CN103778139A - Search method and server - Google Patents

Search method and server Download PDF

Info

Publication number
CN103778139A
CN103778139A CN201210402829.5A CN201210402829A CN103778139A CN 103778139 A CN103778139 A CN 103778139A CN 201210402829 A CN201210402829 A CN 201210402829A CN 103778139 A CN103778139 A CN 103778139A
Authority
CN
China
Prior art keywords
sequence object
ranking value
sequence
current sequence
value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201210402829.5A
Other languages
Chinese (zh)
Other versions
CN103778139B (en
Inventor
陈凡
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Alibaba Group Holding Ltd
Original Assignee
Alibaba Group Holding Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Alibaba Group Holding Ltd filed Critical Alibaba Group Holding Ltd
Priority to CN201210402829.5A priority Critical patent/CN103778139B/en
Publication of CN103778139A publication Critical patent/CN103778139A/en
Application granted granted Critical
Publication of CN103778139B publication Critical patent/CN103778139B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24Querying
    • G06F16/245Query processing
    • G06F16/2455Query execution

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)

Abstract

The invention relates to a search method and a server. The search method comprises receiving a search request through the server; searching sorting objects corresponding to a key word in the search request according to the search request; returning sorting values of the sorting objects and referring to the sorting values to return one or more than one sorting object in the front of the sorting values, wherein the sorting values are obtained through behavior data and information data of the sorting objects; constructing an object relation graph according to the behavior data and the information data; calculating and obtaining through the object relation graph.

