of corresponding goods in each search result
Product feature；
User characteristics are obtained according to the user identifier；
According to the product features of corresponding goods in the user characteristics and each search result, determine that user ties for each search
The consumption intention data of corresponding goods in fruit；
According to the consumption intention data, the multiple search result is ranked up.
Optionally, the product features according to corresponding goods in the user characteristics and each search result, determine user
Consumption intention data for corresponding goods in each search result includes：
According to the product features of corresponding goods in the user characteristics and each search result, pass through the first machine being pre-created
Device learning model determines the user for the payment probability of corresponding goods in each search result, the first machine learning mould
Type is used to determine payment probability according to the product features of corresponding goods in user characteristics and each search result；
According to the product features of corresponding goods in the user characteristics and each search result, pass through the second machine being pre-created
Device learning model determines the user for the reimbursement probability of corresponding goods in each search result, the second machine learning mould
Type is used to determine reimbursement probability according to the product features of corresponding goods in user characteristics and each search result；
According to the payment probability and the reimbursement probability, determine the user for corresponding goods in each search result
Probability is consumed, is the consumption intention data by the consumption determine the probability.
Optionally, in the product features according to corresponding goods in the user characteristics and each search result, by pre-
The first machine learning model first created, before determining the user for the payment probability of corresponding goods in each search result,
Further include：
The history Access Events that multiple users are directed to commodity are obtained, the history Access Events include payment event and unpaid
Money event；
User characteristics and product features are extracted from the history Access Events, and calculate each use according to history Access Events
Payment probability of the family to commodity；
According to the product features, user characteristics and the payment probability calculated extracted, first engineering is created
Practise model.
Optionally, in the product features according to corresponding goods in the user characteristics and each search result, by pre-
The second machine learning model first created, before determining the user for the reimbursement probability of corresponding goods in each search result,
Further include：
The history Access Events that multiple users are directed to commodity are obtained, the history Access Events include refund event and do not move back
Money event；
User characteristics and product features are extracted from the history Access Events, and calculate each use according to history Access Events
Reimbursement probability of the family to commodity；
According to the product features, user characteristics and the reimbursement probability calculated extracted, second engineering is created
Practise model.
Optionally, the product features according to corresponding goods in the user characteristics and each search result, determine user
Consumption intention data for corresponding goods in each search result includes：
According to the product features of corresponding goods in the user characteristics and each search result, pass through the third machine being pre-created
Device learning model determines the user for the payment probability of corresponding goods in each search result, the third machine learning mould
Type is used to determine consumption probability according to the product features of corresponding goods in user characteristics and each search result；
It is the consumption intention data by the consumption determine the probability.
Optionally, in the product features according to corresponding goods in the user characteristics and each search result, by pre-
The third machine learning model first created, before determining the user for the payment probability of corresponding goods in each search result,
Further include：
The history Access Events that multiple users are directed to commodity are obtained, the history Access Events include payment event, unpaid
Money event, refund event and non-refund event；
User characteristics and product features are extracted from the history Access Events, and calculate each use according to history Access Events
Consumption probability of the family to commodity；
According to the product features, user characteristics and the consumption probability calculated extracted, the third engineering is created
Practise model.
Optionally, further include：
Environmental characteristic is obtained, the environmental characteristic includes the user respectively between corresponding goods in each search result
Distance；
It is described according to the consumption intention data, to the multiple search result be ranked up including：
In conjunction with the environmental characteristic and the consumption intention data, the multiple commodity are ranked up.
Optionally, described that multiple search results are obtained according to the end article information search, it determines in each search result
The product features of corresponding goods include：
Determine product features of the corresponding goods in the second preset time period in each search result, second preset time
Section is using current time as the period in the third preset duration of starting point；
It is described to include according to user identifier acquisition user characteristics：
The user characteristics in second preset time period are obtained according to the user identifier.
On the other hand, the present invention provides a kind of search results ranking devices, including：
Receiving module carries user identifier and end article information for receiving searching request in described search request；
First determining module determines each search for obtaining multiple search results according to the end article information search
As a result the product features of middle corresponding goods；
First acquisition module, for obtaining user characteristics according to the user identifier；
Second determining module, for the product features according to corresponding goods in the user characteristics and each search result, really
Determine consumption intention data of the user for corresponding goods in each search result；
Sorting module, for according to the consumption intention data, being ranked up to the multiple search result.
On the other hand, the present invention provides a kind of electronic equipment, including memory, processor and storage are on a memory simultaneously
The computer program that can be run on a processor, the processor are realized when executing described program such as any one of aforementioned the method
The step of.
On the other hand, the present invention provides a kind of computer readable storage medium, it is stored thereon with computer program, the journey
It is realized when sequence is executed by processor such as the step of any one of aforementioned the method.
In embodiments of the present invention, due to may be had differences between different users and different commodity, from
And different users has different consumption demands to different commodity, so the user for submitting searching request, Neng Gougen
Multiple searching results are obtained according to the end article information search carried in searching request, are marked according to the user carried in searching request
Knowledge gets corresponding user characteristics, then according to the product features of corresponding goods in the user characteristics and each search result, really
The fixed user is directed to the consumption intention data of corresponding goods in each search result, and then can will be tied to each search according to the user
The consumption intention data of corresponding goods is ranked up search result in fruit, that is to say difference that can be according to user to searching
The consumption demand of commodity, the search result that targetedly the opposite direction user provides are ranked up, improve and carried out to search result
The accuracy of the accuracy of sequence and the search result provided, and then also improve the accuracy for providing a user commodity.
Above description is only the general introduction of technical solution of the present invention, in order to better understand the technical means of the present invention,
And can be implemented in accordance with the contents of the specification, and in order to allow above and other objects of the present invention, feature and advantage can
It is clearer and more comprehensible, below the special specific implementation mode for lifting the present invention.