Description

Searching method and server
Technical field
The application relates to Internet technology, relates in particular to a kind of searching method and server.
Background technology
Along with popularizing of internet, net purchase user increase gradually.Increasing seller user is by internet sales commodity.The object that server can be concluded the business to these sorts, and for example, the object being sorted can be shop etc., by sequence Optimizing Search, makes user find fast relevant store information.Obviously, carry out after object order, the object that can select user exerts an influence.
At present, the method to object search in prior art, main process is the key word of server according to user's input, searches relevant information from database, and information is back to user after sorted again.In the process of server search, with reference to information such as the credit of intelligence-collecting object, sales volume, turnover, comments, the value of the each information of object is added up, obtain the integrated value of this object, according to the integrated value of object, before object high integrated value is emitted on, the ranking results that user is returned according to server, picks out the object needing.
Along with the development of network and the growth of object, in prior art there is following drawback in object search method:
1) because server is calculating in the process of integrated value, be only that value corresponding each information of object is added up, and the value corresponding to each information of object determined voluntarily by manager, makes the integrated value reliability after adding up low;
2) server is being collected the credit of each object, sales volume, turnover, in the process of the information such as comment, in the time there is the fraudulent means such as object propagation, make server in the time of cumulative integrated value, there is certain error, in the time there is error in integrated value, also affect the sequence of server to object, make some exist the object order of fraud information forward, in the time that server returns to ranking results, user cannot obtain the object needing, force user again to input keyword, server is searched for again, cause user access server number of times, the searching times of server increases, resource and the bandwidth of waste server.
Summary of the invention
The application's object is that the searching times in order to solve server in prior art increases, and the waste resource of server and the problem of bandwidth, provide a kind of searching method and server.
In first aspect, the application provides a kind of searching method.Described method comprises:
Server receives searching request;
According to described searching request, search the sequence object corresponding with keyword in described searching request;
Return to the ranking value of described sequence object, and return to the forward one or more described sequence object of described ranking value with reference to described ranking value;
Wherein, described ranking value is behavioral data and the information data by obtaining described sequence object; Build object relationship figure according to described behavioral data and information data; Utilize described object relationship figure to calculate.
In second aspect, the application provides a kind of server.Described device comprises:
Receiving element, for receiving searching request;
Search unit, for according to described searching request, search the sequence object corresponding with keyword in described searching request;
Processing unit, for returning to the ranking value of described sequence object, and returns to the forward one or more described sequence object of described ranking value with reference to described ranking value;
Wherein, described processing unit also comprises and obtains subelement, builds subelement and computation subunit, and the described ranking value in described processing unit is behavioral data and the information data of obtaining subelement and obtain described sequence object by described; Build subelement and build object relationship figure according to described behavioral data and information data; Computation subunit utilizes described object relationship figure to calculate.
Therefore, the searching method and the server that provide by application the embodiment of the present application, server is according to the searching request receiving, search corresponding sequence object, and return to the forward sequence object of multiple ranking value with reference to the ranking value of sequence object, wherein, ranking value is to build object relationship figure according to the behavioral data of sequence object and information data, utilizes object relationship figure to calculate.The application has avoided the searching times of server in prior art to increase, the waste resource of server and the problem of bandwidth, the application adopts object level algorithms, objectively calculate the ranking value of sequence object, improve the accuracy of search, meanwhile, also improved further the reliability of object search.
Accompanying drawing explanation
The searching method process flow diagram that Fig. 1 provides for the embodiment of the present application one;
The object relationship figure that Fig. 2 provides for the embodiment of the present application;
The server that Fig. 3 provides for the embodiment of the present application two.
Embodiment
In order to provide the implementation that improves search reliability, the embodiment of the present application provides a kind of searching method and server, this technical scheme can be applied to the process that needs search, screening object on user's browsing page, both can be implemented as a kind of method, also can be implemented as a kind of product.Below in conjunction with Figure of description, the application's preferred embodiment is described, should be appreciated that preferred embodiment described herein is only for description and interpretation the application, and be not used in restriction the application.And in the situation that not conflicting, the feature in embodiment and embodiment in the application can combine mutually.
The embodiment of the present application one provides a kind of method of object order, by the method, can improve the reliability of object order.
The method of the object order that the embodiment of the present application one provides can be applied in all kinds of servers, for example, for realizing the Website server of Internet service or sales service, the type of related server is according to the business of specific implementation and different, no matter but the server of which kind of type, the scheme that the present embodiment one provides is all applicable.
The application implement one for object be the shop in internet.
The searching method that the embodiment of the present application one provides, need to utilize behavioral data and the information data of each sequence object, behavioral data is the behavioral data that user produces at this sequence object, information data can comprise the information data that this sequence object is basic.For example, behavioral data comprises, the merchandise news that buyer carries out each transaction in sequence object, and the Bidder Information of transaction, seller's information of transaction, the trading volume of commodity and the turnover of commodity, buyer collects the commodity of sequence object and sequence object etc.; Information data comprises, the basic data of sequence object, preferably, can comprise, the credit level of sequence object, the grade of sequence object self etc.Further, behavioral data and information data are by manager periodically (for example, every day) data importing in object behavior database and object information database in server, by server, behavioral data and information data are carried out to unified management.
The searching method process flow diagram that Fig. 1 provides for the embodiment of the present application one, particularly, this searching method, subject of implementation is server, mainly comprises the steps:
Step 110, server receive searching request.
Particularly, user uses terminal device in browsing page process, wishes to search out multiple information self needing, and terminal, according to the information of user's input, sends searching request to server, the searching request that server receiving terminal sends.
For example, in the embodiment of the present application, user wishes to obtain about fashion brand clothes information, user inputs fashion brand clothes in terminal, terminal, according to the information of user's input, generates searching request and sends to server, carries the keyword of user's input in searching request.
Step 120, according to described searching request, search the sequence object corresponding with keyword in described searching request.
Particularly, server receives after searching request, resolves this searching request, therefrom obtains keyword, according to keyword, searches the sequence object corresponding with keyword in object database.
For example, in the embodiment of the present application, keyword is fashion brand clothes, and server, according to fashion brand clothes, finds out corresponding sequence object, as, ONLY, VERYMODA, ZARA and HM.
Step 130, return to the ranking value of described sequence object, and return to the forward one or more described sequence object of described ranking value with reference to described ranking value.
Particularly, server is finding after the sequence object corresponding with keyword, calculate the ranking value of sequence object, and return to the forward one or more sequence objects of ranking value with reference to ranking value, because server calculates, screens the sequence object of searching, save user's time, also improved accuracy and the reliability of search simultaneously.
Further, the ranking value in the embodiment of the present application is behavioral data and the information data by obtaining sequence object; Build object relationship figure according to behavioral data and information data; Utilize object relationship figure to calculate, describe as example take 4 sequence objects in step 120 below.
First, server obtains behavioral data and the information data of 4 sequence objects from object behavior database and object information database.
Aforementionedly illustrate, the behavioral data of sequence object and information data by manager periodically data importing in object behavior database and object information database in server.
Then, server builds object relationship figure according to behavioral data and information data.
In an example, there are 4 sequence objects, as ONLY, VERYMODA, ZARA and HM.Server obtains behavioral data and the information data of ONLY, VERYMODA, ZARA and HM sequence object, and the behavioral data of sequence object ONLY is as shown in table 1,
Table 1 ONLY behavioral data
Figure BDA00002286257200051
As known from Table 1,5 users produce transaction and collection behavior in ONLY object, and the information data that server obtains sequence object ONLY is basic data, preferably, and object identity, credit level 3 red hats etc.
The behavioral data of sequence object VERYMODA is as shown in table 2,
Table 2 VERYMODA behavioral data
Figure BDA00002286257200061
As known from Table 2,3 users produce transaction and collection behavior in VERYMODA object, and the information data that server obtains sequence object VERYMODA is basic data, preferably, and object identity, credit level 2 red hats etc.
The behavioral data of sequence object ZARA is as shown in table 3,
Table 3 ZARA behavioral data
Figure BDA00002286257200062
As known from Table 3,2 users produce transaction and collection behavior in ZARA object, and the information data that server obtains sequence object ZARA is basic data, preferably, and object identity, credit level 3 red hats etc.
The behavioral data of sequence object HM is as shown in table 4,