Description of the drawings
By reading the detailed description of hereafter preferred embodiment, various other advantages and benefit are common for this field
Technical staff will become clear.Attached drawing only for the purpose of illustrating preferred embodiments, and is not considered as to the present invention
Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings：
Fig. 1 is a kind of step flow chart of the search result ordering method provided according to embodiments of the present invention；
Fig. 2 is the step flow chart of another search result ordering method provided according to embodiments of the present invention；
Fig. 3 is a kind of structure diagram of the search results ranking device provided according to embodiments of the present invention.
Specific implementation mode
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
Completely it is communicated to those skilled in the art.
Embodiment one
Referring to Fig.1, a kind of step flow chart of search result ordering method in the embodiment of the present invention, the search are shown
Result ordering method includes：
Step 101, searching request is received, user identifier and end article information are carried in described search request.
The search result ordering method that the embodiment of the present invention is provided can be applied in cloud platform, and user can pass through net
Page application or client submit searching request to the cloud platform, and user identifier and end article letter are carried in the searching request
Breath, so that the cloud platform can be scanned for according to the end article information, and provides search result according to the user identifier
To user.
The user identifier is used for one user of unique mark, which can be user's name or User ID
(Identification, identity card) etc..
The end article information is keyword for obtaining required commodity, for example, the end article information can be with
At least one of the title of the affiliated businessman of the type of merchandise, commodity.
For example, receiving searching request 1, searching request includes " User ID 1 " and " quick hotel ", wherein User ID 1
For the user identifier of the user of submission searching request 1, quick hotel is the type of merchandise.
Step 102, multiple search results are obtained according to the end article information search, determined corresponding in each search result
The product features of commodity.
Due to being had differences between no commodity, such as quality, price, material etc., so as to meet user demand
Program also can be different, therefore, for the ease of being ranked up to search result for the user of searching request is submitted, Jin Erti
Height provides the accuracy of search result and commodity, can in each search result corresponding goods product features.
The commodity with the end article information matches can be obtained from least one businessman according to the end article information,
To obtain search result, each search result can correspond to a commodity.
Product features are feature possessed by commodity, may include in reimbursement amount, sales volume, evaluation number, the type of merchandise
At least one, certainly, in practical applications, which can also include other features of commodity, such as the affiliated quotient of commodity
The title of family, the reimbursement rate in geographical location (address or longitude and latitude etc.), commodity residing for businessman click number, collection number, price
Deng.Certainly, in practical applications, in order to improve the accuracy of determining product features, and then improve and search result is ranked up
Accuracy, to accurately providing a user commodity, which may include commodity subcharacter, for example, commodity
Reimbursement rate may include the client from different clients type (such as Android client and apple client) to the commodity
The reimbursement rate of reimbursement, weekend and non-all foot couples commodity are carried out to carry out the reimbursement rate of reimbursement, (for example roll into a ball for different Payment Types
Purchase or forward purchasing) reimbursement rate.
Wherein it is possible to according to the commodity sign of corresponding goods in each search result, the history obtained for the commodity accesses
Event, the history Access Events may include click event, payment event, arrearage event, refund event, non-refund event,
Evaluation event such as does not evaluate event, collection event, does not collect event at the history Access Events, passes through and calculates different history and access
The number of event is so that it is determined that different product features, that is to say that extraction obtains the commodity spy of the commodity from history Access Events
Sign.For example, the number for the click event being calculated is the click number of the commodity, what is be calculated searches the number of Tibetan event
The as collection number of the commodity, the number for the refund event being calculated, the number with refund event and non-refund event
The sum of between ratio, the as reimbursement rate of the commodity.Certainly, in embodiments of the present invention, it is preferred that for will not be because of going through
History Access Events and the product features that change can also be obtained by obtaining pre-stored product features, for example, the type of merchandise,
Geographical location etc. residing for the title of the affiliated businessman of commodity and businessman can be the commodity class by receiving the commodity that businessman submits
Geographical location obtains residing for the title of the affiliated businessman of type, commodity and businessman.
Commodity sign is used for one commodity of unique mark, and commodity sign can be title or ID of commodity etc..
History Access Events are user characteristics, the commodity detected when user accesses to the commodity to the user
The event that product features record generates.For example, if detecting that user clicks the commodity, it can be recorded as click event, it should
May include the user characteristics of the user, the type of merchandise of the commodity, the title of the affiliated businessman of commodity or businessman in click event
Residing geographical location etc.；If detecting that the user is to click the commodity and successful payment, payment event can be recorded as；Such as
Fruit detects that the user is to click the commodity but arrearage success, can be recorded as arrearage event；If detecting the use
Family is the commodity successful payment and reimbursement success, can be recorded as refund event；If detecting that the user pays the bill for the commodity
Success but non-reimbursement success, can be recorded as non-refund event.
In embodiments of the present invention, it is preferred that reimbursement refers in the first preset duration after commodity successful payment
Reimbursement.
First preset duration can be determined that the first preset duration can be 1 week or 1 month etc. by cloud platform.
In embodiments of the present invention, it is preferred that further include other feature, such as environmental characteristic in history Access Events.It should
Environmental characteristic is used to illustrate the environment in the user or each search result residing for corresponding goods, such as current time and user
Respectively with the distance between corresponding goods, weather etc. in each search result.
Wherein, the distance between the user and corresponding goods in each search result can be the geographical position residing for the user
Set respectively with the distance between the geographical location residing for corresponding goods in each search result.
Step 103, user characteristics are obtained according to the user identifier.
Since there is also differences between different users, correspondingly, to the desirability of different commodity in search result
It can difference therefore for the ease of being ranked up to search result for the user of searching request is submitted, and then carry
For the accuracy of search result and commodity, user characteristics can be obtained.
User characteristics are feature possessed by user, which may include user activity, user gradation, user
At least one of consuming capacity, user's reimbursement number.Certainly, in practical applications, which can also include other
Feature, for example, geographical location residing for the client type of the client of the user, user.
User activity can be by buying number and the last time of commodity according to user in the first preset time period
Purchase commodity determine that the user activity may be greater than 0 and be less than or equal to 1 apart from second preset duration at current time
Numerical value.Wherein, the first preset time period can be the period of the second preset duration before current time, the first preset time
Section or the second preset duration can be determined by the cloud platform, for example, the second preset duration can be nearest 30 days, 60 days or 90
It etc..
For example, formula 1 can be passed through：ACT=a*N1+b*N2 calculates user activity.Wherein, ACT indicates that user lives
Jerk, N1 buy the number of commodity in the first preset time period, and N2 is the first duration, and a is to be purchased in the first preset time period
The weights of the number of commodity are bought, b is the weights of the second preset duration, and the value of a and b can be by pre-setting to obtain.
User gradation can buy commodity according to user and the sum of pay the bill and payment number determination, which can be with
It is greater than 0 numerical value for being less than or equal to 10, and commodity number of the sum of paying the bill and pay the bill is bought according to user and determines user etc.
The mode of grade, can determine the user with number and the first duration of commodity is bought in the first preset time period according to user
The mode of liveness is similar, no longer repeats one by one herein.
Customer consumption ability can be bought at least one of commodity are paid the bill and/or average value by user.
User's reimbursement number can include that the number of the refund event of the user identifier of the user is determined by statistics.
In addition, in practical applications, step 103 can also be first carried out and execute step 102 again, alternatively, being performed simultaneously step
102 or 103, it that is to say, the embodiment of the present invention is not specifically limited the execution order of step 102 and 103.
Step 104, according to the product features of corresponding goods in the user characteristics and each search result, determine that user is directed to
The consumption intention data of corresponding goods in each search result.
It, can be according to this to accurately provide a user commodity in order to be accurately ranked up to search result
The product features of corresponding goods in the user characteristics of user and each search result determine the user for right in each search result
The consumption intention data for answering commodity, that is to say, can be directed to different users, determine the user for corresponding in each search result
The consumption intention data of commodity.
Consumption intention data is to carry out consuming related data to commodity with user, so as to illustrate user to commodity
Consumption demand or tendency, the consumption intention data can be the payment probability that the user pays the bill to the commodity.
Wherein it is possible to according to the product features of corresponding goods in the user characteristics and each search result, by creating in advance
The first machine learning model built determines the user for the payment probability of corresponding goods in each search result, the first machine
Learning model is used to determine payment probability according to the product features of corresponding goods in user characteristics and each search result.
For example, the user characteristics of user 1 include user gradation is 3, client type is Android, user activity 0.6,
The product features of commodity 1 include that nearest 90 days reimbursement numbers are 10, then by the first machine learning model, according to the use of user 1
The product features of family feature and commodity 1, it is 50% to the payment probability of commodity 1 that user 1, which is calculated,.The user of user 2 is special
Sign include user gradation be 1, client type is Android, user activity 0.1 then passes through the first machine learning model, root
According to the product features of the user characteristics and commodity 1 of user 2, it is 10% to the payment probability of commodity 1 that user 2, which is calculated,.
In embodiments of the present invention, it is preferred that described according to corresponding goods in the user characteristics and each search result
Product features determine the user for corresponding to quotient in each search result by the first machine learning model being pre-created
Before the payment probability of product, the first machine learning model is created.
First machine learning model can be XGBOOST models.
In embodiments of the present invention, it is preferred that from the foregoing it will be appreciated that the history Access Events of commodity include record payment thing
Part and arrearage event, therefore in order to improve the accuracy of the first machine learning model, with more accurately to consuming intention number
According to being calculated, to improve the accuracy being subsequently ranked up to search result, multiple users can be obtained for commodity
History Access Events, the history Access Events include payment event and arrearage event, are carried from the history Access Events
User characteristics and product features are taken, and payment probability of each user to commodity is calculated according to history Access Events, according to being extracted
Product features, user characteristics and the payment probability calculated, create first machine learning model.
Wherein, the user characteristics and product features extracted from history Access Events can be used as sample, pass through the first machine
Device learning model calculates payment probability of each user to commodity according to the sample extracted, can be to the first machine learning mould
Type is trained.
Step 105, according to the consumption intention data, the multiple search result is ranked up.
Since different users is different for the consumption intention data of different commodity, i.e., different user can to different commodity
There can be different consumption propensity, therefore, for the ease of being directed to any user, targetedly provide more accurate search knot
Fruit will be ranked up search result the consumption intention data of corresponding goods in each search result according to the user, that is to say
Consumption demand according to user to the different commodity searched, the search result that targetedly the opposite direction user provides are arranged
Sequence, improves the accuracy of the accuracy and the search result provided that are ranked up to search result, so also improve to
Family provides the accuracy of commodity, can be according to the user to the consumption intention data of corresponding goods in each search result, to multiple
Search result is ranked up.
Wherein, according to the difference of consumption intention data, the mode being ranked up to multiple commodity can also be different, than
Such as, when the consumption intention data is payment probability, can according to payment probability sequence from big to small, to multiple commodity into
Row sequence, to guarantee the larger commodity of probability of paying the bill preferentially being supplied to user.If the consumption intention data is unpaid
When money probability, multiple commodity can be ranked up according to arrearage probability from small arrival volume sequence.
Arrearage probability is the probability that user does not pay the bill to commodity, determines that the mode of arrearage probability can be with determination
The mode of payment probability is identical, no longer repeats one by one herein.
For example, user 1 is 50% to the payment probability of commodity 1, the payment probability to commodity 2 is 65%, to paying for commodity 3
Money probability is 20%, is 80% to the payment probability of commodity 4, therefore, according to the sequence of payment probability from big to small, by commodity 1,
Commodity 2, commodity 3 and commodity 4 are ranked up, and obtain commodity 4, commodity 2, commodity 1, commodity 3.
Furthermore it is possible to which the search result after sequence is supplied to user, rapidly and accurately tied according to search thereby using family
Fruit selects suitable commodity, is consumed convenient for user, while ensuring the interests of user and businessman.