Table 4 HM behavioral data
Figure BDA00002286257200063
As known from Table 4,1 user produces transaction and collection behavior in HM object, and the information data that server obtains sequence object HM is basic data, preferably, and object identity, credit level 4 red hats etc.
Server obtains after the behavioral data and information data of above-mentioned 4 sequence objects, in subordinate act data, filter out transaction and the collection behavior of same user at different objects, in the embodiment of the present application, there is transaction and collection behavior in user 1 in ONLY and ZARA sequence object; In ONLY and VERYMODA sequence object, all there is transaction and collection behavior in user 2, user 3 and user 4; In ONLY sequence object, there is transaction and collection behavior in user 5; In ZARA and HM sequence object, there is transaction and collection behavior in user 6.
Server, according to the behavioral data and the information data that filter out, builds object relationship figure, as shown in Figure 2, and the object relationship figure that Fig. 2 provides for the embodiment of the present application.
As shown in Figure 2, object relationship figure comprises node and limit, wherein, and node representative sequence object; Bian represents the degree of association between any two sequence objects, and the described degree of association characterizes the number of times of any user at two sequence objects generation joint acts; The described degree of association obtains according to behavioral data and information data statistics.
In the embodiment of the present application, in Fig. 2, four nodes represent respectively ONLY, VERYMODA, ZARA and HM sequence object, node 1 represents ONLY sequence object, node 2 represents VERYMODA sequence object, node 3 represents ZARA sequence object, node 4 represents HM sequence object, and the also mark of indicator sequence object of node, and this mark is obtained from each information data.Four edges represents the degree of association between any two sequence objects, according to aforementioned known, there is transaction and collection behavior at ONLY sequence object to user 5 in user 1, and there is transaction and collection behavior at VERYMODA sequence object to user 4 in user 2, therefore, between ONLY sequence object and VERYMODA sequence object, just have a kind of potential relation, the degree of association between these two sequence objects is 3; In like manner, the degree of association between ONLY sequence object and ZARA sequence object is 1; The degree of association between ZARA sequence object and HM sequence object is also 1; Owing to not existing identical user to have transaction and collection behavior in these two sequence objects between VERYMODA sequence object and ZARA sequence object, therefore, the degree of association between these two objects is 0.
It should be noted that in the embodiment of the present application, how to build object relationship figure take 4 objects as example describes, but in practical application, not limiting therewith, can be that multiple sequence objects build object relationship figure, therefore, object relationship figure comprises at least two nodes and at least one limit.
It should be noted that, object relationship figure is also a kind of data structure, and the data in tables of data are showed with the form of scheming, and makes more intuitive, the easy association between object of knowing.
Structure completes after object relationship figure, and server, from object relationship figure, obtains the mark of the sequence object of node institute indicator; From multiple nodes, select a node as current sequence object; The mark of the sequence object relevant with current sequence object is stored in identification list; Server by utilizing object relationship figure calculates the ranking value of sequence object, and according to ranking value, sequence object is sorted.
In the embodiment of the present application, the ranking value that server by utilizing object relationship figure calculates sequence object is specially, and server is using the mark of the mark of current sequence object and the sequence object relevant with current sequence object as key word;
Give the initial ranking value of current sequence object and the sequence object relevant with current sequence object;
Utilize identification list, obtain the out-degree value of the sequence object relevant to current sequence object, out-degree value is the affiliated partner number of the sequence object relevant to current sequence object in the embodiment of the present application, for example, the affiliated partner of VERYMODA sequence object is only ONLY sequence object, therefore, the out-degree value of VERYMODA sequence object is 1, the affiliated partner of ZARA sequence object is ONLY sequence object and HM sequence object, and therefore, the out-degree value of ZARA sequence object is 2.
According to object level algorithms, utilize the out-degree value of initial ranking value and sequence object, calculate the ranking value SR (a) of current sequence object.
The ranking value SR (a) of current sequence object is specially: SR ( a ) = ( 1 - f ) + f * Σ i = 1 n SR ( i ) * W ( i ) N i Formula (1);
Wherein, SR (a) represents the ranking value of described current sequence object; F represents to adjust the factor; W (i) represents weight coefficient, the positive rating of the untie-sell rate * object (i) of described W (i)=object (i), and described N is described first time of value, described n is the object number relevant with described current sequence object.