The search result after sequence can be shown by webpage or client, to provide search result
Give the user.
In embodiments of the present invention, due to may be had differences between different users and different commodity, from
And different users has different consumption demands to different commodity, so the user for submitting searching request, Neng Gougen
Multiple searching results are obtained according to the end article information search carried in searching request, are marked according to the user carried in searching request
Knowledge gets corresponding user characteristics, then according to the product features of corresponding goods in the user characteristics and each search result, really
The fixed user is directed to the consumption intention data of corresponding goods in each search result, and then can will be tied to each search according to the user
The consumption intention data of corresponding goods is ranked up search result in fruit, that is to say difference that can be according to user to searching
The consumption demand of commodity, the search result that targetedly the opposite direction user provides are ranked up, improve and carried out to search result
The accuracy of the accuracy of sequence and the search result provided, and then also improve the accuracy for providing a user commodity.
Embodiment two
With reference to Fig. 2, a kind of step flow chart of search result ordering method in the embodiment of the present invention, the search are shown
Result ordering method includes：
Step 201, cloud platform receives the searching request submitted, and user identifier and target quotient are carried in described search request
Product information.
In embodiments of the present invention, it is preferred that in order to improve the follow-up reliability for providing a user search result and flexibly
Property, user identifier can not be carried in the searching request, but Portable device identifies.When receiving the device identification, according to
User identifier corresponding with the device identification is searched in the device identification.
The device identification is used for one equipment of unique mark, which can be MAC (the Media Access of equipment
Control, media access control) address, IP (Internet Protocol, procotol) address etc..The equipment can be with
The equipment that the equipment of user identifier binding or the corresponding user of the user identifier log in.
For example, Portable device is identified as device id 1 in searching request, the cloud platform is when receiving the searching request, root
According to device id 1, from the correspondence between device identification as shown in table 1 below and user identifier, corresponding user is found
It is identified as User ID 1.
Table 1
| Device identification | User identifier |
| Device id 1 | User ID 1 |
| Device id 3 | Device id 2 |
| Device id 4 | Device id 3 |
| …… | …… |
The embodiment of the present invention only by taking above-mentioned table 1 as an example, say by the correspondence between device identification and user identifier
Bright, correspondence of the above-mentioned table 1 not between device identification and user identifier, which is constituted, to be limited.
In embodiments of the present invention, it is preferred that the equipment for being logged in the user identifier can be interacted with the cloud platform,
The cloud platform is set to get the device identification of the equipment, for example, the equipment is to the cloud platform sending device bind request, the binding
Device identification and the user identifier of the equipment are carried in request.The cloud platform is getting the device identification and the user identifier
When, the device identification and the user identifier can be stored to pair as shown in 1 on table between device identification and user identifier
In should being related to.
Step 202, the cloud platform obtains multiple search results according to the end article information search, determines each search
As a result the product features of middle corresponding goods.
In embodiments of the present invention, it is preferred that since product features may change over time, such as commodity valence
Lattice etc., therefore, in order to improve the accuracy for getting product features, so improve subsequently search result is ranked up it is accurate
Property, it may be determined that product features of the corresponding goods in the second preset time period in each search result, second preset time
Section is using current time as the period in the third preset duration of starting point.
Wherein, the second preset time period or third preset duration can be determined by the cloud platform, and third preset duration can be with
It is 1 hour, 1 day, 1 week, 1 month etc..And when third preset duration more in short-term, the second preset time period it is also shorter, obtained
The real-time of the product features got or the user characteristics subsequently got is also stronger, so ensure that get user characteristics or
The accuracy of product features.
The product features of corresponding goods in each search result for getting can be carried out according to the second preset time period
Filter, to obtain product features of the corresponding goods in the second preset time period in each search result.
In embodiments of the present invention, it is preferred that in order to further increase the accuracy being ranked up to search result, the cloud
Platform can also obtain other feature, such as environmental characteristic.
The environmental characteristic is used to illustrate environment in the user or each search result residing for corresponding goods, for example, it is current when
Carve and user respectively with the distance between corresponding goods etc. in each search result.
Wherein, the distance between the user and corresponding goods in each search result can be the geographical position residing for the user
Set respectively with the distance between the geographical location residing for corresponding goods in each search result.
Step 203, the cloud platform obtains user characteristics according to the user identifier.
In embodiments of the present invention, it is preferred that since user characteristics may change over time, in order to carry
Height gets the accuracy of user characteristics, and then improves the accuracy being subsequently ranked up to search result, can be according to described
User identifier obtains the user characteristics in second preset time period, and second preset time period is to be with current time
Period in the third preset duration of point.
It can be filtered to obtaining user characteristics, according to the second preset time period to obtain for user identifier
User characteristics in second preset time period.
Step 204, the cloud platform is according to the product features of corresponding goods in the user characteristics and each search result, really
Determine consumption intention data of the user for corresponding goods in each search result.
In embodiments of the present invention, it is preferred that since user is after paying the bill to buy commodity to commodity, it is also possible to by
In commercial quality difference or other reasons, to the commodity reimbursement, both it is unfavorable for improving the consumption experience of user, it also can be to businessman
Interests damage, therefore, the consumption intention data can be consumption probability, the consumption probability refer to user to commodity payment and
Nonrefundable probability.
In embodiments of the present invention, it is preferred that in order to improve the accuracy for calculating consumption probability, and then improve and search is tied
The accuracy that fruit is ranked up can pass through according to the product features of corresponding goods in the user characteristics and each search result
The first machine learning model being pre-created determines the user for the payment probability of corresponding goods in each search result, institute
The first machine learning model is stated for determining that payment is general according to the product features of corresponding goods in user characteristics and each search result
Rate；According to the product features of corresponding goods in the user characteristics and each search result, pass through the second engineering being pre-created
Model is practised, determines that reimbursement probability of the user for corresponding goods in each search result, second machine learning model are used
In determining reimbursement probability according to the product features of corresponding goods in user characteristics and each search result, according to the payment probability and
The reimbursement probability determines consumption probability of the user for corresponding goods in each search result, and the consumption probability is true
It is set to the consumption intention data.
Formula 2 can be passed through：P1=P2* (1-P3) comes according to payment probability and the reimbursement determine the probability consumption probability.Its
In, P1 mark consumption probability, P2 indicates that payment probability, P3 indicate reimbursement probability.
Wherein, the mode for creating the first machine learning model, with may refer to it is aforementioned in associated description, it is not another herein
One repeats.
It can be in the product features according to corresponding goods in the user characteristics and each search result, by creating in advance
The second machine learning model built, determines reimbursement probability of the user for corresponding goods in each search result, described second
Before machine learning model is used to determine reimbursement probability according to the product features of corresponding goods in user characteristics and each search result,
Create second machine learning model.
Second machine learning model can be XGBOOST models.
In embodiments of the present invention, it is preferred that in order to improve the accuracy for creating the second machine learning model, with accurately
Reimbursement probability is calculated, to improve the accuracy for determining consumption probability and being ranked up to search result, can be obtained
Multiple users are taken to be directed to the history Access Events of commodity, the history Access Events include refund event and non-refund event, from
User characteristics and product features are extracted in the history Access Events, and calculate each user to commodity according to history Access Events
Reimbursement probability creates second engineering according to the product features, user characteristics and the reimbursement probability calculated extracted
Practise model.
Wherein, the mode of the second machine learning model is created, can be similar to the mode of the first machine learning model is created,
It no longer repeats one by one herein.
In embodiments of the present invention, it is preferred that in order to reduce needs machine mould quantity, to save establishment and instruction
Practice the time cost needed for machine mould, improves the efficiency for determining consumption intention data, and then improve and arrange search result
The efficiency of sequence and the efficiency for providing a user search result can correspond to quotient according in the user characteristics and each search result
The product features of product determine the user for corresponding in each search result by the third machine learning model being pre-created
The payment probability of commodity, the third machine learning model are used for the quotient according to corresponding goods in user characteristics and each search result
Product feature determines consumption probability, is the consumption intention data by the consumption determine the probability.
Wherein it is possible in the product features according to corresponding goods in the user characteristics and each search result, pass through
The third machine learning model being pre-created, determine the user for corresponding goods in each search result payment probability it
Before, create third machine learning model.
Third machine learning model can be XGBOOST models.
In embodiments of the present invention, it is preferred that in order to improve the accuracy of third machine learning model, and then improve and calculate
Consumption intention data and the accuracy being ranked up to search result, the history that can obtain multiple users for commodity access
Event, the history Access Events include payment event, arrearage event, refund event and non-refund event, from the history
User characteristics and product features are extracted in Access Events, and it is general to the consumption of commodity according to history Access Events to calculate each user
Rate creates the third machine learning mould according to the product features, user characteristics and the consumption probability calculated extracted
Type.
Wherein, the mode of third machine learning model is created, can be similar to the mode of the first machine learning model is created,
It no longer repeats one by one herein.
In embodiments of the present invention, it is preferred that since the cloud platform can also obtain the other features such as environmental characteristic, because
This, which can be other according to the product features and environmental characteristic of corresponding goods in user characteristics, each search result etc.
Feature determines the consumption intention data.And according to the product features and environment of corresponding goods in user characteristics, each search result
The other features such as feature determine the mode of the consumption intention data, can correspond to quotient with according in user characteristics, each search result
The product features of product determine that the mode of the consumption intention data is similar, no longer repeat one by one herein.
For example, the other feature further includes environmental characteristic, quotient can be corresponded to according in the user characteristics, each search result
The product features and environmental characteristic of product determine that the user is directed to each search by the 4th machine learning model being pre-created
As a result the payment probability of middle corresponding goods, the 4th machine learning model are used for according to right in user characteristics, each search result
The product features of commodity and environmental characteristic are answered to determine payment probability, according to corresponding goods in the user characteristics and each search result
Product features determine the user for corresponding to quotient in each search result by the 5th machine learning model being pre-created
The reimbursement probability of product, the 5th machine learning model are used for the commodity according to corresponding goods in user characteristics, each search result
Feature and environmental characteristic determine reimbursement probability, according to the payment probability and the reimbursement probability, determine the user for each
The consumption determine the probability is the consumption intention data by the consumption probability of corresponding goods in search result.
Wherein, create the mode of the 4th machine learning model or the 5th machine learning model, can with create the first machine
The mode of learning model is similar, no longer repeats one by one herein.
Step 205, the cloud platform is ranked up the multiple search result according to the consumption intention data, and
Search result after sequence is supplied to user.
In embodiments of the present invention, it is preferred that since environmental characteristic can illustrate the environment residing for user or commodity, than
Such as, user respectively in each search result at a distance from corresponding goods etc., therefore, in order to make the sequence to commodity and environmental characteristic phase
It coincide, further increases the accuracy being ranked up to search result, it can be in conjunction with the environmental characteristic and the consumption intention
Data are ranked up the multiple search result, and the search result after sequence is supplied to user, to rapidly and accurately
Commodity are provided for user.
Wherein it is possible to be preferentially ranked up to corresponding goods in each search result according to environmental characteristic, for environmental characteristic
Identical commodity are ranked up according still further to the consumption intention data.It is of course also possible to preferentially according to the consumption intention data to each
Corresponding goods are ranked up in search result, and commodity identical for consumption intention data are arranged according still further to the environmental characteristic
Sequence.