In an example, after object relationship figure has as shown in Figure 2 built, server is the ranking value of the sequence object of 1 indicator of computing node first, it is the ranking value of ONLY sequence object, server is from object relationship figure, obtain the mark of the ONLY sequence object of 1 indicator of node, and using node 1 as current sequence object, the mark of the sequence object relevant with ONLY sequence object is stored in identification list, in the embodiment of the present application, because VERYMODA in Fig. 2 is all relevant with ONLY sequence object with ZARA sequence object, (be connected by limit with ONLY sequence object, HM is not relevant with ONLY sequence object), therefore, the mark of the VERYMODA of node 2 and 3 indicators of node and ZARA sequence object is stored in identification list, described identification list is as shown in table 5,
Table 5 identification list
Server calculates the ranking value of sequence object according to object grade ShopRank algorithm iteration; Described object grade ShopRank algorithm comprises to be collected Map stage and iteration Reduce stage.
In the Map stage using the mark of the mark of current sequence object and the sequence object relevant with current sequence object as input key word, if current sequence object is first sequence object, give at random the initial ranking value of current sequence object and the sequence object relevant with current sequence object, using the affiliated partner in initial ranking value and identification list as input key value.
The Map stage is output as, and the object identity that the Map stage is inputted to the affiliated partner in key value is as output key word, using the object identity of the sequence object relevant to this output key word, initial ranking value and the degree of association as output key value.
Using the output in Map stage as input, export the object identity of current sequence object and the ranking value SR (a) of current sequence object in the iteration Reduce stage.
In an example, server is using the mark of the mark of the ONLY sequence object of 1 indicator of node and VERYMOD, ZARA sequence object as input key word, in the time that ONLY sequence object sorts object for first, give at random the initial ranking value that ONLY sequence object and VERYMOD, the ZARA relevant with ONLY sequence object sort object, (for example, 1.0), using the affiliated partner in initial ranking value and identification list as input key value, in the embodiment of the present application, in identification list, the affiliated partner of node 2 is ONLY; The affiliated partner of node 3 is ONLY and HM.
Because the affiliated partner in the input key value in Map stage is ONLY and HM, therefore, in the output in Map stage, using the mark of ONLY and HM as output key word, using the object identity of the VERYMOD relevant to this output key word, ZARA sequence object, initial ranking value and the degree of association as output key value.
Using the output in Map stage as input, export the object identity of ONLY sequence object and the ranking value SR (a) of ONLY sequence object in the iteration Reduce stage.
The ranking value SR (a) of ONLY sequence object determines by formula 1, SR ( a ) = ( 1 - f ) + f * Σ i = 1 n SR ( i ) * W ( i ) N i .
In the embodiment of the present application, getting f, to adjust the value of the factor be 0.9; W (i) value is 1.0.
Therefore, SR ( a ) = ( 1 - 0.9 ) + 0.9 * ( 1.0 * 1.0 1 + 1.0 * 1.0 2 ) = 1.45 , The ranking value of ONLY object is 1.45.
Repeat above-mentioned steps, calculate the ranking value of VERYMOD, ZARA and HM sequence object, in the time calculating the ranking value of VERYMOD, ZARA and HM sequence object, the initial ranking value of the object that can sort the ranking value of previous object as the next one.Calculating completes after the ranking value of all sequence objects, using the object identity of multiple objects as input key word, and using the ranking value of multiple objects as input key value, the sorted lists of output sequence object.
Provide concrete algorithm flow below.Wherein, represent ONLY sequence object with Shop1; Shop2 represents VERYMODA sequence object; Shop3 represents ZARA sequence object; Shop4 represents HM sequence object.
The Map stage is inputted:
Key word Key: key value value:
shop1 1.0
shop2 1.0 shop1
shop3 1.0 shop1,shop4
The Map stage exports:
Key: value:
Shop1 shop2 1.0 1
Shop1 shop3 1.0 2
Shop4 shop3 1.0 2
The Reduce stage is inputted:
Key: value:
Shop1 shop2 1.0 1
Shop1 shop3 1.0 2
Shop4 shop3 1.0 2
The Reduce stage exports:
Key: value:
Shop1 1.45
Sequence flow process is specially:
The Map stage is inputted:
Key: value:
shop1 1.45 ……
The Map stage exports:
Key: value:
1.45 shop1 ……
The above-mentioned method of calculating the ranking value of sequence object by object grade ShopRank algorithm, utilize the object relationship figure building, calculate the ranking value of each sequence object, objectively calculate the ranking value of sequence object, improve the accuracy of search, meanwhile, also improved further the reliability of object search.
Therefore, the searching method providing by application the embodiment of the present application, server is according to the searching request receiving, search corresponding sequence object, and return to the forward sequence object of multiple ranking value with reference to the ranking value of sequence object, wherein, ranking value is to build object relationship figure according to the behavioral data of sequence object and information data, utilizes object relationship figure to calculate.The application has avoided the searching times of server in prior art to increase, the waste resource of server and the problem of bandwidth, the application adopts object level algorithms, objectively calculate the ranking value of sequence object, improve the accuracy of search, meanwhile, also improved further the reliability of object search.
Correspondingly, corresponding with the flow process that above-described embodiment one provides, it is a kind of for realizing the server of above-mentioned flow process that the embodiment of the present application two provides, and by this server, can improve the reliability of search.
The server that Fig. 3 provides for the embodiment of the present application two, particularly, described server, can comprise:
Receiving element 310, for receiving searching request;
Search unit 320, for according to described searching request, search the sequence object corresponding with keyword in described searching request;
Processing unit 330, for returning to the ranking value of described sequence object, and returns to the forward one or more described sequence object of described ranking value with reference to described ranking value;
Wherein, described processing unit 330 also comprises and obtains subelement 331, builds subelement 332 and computation subunit 333, and the described ranking value in described processing unit is behavioral data and the information data of obtaining subelement and obtain described sequence object by described; Build subelement and build object relationship figure according to described behavioral data and information data; Computation subunit utilizes described object relationship figure to calculate.
The object relationship figure building in described structure subelement 332 comprises at least two nodes and at least 1 limit;
Described node represents described sequence object;
Described limit represents the degree of association between any two described sequence objects, and the described degree of association characterizes the number of times of any user to two described sequence objects generation joint acts;
The described degree of association obtains according to described behavioral data and information data statistics.
Described obtain subelement 331 also for, from described object relationship figure, obtain the mark of the represented described sequence object of described node;
Described processing unit 330 also comprises: selected cell 334, for selecting a described node from described node as current sequence object;
Storing sub-units 335, for being stored in identification list by the mark of the described sequence object relevant with described current sequence object.
Described computation subunit 333 specifically for, using the mark of the mark of described current sequence object and the described sequence object relevant with described current sequence object as key word;
Give the initial ranking value of described current sequence object and the described sequence object relevant with described current sequence object;
Utilize described identification list, obtain the out-degree value of the described sequence object relevant to described current sequence object;
According to object level algorithms, utilize described initial ranking value and described out-degree value, calculate the ranking value SR (a) of described current sequence object.
The initial ranking value that described computation subunit 333 is given described current sequence object is specially, the initial ranking value using the ranking value SR of described current sequence object (a) as next described sequence object;
Or when described current sequence object is when sorting object described in first, give at random the initial ranking value of described current sequence object and the described sequence object relevant with described current sequence object.
In described computation subunit 333, the ranking value SR (a) of current sequence object is specially,
SR ( a ) = ( 1 - f ) + f * Σ i = 1 n SR ( i ) * W ( i ) N i ;
Wherein, SR (a) represents the ranking value of described current sequence object; F represents to adjust the factor; W (i) represents weight coefficient, the positive rating of the untie-sell rate * object (i) of described W (i)=object (i), and described N is described first time of value, described n is the object number relevant with described current sequence object.
The server providing by application the embodiment of the present application, server is according to the searching request receiving, search corresponding sequence object, and return to the forward sequence object of multiple ranking value with reference to the ranking value of sequence object, wherein, ranking value is to build object relationship figure according to the behavioral data of sequence object and information data, utilizes object relationship figure to calculate.The application has avoided the searching times of server in prior art to increase, the waste resource of server and the problem of bandwidth, the application adopts object level algorithms, objectively calculate the ranking value of sequence object, improve the accuracy of search, meanwhile, also improved further the reliability of object search.
Professional should further recognize, unit and the algorithm steps of each example of describing in conjunction with embodiment disclosed herein, can realize with electronic hardware, computer software or the combination of the two, for the interchangeability of hardware and software is clearly described, composition and the step of each example described according to function in the above description in general manner.These functions are carried out with hardware or software mode actually, depend on application-specific and the design constraint of technical scheme.Professional and technical personnel can realize described function with distinct methods to each specifically should being used for, but this realization should not thought and exceeds the application's scope.
The software module that the method for describing in conjunction with embodiment disclosed herein or the step of algorithm can use hardware, processor to carry out, or the combination of the two is implemented.Software module can be placed in the storage medium of any other form known in random access memory (RAM), internal memory, ROM (read-only memory) (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field.
Above-described embodiment; object, technical scheme and beneficial effect to the application further describe; institute is understood that; the foregoing is only the application's embodiment; and be not used in and limit the application's protection domain; all within the application's spirit and principle, any modification of making, be equal to replacement, improvement etc., within all should being included in the application's protection domain.