For example, user 2 is 50% to the consumption probability of commodity 1, the distance between user 2 and commodity 1 are 1 kilometer, and user 2
Consumption probability to commodity 2 is 20%, and the distance between user 2 and commodity 2 are 7 kilometers, consumption probability of the user 2 to commodity 3
It is 20%, the distance between user 2 and commodity 3 are 3 kilometers, and user 2 is 80% to the consumption probability of commodity 4, user 2 and commodity
The distance between 4 be 4 kilometers.Therefore, preferentially according to consumption probability to being ranked up commodity 1, commodity 2, commodity 3 and commodity 4,
Commodity 4, commodity 1, commodity 2, commodity 3 are obtained, since the consumption probability to commodity 2 with commodity 3 is identical, so according to user and quotient
The distance between product are ranked up commodity 2 and commodity 3 to remote sequence from closely to obtain commodity 4, commodity 1, commodity 3, commodity 4.
Certainly, if in the foregoing according to the product features of corresponding goods in user characteristics, each search result and
Environmental characteristic determines that the user to the consumption probability of corresponding goods in each search result, can directly disappear according to this in this step
Expense intention data is ranked up multiple search result, and the search result after sequence is supplied to the user.
In embodiments of the present invention, firstly, since may exist between different users and different commodity it is poor
It is different, to which different users has different consumption demands to different commodity, so the user for submitting searching request, energy
It is enough that multiple searching results are obtained according to the end article information search carried in searching request, according to the use carried in searching request
Family mark gets corresponding user characteristics, then special according to the commodity of corresponding goods in the user characteristics and each search result
Sign, determines consumption intention data of the user for corresponding goods in each search result, and then can will be according to the user to each
The consumption intention data of corresponding goods is ranked up search result in search result, and that is to say can be according to user to searching
Different commodity consumption demand, targetedly opposite direction the user provide search result be ranked up, improve to search tie
The accuracy of accuracy and the search result provided that fruit is ranked up, and then also improve the standard for providing a user commodity
True property.
Secondly, it is general to the payment of corresponding goods in each search result that the user can be calculated according to the first machine learning model
Rate calculates reimbursement probability of the user to corresponding goods in each search result according to the second machine learning model, and according to the use
Family determines the user to corresponding goods in each search result the payment probability and reimbursement probability of corresponding goods in each search result
Consumption probability.Using the consumption probability as the consumption intention data, the accuracy for determining consumption intention data really is improved
And to the accuracy that search result is ranked up, also ensure that can preferentially to provide a user the customer consumption probability higher
Commodity, to ensure the interests of businessman and user simultaneously.
Furthermore it is possible to determine that the user is general to the consumption of corresponding goods in each search result according to third machine learning model
Rate reduces the quantity of the machine mould of needs, to save the time cost needed for establishment and training machine model, improves
It determines the efficiency of consumption intention data, and then improves the efficiency being ranked up to search result and provide a user search knot
The efficiency of fruit.
It should be noted that for embodiment of the method above-mentioned, for simple description, therefore it is all expressed as a series of
Combination of actions, but those skilled in the art should understand that, the present invention is not limited by the described action sequence, because according to
According to the present invention, certain steps can be performed in other orders or simultaneously.Next, those skilled in the art should also know that,
Embodiment described in this description belongs to preferred embodiment, and involved action is not necessarily essential to the invention.
Embodiment three
With reference to Fig. 3, a kind of structure diagram of search results ranking device in the embodiment of the present invention is shown.Described search
Sort result device includes：
Receiving module 301 carries user identifier and end article letter for receiving searching request in described search request
Breath；
First determining module 302, for obtaining multiple search results according to the end article information search, determination is respectively searched
The product features of corresponding goods in hitch fruit；
First acquisition module 303, for obtaining user characteristics according to the user identifier；
Second determining module 304 is used for the product features according to corresponding goods in the user characteristics and each search result,
Determine consumption intention data of the user for corresponding goods in each search result；
Sorting module 305, for according to the consumption intention data, being ranked up to the multiple search result.
Optionally, second determining module includes：
First determination sub-module is used for the product features according to corresponding goods in the user characteristics and each search result,
By the first machine learning model being pre-created, determine that the user is general for the payment of corresponding goods in each search result
Rate, first machine learning model are used to be determined according to the product features of corresponding goods in user characteristics and each search result and pay
Money probability；
Second determination sub-module is used for the product features according to corresponding goods in the user characteristics and each search result,
By the second machine learning model being pre-created, determine that the user is general for the reimbursement of corresponding goods in each search result
Rate, second machine learning model are used to be moved back according to the product features determination of corresponding goods in user characteristics and each search result
Money probability；
Third determination sub-module, for according to the payment probability and the reimbursement probability, determining the user for each
The consumption determine the probability is the consumption intention data by the consumption probability of corresponding goods in search result.
Optionally, second determining module further includes：
First acquisition submodule, the history Access Events of commodity are directed to for obtaining multiple users, and the history accesses thing
Part includes payment event and arrearage event；
First computational submodule, for extraction user characteristics and product features from the history Access Events, and according to
History Access Events calculate payment probability of each user to commodity；
First creates submodule, for according to the product features, user characteristics and the payment probability calculated extracted,
Create first machine learning model.
Optionally, second determining module further includes：
Second acquisition submodule, the history Access Events of commodity are directed to for obtaining multiple users, and the history accesses thing
Part includes refund event and non-refund event；
Second computational submodule, for extraction user characteristics and product features from the history Access Events, and according to
History Access Events calculate reimbursement probability of each user to commodity；
Second creates submodule, for according to the product features, user characteristics and the reimbursement probability calculated extracted,
Create second machine learning model.
Optionally, second determining module includes：
4th determination sub-module is used for the product features according to corresponding goods in the user characteristics and each search result,
By the third machine learning model being pre-created, determine that the user is general for the payment of corresponding goods in each search result
Rate, the third machine learning model are used to be disappeared according to the product features determination of corresponding goods in user characteristics and each search result
Take probability；
5th determination sub-module, for being the consumption intention data by the consumption determine the probability.
Optionally, the second determining module further includes：
Third acquisition submodule, the history Access Events of commodity are directed to for obtaining multiple users, and the history accesses thing
Part includes payment event, arrearage event, refund event and non-refund event；
Third computational submodule, for extraction user characteristics and product features from the history Access Events, and according to
History Access Events calculate consumption probability of each user to commodity；
Third creates submodule, for according to the product features, user characteristics and the consumption probability calculated extracted,
Create the third machine learning model.
Optionally, described device further includes：
Second acquisition module, for obtaining environmental characteristic, the environmental characteristic includes that the user ties with each search respectively
The distance between corresponding goods in fruit；
The sorting module includes：
Sorting sub-module, in conjunction with the environmental characteristic and the consumption intention data, being carried out to the multiple commodity
Sequence.
Optionally, first determining module includes：
6th determination sub-module, for determining, commodity of the corresponding goods in the second preset time period are special in each search result
Sign, second preset time period are using current time as the period in the third preset duration of starting point；
First acquisition module includes：
4th acquisition submodule, for obtaining the spy of the user in second preset time period according to the user identifier
Sign.
In embodiments of the present invention, due to may be had differences between different users and different commodity, from
And different users has different consumption demands to different commodity, so the user for submitting searching request, Neng Gougen
Multiple searching results are obtained according to the end article information search carried in searching request, are marked according to the user carried in searching request
Knowledge gets corresponding user characteristics, then according to the product features of corresponding goods in the user characteristics and each search result, really
The fixed user is directed to the consumption intention data of corresponding goods in each search result, and then can will be tied to each search according to the user
The consumption intention data of corresponding goods is ranked up search result in fruit, that is to say difference that can be according to user to searching
The consumption demand of commodity, the search result that targetedly the opposite direction user provides are ranked up, improve and carried out to search result
The accuracy of the accuracy of sequence and the search result provided, and then also improve the accuracy for providing a user commodity.
For above-mentioned apparatus embodiment, since it is basically similar to the method embodiment, so description is fairly simple,
The relevent part can refer to the partial explaination of embodiments of method.
Example IV
The present invention also provides a kind of memory, processor and store the meter that can be run on a memory and on a processor
Calculation machine program, the processor realize each process such as the search result ordering method in aforementioned when executing described program, and
Identical technique effect can be reached, to avoid repeating, no longer repeated one by one herein.
Embodiment five
The embodiment of the present invention also provides a kind of computer readable storage medium, and meter is stored on computer readable storage medium
Calculation machine program realizes each process such as the search result ordering method in aforementioned when the computer program is executed by processor,
And identical technique effect can be reached, to avoid repeating, no longer repeat one by one herein.Wherein, described computer-readable to deposit
Storage media, such as read-only memory (Read-Only Memory, abbreviation ROM), random access memory (Random Access
Memory, abbreviation RAM), magnetic disc or CD etc..
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with
The difference of other embodiment, the same or similar parts between the embodiments can be referred to each other.
It would have readily occurred to a person skilled in the art that be：The arbitrary combination application of above-mentioned each embodiment is all feasible, therefore
Arbitrary combination between above-mentioned each embodiment is all embodiment of the present invention, but this specification exists as space is limited,
This is not just detailed one by one.
Provided herein based on mobile terminal phone report scheme not with any certain computer, virtual system or its
Its equipment is inherently related.Various general-purpose systems can also be used together with teaching based on this.As described above, it constructs
Structure required by system with the present invention program is obvious.In addition, the present invention is not also directed to any certain programmed
Language.It should be understood that the content of various programming languages realization invention described herein can be utilized, and above to specific language
The done description of speech is to disclose the preferred forms of the present invention.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention
Example can be put into practice without these specific details.In some instances, well known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this description.
Similarly, it should be understood that in order to simplify the disclosure and help to understand one or more of each inventive aspect,
Above in the description of exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：It is i.e. required to protect
Shield the present invention claims the more features of feature than being expressly recited in each claim.More precisely, such as right
As claim reflects, inventive aspect is all features less than single embodiment disclosed above.Therefore, it then follows tool
Thus claims of body embodiment are expressly incorporated in the specific implementation mode, wherein each claim conduct itself
The separate embodiments of the present invention.
Those skilled in the art, which are appreciated that, to carry out adaptively the module in the equipment in embodiment
Change and they are arranged in the one or more equipment different from the embodiment.It can be the module or list in embodiment
Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or
Sub-component.Other than such feature and/or at least some of process or unit exclude each other, it may be used any
Combination is disclosed to all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so to appoint
Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power
Profit requires, abstract and attached drawing) disclosed in each feature can be by providing the alternative features of identical, equivalent or similar purpose come generation
It replaces.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included certain features rather than other feature, but the combination of the feature of different embodiments means in of the invention
Within the scope of and form different embodiments.For example, in detail in the claims, embodiment claimed it is one of arbitrary
It mode can use in any combination.
The all parts embodiment of the present invention can be with hardware realization, or to run on one or more processors
Software module realize, or realized with combination thereof.It will be understood by those of skill in the art that can use in practice
Microprocessor or digital signal processor (DSP) come realize in goods providers case according to the ... of the embodiment of the present invention some or
The some or all functions of person's whole component.The present invention is also implemented as one for executing method as described herein
Divide either whole equipment or program of device (for example, computer program and computer program product).Such this hair of realization
Bright program can may be stored on the computer-readable medium, or can be with the form of one or more signal.It is such
Signal can be downloaded from internet website and be obtained, and either provided on carrier signal or provided in any other forms.
It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability
Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference mark between bracket should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" before element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real
It is existing.In the unit claims listing several devices, several in these devices can be by the same hardware branch
To embody.The use of word first, second, and third does not indicate that any sequence.These words can be explained and be run after fame
Claim.
Claims (11)
1. a kind of search result ordering method, which is characterized in that including：
Searching request is received, user identifier and end article information are carried in described search request；
Multiple search results are obtained according to the end article information search, determine that the commodity of corresponding goods in each search result are special
Sign；
User characteristics are obtained according to the user identifier；
According to the product features of corresponding goods in the user characteristics and each search result, determine user in each search result
The consumption intention data of corresponding goods；
According to the consumption intention data, the multiple search result is ranked up.
2. search result ordering method according to claim 1, which is characterized in that described according to the user characteristics and each
The product features of corresponding goods in search result determine consumption intention data packet of the user for corresponding goods in each search result
It includes：
According to the product features of corresponding goods in the user characteristics and each search result, pass through the first engineering being pre-created
Model is practised, determines that payment probability of the user for corresponding goods in each search result, first machine learning model are used
In the product features determination payment probability according to corresponding goods in user characteristics and each search result；
According to the product features of corresponding goods in the user characteristics and each search result, pass through the second engineering being pre-created
Model is practised, determines that reimbursement probability of the user for corresponding goods in each search result, second machine learning model are used
In determining reimbursement probability according to the product features of corresponding goods in user characteristics and each search result；
According to the payment probability and the reimbursement probability, consumption of the user for corresponding goods in each search result is determined
The consumption determine the probability is the consumption intention data by probability.
3. search result ordering method according to claim 2, which is characterized in that it is described according to the user characteristics and
The product features of corresponding goods in each search result determine user's needle by the first machine learning model being pre-created
Before the payment probability of corresponding goods in each search result, further include：
The history Access Events that multiple users are directed to commodity are obtained, the history Access Events include payment event and arrearage thing
Part；
User characteristics and product features are extracted from the history Access Events, and calculate each user couple according to history Access Events
The payment probability of commodity；
According to the product features, user characteristics and the payment probability calculated extracted, the first machine learning mould is created
Type.
4. search result ordering method according to claim 2, which is characterized in that it is described according to the user characteristics and
The product features of corresponding goods in each search result determine user's needle by the second machine learning model being pre-created
Before the reimbursement probability of corresponding goods in each search result, further include：
The history Access Events that multiple users are directed to commodity are obtained, the history Access Events include refund event and non-reimbursement thing
Part；
User characteristics and product features are extracted from the history Access Events, and calculate each user couple according to history Access Events
The reimbursement probability of commodity；
According to the product features, user characteristics and the reimbursement probability calculated extracted, the second machine learning mould is created
Type.
5. search result ordering method according to claim 1, which is characterized in that described according to the user characteristics and each
The product features of corresponding goods in search result determine consumption intention data packet of the user for corresponding goods in each search result
It includes：
According to the product features of corresponding goods in the user characteristics and each search result, pass through the third engineering being pre-created
Model is practised, determines that payment probability of the user for corresponding goods in each search result, the third machine learning model are used
Probability is consumed in being determined according to the product features of corresponding goods in user characteristics and each search result；
It is the consumption intention data by the consumption determine the probability.
6. search result ordering method according to claim 5, which is characterized in that it is described according to the user characteristics and
The product features of corresponding goods in each search result determine user's needle by the third machine learning model being pre-created
Before the payment probability of corresponding goods in each search result, further include：
The history Access Events that multiple users are directed to commodity are obtained, the history Access Events include payment event, arrearage thing
Part, refund event and non-refund event；
User characteristics and product features are extracted from the history Access Events, and calculate each user couple according to history Access Events
The consumption probability of commodity；
According to the product features, user characteristics and the consumption probability calculated extracted, the third machine learning mould is created
Type.
7. search result ordering method according to claim 1, which is characterized in that further include：
Obtain environmental characteristic, the environmental characteristic include the user respectively between corresponding goods in each search result away from
From；
It is described according to the consumption intention data, to the multiple search result be ranked up including：
In conjunction with the environmental characteristic and the consumption intention data, the multiple commodity are ranked up.
8. search result ordering method according to claim 1, which is characterized in that described according to the end article information
Search obtains multiple search results, determines that the product features of corresponding goods in each search result include：
Determine that product features of the corresponding goods in the second preset time period in each search result, second preset time period are
Using current time as the period in the third preset duration of starting point；
It is described to include according to user identifier acquisition user characteristics：
The user characteristics in second preset time period are obtained according to the user identifier.
9. a kind of search results ranking device, which is characterized in that including：
Receiving module carries user identifier and end article information for receiving searching request in described search request；
First determining module determines each search result for obtaining multiple search results according to the end article information search
The product features of middle corresponding goods；
First acquisition module, for obtaining user characteristics according to the user identifier；
Second determining module is determined and is used for the product features according to corresponding goods in the user characteristics and each search result
Family is directed to the consumption intention data of corresponding goods in each search result；
Sorting module, for according to the consumption intention data, being ranked up to the multiple search result.
10. a kind of electronic equipment, including memory, processor and storage are on a memory and the calculating that can run on a processor
Machine program, which is characterized in that the processor realizes any one the method for claim 1 to 8 when executing described program
Step.
11. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is by processor
The step of any one the method for claim 1 to 8 is realized when execution.
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| CN106776860A (en) * | 2016-11-28 | 2017-05-31 | 北京三快在线科技有限公司 | One kind search abstraction generating method and device |
| CN107424043A (en) * | 2017-06-15 | 2017-12-01 | 北京三快在线科技有限公司 | A kind of Products Show method and device, electronic equipment |
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| CN109242644A (en) * | 2018-10-10 | 2019-01-18 | 深圳市心版图科技有限公司 | Method of Commodity Recommendation, terminal and computer readable storage medium |
| CN110569439A (en) * | 2019-09-10 | 2019-12-13 | 拉扎斯网络科技（上海）有限公司 | Entity display method, entity display device, storage medium and electronic equipment |
| CN111091442A (en) * | 2019-11-25 | 2020-05-01 | 北京一七网络技术有限公司 | Demand information synchronous collection method based on user query operation |
| CN114154046A (en) * | 2022-02-07 | 2022-03-08 | 深圳华强电子交易网络有限公司 | Website search ranking method and system |
| CN114154046B (en) * | 2022-02-07 | 2022-08-12 | 深圳华强电子交易网络有限公司 | Website search ranking method and system |
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