Claims (12)

1. a searching method, is characterized in that, described method comprises:
Server receives searching request;
According to described searching request, search the sequence object corresponding with keyword in described searching request;
Return to the ranking value of described sequence object, and return to the forward one or more described sequence object of described ranking value with reference to described ranking value;
Wherein, described ranking value is behavioral data and the information data by obtaining described sequence object; Build object relationship figure according to described behavioral data and information data; Utilize described object relationship figure to calculate.
2. searching method according to claim 1, is characterized in that, described object relationship figure comprises at least two nodes and at least 1 limit;
Described node represents described sequence object;
Described limit represents the degree of association between any two described sequence objects, and the described degree of association characterizes the number of times of any user to two described sequence objects generation joint acts;
The described degree of association obtains according to described behavioral data and information data statistics.
3. searching method according to claim 2, is characterized in that, describedly utilizes described object relationship figure also to comprise before calculating:
From described object relationship figure, obtain the mark of the represented described sequence object of described node;
From described node, select a described node as current sequence object;
The mark of the described sequence object relevant with described current sequence object is stored in identification list.
4. searching method according to claim 3, is characterized in that, describedly utilizes described object relationship figure to calculate to be specially:
Using the mark of the mark of described current sequence object and the described sequence object relevant with described current sequence object as key word;
Give the initial ranking value of described current sequence object and the described sequence object relevant with described current sequence object;
Utilize described identification list, obtain the out-degree value of the described sequence object relevant to described current sequence object;
According to object level algorithms, utilize described initial ranking value and described out-degree value, calculate the ranking value SR (a) of described current sequence object.
5. searching method according to claim 4, is characterized in that, described initial ranking value is specially:
Initial ranking value using the ranking value SR of described current sequence object (a) as next described sequence object;
Or when described current sequence object is when sorting object described in first, give at random the initial ranking value of described current sequence object and the described sequence object relevant with described current sequence object.
6. searching method according to claim 4, is characterized in that, the ranking value SR (a) of described current sequence object is specially:
SR ( a ) = ( 1 - f ) + f * Σ i = 1 n SR ( i ) * W ( i ) N i ;
Wherein, SR (a) represents the ranking value of described current sequence object; F represents to adjust the factor; W (i) represents weight coefficient, the positive rating of the untie-sell rate * object (i) of described W (i)=object (i), and described N is described first time of value, described n is the object number relevant with described current sequence object.
7. a server, is characterized in that, described server comprises:
Receiving element, for receiving searching request;
Search unit, for according to described searching request, search the sequence object corresponding with keyword in described searching request;
Processing unit, for returning to the ranking value of described sequence object, and returns to the forward one or more described sequence object of described ranking value with reference to described ranking value;
Wherein, described processing unit also comprises and obtains subelement, builds subelement and computation subunit, and the described ranking value in described processing unit is behavioral data and the information data of obtaining subelement and obtain described sequence object by described; Build subelement and build object relationship figure according to described behavioral data and information data; Computation subunit utilizes described object relationship figure to calculate.
8. server according to claim 7, is characterized in that, the object relationship figure building in described structure subelement comprises at least two nodes and at least 1 limit;
Described node represents described sequence object;
Described limit represents the degree of association between any two described sequence objects, and the described degree of association characterizes the number of times of any user to two described sequence objects generation joint acts;
The described degree of association obtains according to described behavioral data and information data statistics.
9. server according to claim 8, is characterized in that, described in obtain subelement also for, from described object relationship figure, obtain the mark of the represented described sequence object of described node;
Described processing unit also comprises: chooser unit, for selecting a described node from described node as current sequence object;
Storing sub-units, for being stored in identification list by the mark of the described sequence object relevant with described current sequence object.
10. server according to claim 9, is characterized in that, described computation subunit specifically for,
Using the mark of the mark of described current sequence object and the described sequence object relevant with described current sequence object as key word;
Give the initial ranking value of described current sequence object and the described sequence object relevant with described current sequence object;
Utilize described identification list, obtain the out-degree value of the described sequence object relevant to described current sequence object;
According to object level algorithms, utilize described initial ranking value and described out-degree value, calculate the ranking value SR (a) of described current sequence object.
11. servers according to claim 10, is characterized in that, the initial ranking value that described computation subunit is given described current sequence object is specially,
Initial ranking value using the ranking value SR of described current sequence object (a) as next described sequence object;
Or when described current sequence object is when sorting object described in first, give at random the initial ranking value of described current sequence object and the described sequence object relevant with described current sequence object.
12. servers according to claim 10, is characterized in that, in described computation subunit, the ranking value SR of current sequence object (a) is specially,
SR ( a ) = ( 1 - f ) + f * Σ i = 1 n = SR ( i ) * W ( i ) N i ;
Wherein, SR (a) represents the ranking value of described current sequence object; F represents to adjust the factor; W (i) represents weight coefficient, the positive rating of the untie-sell rate * object (i) of described W (i)=object (i), and described N is described first time of value, described n is the object number relevant with described current sequence object.
CN201210402829.5A 2012-10-22 2012-10-22 Searching method and server Active CN103778139B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201210402829.5A CN103778139B (en) 2012-10-22 2012-10-22 Searching method and server

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201210402829.5A CN103778139B (en) 2012-10-22 2012-10-22 Searching method and server

Publications (2)

Publication Number Publication Date
CN103778139A true CN103778139A (en) 2014-05-07
CN103778139B CN103778139B (en) 2017-09-19

Family

ID=50570382

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201210402829.5A Active CN103778139B (en) 2012-10-22 2012-10-22 Searching method and server

Country Status (1)

Country Link
CN (1) CN103778139B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111259272A (en) * 2020-01-14 2020-06-09 口口相传(北京)网络技术有限公司 Search result ordering method and device

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100543744C (en) * 2006-12-12 2009-09-23 孙斌 Method to webpage and website grading
CN101996215B (en) * 2009-08-27 2013-07-24 阿里巴巴集团控股有限公司 Information matching method and system applied to e-commerce website

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111259272A (en) * 2020-01-14 2020-06-09 口口相传(北京)网络技术有限公司 Search result ordering method and device

Also Published As

Publication number Publication date
CN103778139B (en) 2017-09-19

Similar Documents

Publication Publication Date Title
CN108121737B (en) Method, device and system for generating business object attribute identifier
US20200218737A1 (en) Method, system and program product for matching of transaction records
CN102236851B (en) The method and system that the multidimensional credit system composing power based on user calculates in real time
US9104968B2 (en) Identifying categorized misplacement
US8838521B2 (en) Systems and methods for trend aware self-correcting entity relationship extraction
CN104252456B (en) A kind of weight method of estimation, apparatus and system
CN104866474A (en) Personalized data searching method and device
CN103902545B (en) A kind of classification path identification method and system
CN108108380A (en) Search ordering method, searching order device, searching method and searcher
CN104424291A (en) Method and device for sorting search results
CN104699725A (en) Data searching processing method and system
CN103268348A (en) Method for identifying user query intention
CN103885971A (en) Data pushing method and data pushing device
CN106446189A (en) Message-recommending method and system
CN103309894B (en) Based on search implementation method and the system of user property
CN111612499B (en) Information pushing method and device, storage medium and terminal
CN110033331A (en) Method, system and terminal device for issuing coupons
CN105023178B (en) A kind of electronic commerce recommending method based on ontology
CN110825977A (en) Data recommendation method and related equipment
CN104951460A (en) Ranking parameter value determination method and device based on keyword clustering
CN104965863A (en) Object clustering method and apparatus
CN104536957B (en) Agricultural land circulation information retrieval method and system
CN107766229B (en) Method for evaluating correctness of commodity search system by using metamorphic test
Dong et al. Identification of international trade patterns of agricultural products: The evolution of communities and their core countries
CN103778139A (en) Search method and server

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant