CN103218364A - Searching method and system - Google Patents
Searching method and system Download PDFInfo
- Publication number
- CN103218364A CN103218364A CN2012100181493A CN201210018149A CN103218364A CN 103218364 A CN103218364 A CN 103218364A CN 2012100181493 A CN2012100181493 A CN 2012100181493A CN 201210018149 A CN201210018149 A CN 201210018149A CN 103218364 A CN103218364 A CN 103218364A
- Authority
- CN
- China
- Prior art keywords
- field
- newer
- destination object
- word
- index
- 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
Links
Images
Abstract
The invention provides a searching method and system, which relate to the technical field of network. The searching method comprises the following steps of utilizing a first separator to splice the header message field area and the attribute information field area of a target object to form a new field, and constructing an index based on the target object; after constructing the index, computing search terms of a user based on the index and the first separator, and computing a total relevancy of the search terms and the new field according to a field area, on which each query term of the new field is located; and returning at least one target object corresponding to the new field based on the total relevancy of each new field and the search terms. According to the searching method and the system, headers and brand messages of products are spliced to form the new field by utilizing the separator, and search engine index construction is carried out on the new field, so that a product result meeting the expectation of a user can be quickly returned; and in addition, text relevancy can be computed at one time only through incrementally updating the index once, so that the computing cost and the hardware resources are greatly reduced.
Description
Technical field
The application relates to networking technology area, particularly relates to a kind of searching method and system.
Background technology
A kind of product or commodity all can have a brand usually.Such as this commodity of sport footwear, the sport footwear of Adidas brand is arranged, the sport footwear of Nike brand is arranged, the sport footwear of Li Ning brand is arranged.For the quality of commodity, brand is the strongest evidence beyond doubt, is the assurance of service.Along with development of internet technology, increasing user buys commodity on the net, owing to lack the on-the-spot link of experiencing, so the brand message of commodity is particularly important especially to the quality of commodity, therefore in ecommerce, system returns accurately according to user's search word that brand message seems particularly important.
In the prior art, a kind of method is that a title to commodity carries out the keyword coupling, like this may output the brand article of non-user expectation, as search for Adidas, Search Results only can provide the commodity that contain multi-form key words such as Adidas, A Di, adidas in the commodity title, but brand generic that might these commodity is not the Adidas brand.In addition, for the commodity that itself are the Adidas brand, but because of not occurring the relevant brand keyword of Adidas in the title, and when searching order, miss easily.
Another kind method is to set up two independent engines, one is the title engine of commodity, one is the brand message engine, after the search word to the user carries out participle, query word behind the participle is carried out matching operation respectively in title engine and brand message engine, and then the result of calculation of two engines is combined the degree of correlation of calculating integral body.If but the title of commodity and brand have all been updated, need the index of 2 engines of incremental update simultaneously, even of only having revised title or brand message, also need to upgrade simultaneously 2 engines, this not only needs to increase more extra computation cost, and in each engine with no locator meams storing commodity, it is huger to assess the cost when upgrading engine index so, therefore, this method is handled slowly, maintenance cost is than higher, and hardware cost is also than higher, and is unfavorable for fast updating.
Summary of the invention
The application's technical matters to be solved provides a kind of searching method and system, and energy fast processing return results maintains easily, and maintenance cost is low.
In order to address the above problem, the application discloses a kind of searching method, comprising:
For the search word of user's input, obtain each query word of described search word correspondence;
At each query word that obtains, search and corresponding each index terms of each query word in index, described index makes up according to the field of destination object, and the field of described destination object comprises the newer field that the header information field district and the attribute information field area of destination object is spliced into by first separator;
According to each index terms in affiliated newer field the position and described newer field in the position of first separator, confirm that the query word of index terms correspondence belongs to the header information field district or belongs to the attribute information field area in affiliated newer field;
Calculate the total correlation degree of search word and this newer field according to field area under each query word place of described newer field; Described total correlation degree comprises first degree of correlation according to the weight calculation of field area under each query word place of described newer field;
Based on the total correlation degree of each newer field and search word, return the destination object of at least one newer field correspondence.
Preferably, described by first separator with the newer field that the header information field district and the attribute information field area of destination object is spliced into, may further comprise the steps:
Read the header information field district and the attribute information field area of destination object;
Replace character identical in the described newer field with blank character with first separator;
Header information field district and attribute information field area after replacing are spliced into a newer field by first separator.
Preferably, undertaken by following steps according to each the participle index building in the described field:
Carry out corresponding by second separator with corresponding newer field the sign of each destination object;
Each newer field is carried out the participle operation;
The participle that obtains with participle operation carries out corresponding with sign and this index terms position in each newer field of relevant each destination object index terms as index terms.
Preferably, confirm that by following steps described query word is to belong to the header information field district or belong to the attribute information field area:
According to the corresponding relation of described index terms with the sign of relevant each destination object, inquiry and the corresponding newer field of sign;
Position and first separator the position in described newer field of described index terms in described newer field compared, and the query word of confirming described index terms correspondence is to belong to the header information field district or belong to the attribute information field area.
Preferably, obtain described first degree of correlation by following steps:
Each query word string length divided by field area, place string length, is obtained the interval degree of correlation of each query word and field area, place;
With weight and the addition that each degree of correlation multiply by the field area, place, obtain first degree of correlation of search word and newer field.
Preferably, described search word comprises:
With the keyword of user's input as search word;
Perhaps, in the suggestion speech that returns of the input speech according to this user that the user is selected is as search word; Wherein, described suggestion speech obtains with corresponding result's click relation extraction by the input speech of user's input of statistics in advance.
Preferably, for the search word of user input, comprise when obtaining the query word of described search word:
The search word of importing for user error by the intelligent correction engine carries out error correction.
Preferably, described destination object comprises commodity; Described attribute information comprises the brand message of commodity.
Preferably, based on the total correlation degree of each newer field and search word, when exporting a newer field at least to user side:
At least export a destination object that ordering is forward; Described destination object sorts based on the total correlation degree of corresponding newer field and search word.
Accordingly, the application discloses a kind of searcher, comprising:
The query word acquisition module for the search word of user's input, obtains each query word of described search word correspondence;
The index terms search module, be used at each query word that obtains, search and corresponding each index terms of each query word in index, described index makes up according to the field of destination object, and the field of described destination object comprises the newer field that the header information field district and the attribute information field area of destination object is spliced into by first separator;
The location confirmation module, be used for position, confirm that the query word of index terms correspondence belongs to the header information field district or belongs to the attribute information field area in affiliated newer field according to each index terms first separator in the position of affiliated newer field and described newer field;
The relatedness computation module is used for the total correlation degree according to field area calculating search word and this newer field under each query word place of described newer field; Described total correlation degree comprises first degree of correlation according to the weight calculation of field area under each query word place of described newer field;
Output module is used for the total correlation degree based on each newer field and search word, returns the destination object of at least one newer field correspondence.
Compared with prior art, the application comprises following advantage:
The application is with the title of commodity and the brand message of commodity, utilize separator to be assembled into newer field, and then this newer field is carried out search engine index make up, but the commodity result who meets user's expectation by the application's fast return, and only needing incremental update one to search high and low for draws, when calculating text relevant, can once-through operation finish, this significantly reduces and assesses the cost and hardware resource.
Description of drawings
Fig. 1 is the schematic flow sheet of a kind of searching method of the application;
Fig. 2 is the structural representation of a kind of searcher of the application.
Embodiment
For above-mentioned purpose, the feature and advantage that make the application can become apparent more, the application is described in further detail below in conjunction with the drawings and specific embodiments.
With reference to Fig. 1, the schematic flow sheet that it shows a kind of searching method of the application comprises:
For the search word of user's input, such as " Adidas clover ", obtain its query word, in practice such as " Adidas ", " clover ".Generally can carry out the participle operation to the search word of user's input, search word such as user's input is " an Adidas clover ", then, then above-mentioned search word can be divided into two query words " Adidas ", " clover " according to the information and the longest match principle of commodity.
In practice, the search word for the user error input can carry out error correction by the intelligent correction engine.Such as, user input " A Di is big by four " is so according to the statistic analysis result of reality, " A Di is big by four " is in order to import " Adidas " basically, and the intelligent correction engine can be corrected as " Adidas " with " A Di is big by four " of user's input and carrying out subsequent treatment so.
In addition, for the search word of user input, can be with the keyword of user's input as search word.
The user directly with the keyword of oneself input as search word, such as user input " A Di ", the user directly puts and confirms search so, with this keyword as the search word that is input to search engine.
Perhaps, in the suggestion speech that the input speech according to this user that the user selects can be returned is as search word; Wherein, described suggestion speech obtains with corresponding result's click relation extraction by the input speech of user's input of statistics in advance.
Such as, the user imports " A Di ", system can return suggestion speech " Adidas ", " A Di king ", " Adidas clover " etc. according to the statistic analysis result of reality so, and the user can select one of them suggestion speech finally to be input to search engine as search word according to the demand of oneself.
In this application, preferred, described destination object comprises commodity; Described attribute information comprises the brand message of commodity.
Before the application handles the search word of user's input, also comprise index building, set up the step of search engine, specifically comprise:
Step S101 is spliced into newer field by first separator with the header information field district and the attribute information field area of destination object.
Preferably, the header information field district of splicing destination object by first separator becomes newer field to be undertaken by following steps with the attribute information field area:
Steps A 1 reads the header information field district and the attribute information field area of destination object.
In the reality, before splicing, need the heading message and the brand message of each destination object in the reading database, in the embodiment of the present application, described destination object comprises commodity, and described attribute information comprises the brand message of commodity.
Steps A 2 is replaced character identical with first separator in the described newer field with blank character.
Read the header information field district and the brand message field area of commodity earlier, and replace the character identical in header information field district and the brand message field area with first separator with the blank character string.
First separator is: separate the character of brand message and heading message in text, value in practice can adopt the symbol that does not often appear in commodity title or the brand message.Such as Zhi Biaofu t, space, slash, comma etc. all than being easier to appear in title or the brand message, therefore should not be as separator, and as ctrl+A, ascii code value 0x01 Huo ﹠﹠﹠'s and so on, generally can not appear in the text string, then can be with it as first separator.
In practice, after choosing first separator according to mentioned above principle, in the header information field district of commodity and brand message field area, may also there be the character identical with first separator, need so the character replacement identical with first separator in the header information field district of commodity and brand message field area fallen, so that subsequent treatment.
Steps A 3 is spliced into a newer field with header information field district and attribute information field area after replacing by first separator.
The brand of supposing a certain commodity is " clover ", title is " Adidas sport footwear 1 folding is dumped ", first separator is ctrl+A, the final character string that forms is: clover Adidas sport footwear 3 foldings are dumped, and the position of rs chacter is that 6 (position 0-5 is a clover, position 6 is a separator, and all the other are the commodity heading message).
Step S102 at described newer field index building, sets up search engine.
Preferably, undertaken by following steps according to each the participle index building in the described field:
Step B1 carries out corresponding by second separator with corresponding newer field the sign of each destination object.
Commodity generally carry out corresponding by its sign (being generally digital id) with the newer field of these commodity.
In practice, the file layout during the commodity storage is commodity digital id and new character strings, i.e. two fields of auction_id and brand_title: numeric type and character type.Open every (i.e. second separator) with the another one separator between these 2 row of numeric type and character type, and necessary different with first separator in the steps A 3.And fall for the character replacement identical in the header information field district of commodity and the brand message field area with second separator.
Such as for aforementioned be first separator with ctrl+A, and second separator adopts ||, the storage format for following two commodity is so:
" 12345|| clover Adidas sport footwear 1 folding is dumped "
" 2011 autumn of 83635789|| clover new product plate footwear, 8 folding bags are posted ".
Step B2 carries out the participle operation to each newer field.
Second separator among the step B1 || the newer field of back is carried out participle, and word segmentation result is followed successively by:
" clover Adidas sport footwear 1 folding is dumped "
" 2011 autumn of clover new product plate footwear, 8 folding bags are posted "
In practice, when when setting up index each newer field being handled, participle according to the actual requirements, such as, except above-mentioned word segmentation result, also can tell speech such as " three leaves ", " A Di " for " clover Adidas sport footwear 1 folding is dumped ".
Step B3, the participle that obtains with participle operation carries out corresponding with sign and this index terms position in each newer field of relevant each destination object index terms as index terms.
Such as setting up index for aforementioned word segmentation result, commodity digital id (auction_id) is followed behind index terms, the result who then sets up index is:
Clover---12345_0,83635789_0
Adidas---12345_7
Sport footwear---12345_15
1 folding---12345_21
Dump---12345_24
2011 autumns---83635789_7
New product---83635789_13
Plate footwear---83635789_17
8 folding---83635789_21
Bag is posted---83535789_24
The index front be index terms, the commodity id that can relate to for this participle of back, and the position that occurs of participle, (mark for the position can adopt " _ " or ": " or the like) is pressed into index in the internal memory, guarantees its search efficiency at a high speed.Wherein, Chinese character can be designated as 2 bytes.
So, at above-mentioned index, according to query word " Adidas " and " clover " that step 110 obtains, search search engine with above-mentioned two speech respectively, the result is: Adidas: 12345_7; Clover: 12345_0 and 83635789_0.
To Search Results, calculate the word string index position of each search word in character string, and compare with the position of separator, in previous example, the brand message field area is preceding, the header information field district after, so if the position of index terms in newer field of query word correspondence less than the first separator position, show that then participle is present in the brand message field area, if the position of index terms in newer field of query word correspondence shows then that greater than the first separator position participle is present in the commodity header information field district.
Such as the Search Results in index in the previous example be: Adidas: 12345_7; Clover: 12345_0 and 83635789_0, setting up structure and can determine the commodity id and the position of first separator in newer field that match according to index." Adidas " position in the newer field of 12345 correspondences is 7 so, and is bigger than the position 6 of first separator in this newer field, and search word " Adidas " belongs to the header information field district of 12345 corresponding newer fields so; " clover " position in the newer field of 12345 correspondences is 0, and is littler than the position 6 of first separator in this newer field, and search word " clover " belongs to the brand message field area of 12345 corresponding newer fields so.
Step 140 is according to the total correlation degree of field area calculating search word and this newer field under each query word place of described newer field; Described total correlation degree comprises first degree of correlation that the weight calculation of the affiliated field area, each query word place of the described newer field of foundation obtains.
According to the result of above-mentioned steps to each participle of search word, COMPREHENSIVE CALCULATING user's inputted search speech whether with the interval degree of correlation in brand message field area or header information field district, and calculate the total correlation degree of search word and this newer field.Can be divided into following 4 classes in practice:
A) search word mates brand and title simultaneously;
B) search word only mates brand;
C) search word only mates title;
D) search word and brand and title all do not match.
Preferably, obtain described first degree of correlation by following steps:
Step C1 divided by field area, place string length, obtains the degree of correlation of each query word and field area, place with each query word string length.
In practice, calculate the interval degree of correlation of brand of query word and brand message field area by length (participle)/length (brand message); By the degree of correlation between the header area in length (participle)/length (heading message) calculating query word and header information field district; Wherein length (participle) represents the string length of query word, the string length of length (brand message) expression brand message field area, the string length in length (heading message) expression header information field district.
Step C2 with weight and the addition that described each interval degree of correlation multiply by the field area, place, obtains first degree of correlation of search word and newer field.
In practice, by " brand message field area weight * length (participle)/length (brand message)+header information field district weight * length (participle)/length (heading message) " total correlation degree as commodity.Be respectively 0.3 and 0.7 such as brand message field area weight and header information field district, formula is so: 0.3*length (participle)/length (brand message)+0.7*length (participle)/length (heading message).
Such as for previous example:
For 12345, i.e. 0.3*6/6+0.7*8/21=0.56
For 83635789, i.e. 0.3*6/6+0.7*0/21=0.3
So for the search word of " Adidas clover ", the total correlation degree of " clover Adidas sport footwear 1 folding is dumped " will be higher than " 2011 autumn of clover new product plate footwear, 8 folding bags are posted ", also is that the total correlation degree of commodity 12345 will be higher than commodity 83635789.
Also can calculate the degree of correlation of search word and newer field by other modes, the application is not limited it.
Comprised described first degree of correlation in the wherein said total correlation degree, obviously, can also as sales volume, prestige etc., finally obtain described total correlation degree with reference to other guide.
In practice, based on the total correlation degree of each newer field and search word, when exporting a newer field at least, can be to user side:
At least export a destination object that ordering is forward; Described destination object sorts based on the total correlation degree of corresponding newer field and search word.
Obtain the degree of correlation of each newer field and search word in the previous example, the destination object that also promptly obtains each newer field correspondence is the degree of correlation of commodity and search word, commodity and the merchandise news of returning user side can be returned according to the ordering of total correlation degree so.In practice, for a plurality of commodity in the same class, also can browse in conjunction with the sales volume of commodity, comprehensive conditions such as concern and corresponding seller finally sort, and then return to user side.
With reference to Fig. 2, the structural representation that it shows a kind of searcher of the application comprises:
Query word acquisition module 210 for the search word of user input, obtains each query word of described search word correspondence;
Index terms search module 220, be used at each query word that obtains, search and corresponding each index terms of each query word in index, described index makes up according to the field of destination object, and the field of described destination object comprises the newer field that the header information field district and the attribute information field area of destination object is spliced into by first separator;
Wherein, at total correlation degree, when exporting a newer field at least: export a destination object that ordering is forward at least to user side based on each newer field and search word; Described destination object sorts based on the total correlation degree of corresponding newer field and search word.
Preferably, header information field district and the attribute information field area with destination object is spliced into a newer field by carrying out with lower module by first separator:
The information acquisition module is used to read the header information field district and the attribute information field area of destination object;
The character replacement module is replaced character identical with first separator in the described newer field with blank character;
Concatenation module, header information field district and attribute information field area after being used for will replacing by first separator are spliced into newer field.
Preferably, according to each the participle index building in the described field by carrying out with lower module:
The destination object respective modules is used for carrying out corresponding by second separator with corresponding newer field the sign of each destination object;
Newer field query word acquisition module carries out the participle operation to each newer field;
The index construct module, the participle that obtains with participle operation carries out corresponding with sign and this index terms position in each newer field of relevant each destination object index terms as index terms.
Preferably, by confirming that with lower module described query word is to belong to the header information field district or belong to the attribute information field area:
The newer field enquiry module is used for according to the corresponding relation of described index terms with the sign of relevant each destination object, inquiry and the corresponding newer field of sign;
Query word location confirmation module, be used for position and first separator the position in described newer field of described index terms in described newer field compared, the query word of confirming described index terms correspondence is to belong to the header information field district or belong to the attribute information field area.
Preferably, by obtain described total correlation degree with lower module:
Field area relatedness computation module is used for each query word string length obtaining the degree of correlation of each query word and field area, place divided by field area, place string length;
Total correlation degree computing module is used for each degree of correlation be multiply by the weight and the addition of field area, place, obtains the total correlation degree of search word and newer field.
Preferably, the application also comprises:
Intelligence engine is used for carrying out error correction by the search word that the intelligent correction engine is imported for user error.
Preferably, the application also comprises:
Suggestion speech engine is used for returning the suggestion speech according to this user's input speech.
For system embodiment, because it is similar substantially to method embodiment, so description is fairly simple, relevant part gets final product referring to the part explanation of method embodiment.
Each embodiment in this instructions all adopts the mode of going forward one by one to describe, and what each embodiment stressed all is and the difference of other embodiment that identical similar part is mutually referring to getting final product between each embodiment.
The application can describe in the general context of the computer executable instructions of being carried out by computing machine, for example program module.Usually, program module comprises the routine carrying out particular task or realize particular abstract, program, object, assembly, data structure or the like.Also can in distributed computing environment, put into practice the application, in these distributed computing environment, by by communication network connected teleprocessing equipment execute the task.In distributed computing environment, program module can be arranged in the local and remote computer-readable storage medium that comprises memory device.
More than to a kind of searching method and system that the application provided, be described in detail, used specific case herein the application's principle and embodiment are set forth, the explanation of above embodiment just is used to help to understand the application's method and core concept thereof; Simultaneously, for one of ordinary skill in the art, according to the application's thought, the part that all can change in specific embodiments and applications, in sum, this description should not be construed as the restriction to the application.
Claims (10)
1. a searching method is characterized in that, comprising:
For the search word of user's input, obtain each query word of described search word correspondence;
At each query word that obtains, search and corresponding each index terms of each query word in index, described index makes up according to the field of destination object, and the field of described destination object comprises the newer field that the header information field district and the attribute information field area of destination object is spliced into by first separator;
According to each index terms in affiliated newer field the position and described newer field in the position of first separator, confirm that the query word of index terms correspondence belongs to the header information field district or belongs to the attribute information field area in affiliated newer field;
Calculate the total correlation degree of search word and this newer field according to field area under each query word place of described newer field; Described total correlation degree comprises first degree of correlation according to the weight calculation of field area under each query word place of described newer field;
Based on the total correlation degree of each newer field and search word, return the destination object of at least one newer field correspondence.
2. method according to claim 1 is characterized in that, described by first separator with the newer field that the header information field district and the attribute information field area of destination object is spliced into, may further comprise the steps:
Read the header information field district and the attribute information field area of destination object;
Replace character identical in the described newer field with blank character with first separator;
Header information field district and attribute information field area after replacing are spliced into a newer field by first separator.
3. method according to claim 1 is characterized in that, is undertaken by following steps according to each the participle index building in the described field:
Carry out corresponding by second separator with corresponding newer field the sign of each destination object;
Each newer field is carried out the participle operation;
The participle that obtains with participle operation carries out corresponding with sign and this index terms position in each newer field of relevant each destination object index terms as index terms.
4. method according to claim 3 is characterized in that, confirms that by following steps described query word is to belong to the header information field district or belong to the attribute information field area:
According to the corresponding relation of described index terms with the sign of relevant each destination object, inquiry and the corresponding newer field of sign;
Position and first separator the position in described newer field of described index terms in described newer field compared, and the query word of confirming described index terms correspondence is to belong to the header information field district or belong to the attribute information field area.
5. according to one of them described method of claim 1, it is characterized in that, obtain described first degree of correlation by following steps:
Each query word string length divided by field area, place string length, is obtained the interval degree of correlation of each query word and field area, place;
With weight and the addition that each degree of correlation multiply by the field area, place, obtain first degree of correlation of search word and newer field.
6. method according to claim 1 is characterized in that, described search word comprises:
With the keyword of user's input as search word;
Perhaps, in the suggestion speech that returns of the input speech according to this user that the user is selected is as search word; Wherein, described suggestion speech obtains with corresponding result's click relation extraction by the input speech of user's input of statistics in advance.
7. method according to claim 1 is characterized in that, for the search word of user input, comprises when obtaining the query word of described search word:
The search word of importing for user error by the intelligent correction engine carries out error correction.
8. method according to claim 1 is characterized in that:
Described destination object comprises commodity; Described attribute information comprises the brand message of commodity.
9. method according to claim 1 is characterized in that, based on the total correlation degree of each newer field and search word, when exporting a newer field at least to user side:
At least export a destination object that ordering is forward; Described destination object sorts based on the total correlation degree of corresponding newer field and search word.
10. a searcher is characterized in that, comprising:
The query word acquisition module for the search word of user's input, obtains each query word of described search word correspondence;
The index terms search module, be used at each query word that obtains, search and corresponding each index terms of each query word in index, described index makes up according to the field of destination object, and the field of described destination object comprises the newer field that the header information field district and the attribute information field area of destination object is spliced into by first separator;
The location confirmation module, be used for position, confirm that the query word of index terms correspondence belongs to the header information field district or belongs to the attribute information field area in affiliated newer field according to each index terms first separator in the position of affiliated newer field and described newer field;
The relatedness computation module is used for the total correlation degree according to field area calculating search word and this newer field under each query word place of described newer field; Described total correlation degree comprises first degree of correlation according to the weight calculation of field area under each query word place of described newer field;
Output module is used for the total correlation degree based on each newer field and search word, returns the destination object of at least one newer field correspondence.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210018149.3A CN103218364B (en) | 2012-01-19 | 2012-01-19 | A kind of searching method and system |
| HK13110910.1A HK1183540A1 (en) | 2012-01-19 | 2013-09-25 | Method for searching and system thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210018149.3A CN103218364B (en) | 2012-01-19 | 2012-01-19 | A kind of searching method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103218364A true CN103218364A (en) | 2013-07-24 |
| CN103218364B CN103218364B (en) | 2016-05-04 |
Family
ID=48816167
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210018149.3A Active CN103218364B (en) | 2012-01-19 | 2012-01-19 | A kind of searching method and system |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN103218364B (en) |
| HK (1) | HK1183540A1 (en) |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544266A (en) * | 2013-10-16 | 2014-01-29 | 北京奇虎科技有限公司 | Method and device for generating search suggestion words |
| CN103678560A (en) * | 2013-12-06 | 2014-03-26 | 乐视网信息技术（北京）股份有限公司 | Multimedia resource error correction searching method and system and multimedia resource server |
| CN103838883A (en) * | 2014-03-31 | 2014-06-04 | 上海久科信息技术有限公司 | Intelligent SKU matching method |
| CN104063523A (en) * | 2014-07-21 | 2014-09-24 | 焦点科技股份有限公司 | E-commerce search scoring and ranking method and system |
| CN104252534A (en) * | 2014-09-12 | 2014-12-31 | 百度在线网络技术（北京）有限公司 | Search method and search device |
| CN104268298A (en) * | 2014-10-27 | 2015-01-07 | 中电海康集团有限公司 | Method for creating database index and inquiring data |
| CN106874442A (en) * | 2017-02-08 | 2017-06-20 | 三和智控(北京)系统集成有限公司 | Named by data name and realize data from the method and device for carrying characteristic information |
| CN106919603A (en) * | 2015-12-25 | 2017-07-04 | 北京奇虎科技有限公司 | The method and apparatus for calculating participle weight in query word pattern |
| CN107066533A (en) * | 2017-03-01 | 2017-08-18 | 北京奇艺世纪科技有限公司 | Search inquiry error correction system and method |
| CN108572992A (en) * | 2017-03-14 | 2018-09-25 | 苏宁云商集团股份有限公司 | A kind of method and device of commodity sequence |
| CN108875743A (en) * | 2017-05-15 | 2018-11-23 | 阿里巴巴集团控股有限公司 | A kind of text recognition method and device |
| CN110309266A (en) * | 2019-07-05 | 2019-10-08 | 拉扎斯网络科技（上海）有限公司 | Object search method, apparatus, electronic equipment and storage medium |
| CN110941765A (en) * | 2019-12-04 | 2020-03-31 | 青梧桐有限责任公司 | Search intention identification method, information search method and device and electronic equipment |
| CN111143582A (en) * | 2019-12-04 | 2020-05-12 | 青岛聚看云科技有限公司 | Multimedia resource recommendation method and device for updating associative words in real time through double indexes |
| CN111767451A (en) * | 2020-01-15 | 2020-10-13 | 北京沃东天骏信息技术有限公司 | Searching method, electronic equipment and computer readable storage medium |
| CN113468393A (en) * | 2021-06-09 | 2021-10-01 | 北京达佳互联信息技术有限公司 | Index generation method and device, electronic equipment and storage medium |
| CN111767451B (en) * | 2020-01-15 | 2024-04-16 | 北京沃东天骏信息技术有限公司 | Searching method, electronic device and computer readable storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1593054A2 (en) * | 2003-02-13 | 2005-11-09 | Sap Ag | Adapting business objects for searches and searching adapted business objects |
| CN101043353A (en) * | 2006-03-25 | 2007-09-26 | 中兴通讯股份有限公司 | Process for improving data-handling efficiency of network management system |
| CN101206674A (en) * | 2007-12-25 | 2008-06-25 | 北京科文书业信息技术有限公司 | Enhancement type related search system and method using commercial articles as medium |
| CN101609460A (en) * | 2009-07-22 | 2009-12-23 | 中国科学院地理科学与资源研究所 | A kind of search method and searching system of supporting the heterogeneous earth science data resource |
| CN101645086A (en) * | 2009-08-28 | 2010-02-10 | 用友软件股份有限公司 | Retrieval method |
| CN102117320A (en) * | 2011-01-11 | 2011-07-06 | 百度在线网络技术（北京）有限公司 | Structured data searching method and device |
| US20110276564A1 (en) * | 2007-04-19 | 2011-11-10 | Retrevo Inc. | Indexing and searching product identifiers |
-
2012
- 2012-01-19 CN CN201210018149.3A patent/CN103218364B/en active Active
-
2013
- 2013-09-25 HK HK13110910.1A patent/HK1183540A1/en unknown
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1593054A2 (en) * | 2003-02-13 | 2005-11-09 | Sap Ag | Adapting business objects for searches and searching adapted business objects |
| CN101043353A (en) * | 2006-03-25 | 2007-09-26 | 中兴通讯股份有限公司 | Process for improving data-handling efficiency of network management system |
| US20110276564A1 (en) * | 2007-04-19 | 2011-11-10 | Retrevo Inc. | Indexing and searching product identifiers |
| CN101206674A (en) * | 2007-12-25 | 2008-06-25 | 北京科文书业信息技术有限公司 | Enhancement type related search system and method using commercial articles as medium |
| CN101609460A (en) * | 2009-07-22 | 2009-12-23 | 中国科学院地理科学与资源研究所 | A kind of search method and searching system of supporting the heterogeneous earth science data resource |
| CN101645086A (en) * | 2009-08-28 | 2010-02-10 | 用友软件股份有限公司 | Retrieval method |
| CN102117320A (en) * | 2011-01-11 | 2011-07-06 | 百度在线网络技术（北京）有限公司 | Structured data searching method and device |
Non-Patent Citations (1)
| Title |
|---|
| 韩希先: "基于分类树的P2P电子商务平台搜索机制的研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (24)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544266A (en) * | 2013-10-16 | 2014-01-29 | 北京奇虎科技有限公司 | Method and device for generating search suggestion words |
| CN103678560A (en) * | 2013-12-06 | 2014-03-26 | 乐视网信息技术（北京）股份有限公司 | Multimedia resource error correction searching method and system and multimedia resource server |
| CN103838883A (en) * | 2014-03-31 | 2014-06-04 | 上海久科信息技术有限公司 | Intelligent SKU matching method |
| CN104063523A (en) * | 2014-07-21 | 2014-09-24 | 焦点科技股份有限公司 | E-commerce search scoring and ranking method and system |
| CN104252534A (en) * | 2014-09-12 | 2014-12-31 | 百度在线网络技术（北京）有限公司 | Search method and search device |
| CN104252534B (en) * | 2014-09-12 | 2017-05-10 | 百度在线网络技术（北京）有限公司 | Search method and search device |
| CN104268298B (en) * | 2014-10-27 | 2018-05-04 | 中电海康集团有限公司 | A kind of method for creating database index and its inquiry |
| CN104268298A (en) * | 2014-10-27 | 2015-01-07 | 中电海康集团有限公司 | Method for creating database index and inquiring data |
| CN106919603B (en) * | 2015-12-25 | 2020-12-04 | 北京奇虎科技有限公司 | Method and device for calculating word segmentation weight in query word mode |
| CN106919603A (en) * | 2015-12-25 | 2017-07-04 | 北京奇虎科技有限公司 | The method and apparatus for calculating participle weight in query word pattern |
| CN106874442B (en) * | 2017-02-08 | 2023-08-18 | 三和智控(北京)系统集成有限公司 | Method and device for realizing self-carrying characteristic information of data through naming of data name |
| CN106874442A (en) * | 2017-02-08 | 2017-06-20 | 三和智控(北京)系统集成有限公司 | Named by data name and realize data from the method and device for carrying characteristic information |
| CN107066533B (en) * | 2017-03-01 | 2020-10-27 | 北京奇艺世纪科技有限公司 | Search query error correction system and method |
| CN107066533A (en) * | 2017-03-01 | 2017-08-18 | 北京奇艺世纪科技有限公司 | Search inquiry error correction system and method |
| CN108572992A (en) * | 2017-03-14 | 2018-09-25 | 苏宁云商集团股份有限公司 | A kind of method and device of commodity sequence |
| CN108875743A (en) * | 2017-05-15 | 2018-11-23 | 阿里巴巴集团控股有限公司 | A kind of text recognition method and device |
| CN108875743B (en) * | 2017-05-15 | 2022-02-22 | 创新先进技术有限公司 | Text recognition method and device |
| CN110309266A (en) * | 2019-07-05 | 2019-10-08 | 拉扎斯网络科技（上海）有限公司 | Object search method, apparatus, electronic equipment and storage medium |
| CN111143582A (en) * | 2019-12-04 | 2020-05-12 | 青岛聚看云科技有限公司 | Multimedia resource recommendation method and device for updating associative words in real time through double indexes |
| CN110941765A (en) * | 2019-12-04 | 2020-03-31 | 青梧桐有限责任公司 | Search intention identification method, information search method and device and electronic equipment |
| CN111143582B (en) * | 2019-12-04 | 2023-09-22 | 青岛聚看云科技有限公司 | Multimedia resource recommendation method and device for updating association words in double indexes in real time |
| CN111767451A (en) * | 2020-01-15 | 2020-10-13 | 北京沃东天骏信息技术有限公司 | Searching method, electronic equipment and computer readable storage medium |
| CN111767451B (en) * | 2020-01-15 | 2024-04-16 | 北京沃东天骏信息技术有限公司 | Searching method, electronic device and computer readable storage medium |
| CN113468393A (en) * | 2021-06-09 | 2021-10-01 | 北京达佳互联信息技术有限公司 | Index generation method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| HK1183540A1 (en) | 2013-12-27 |
| CN103218364B (en) | 2016-05-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103218364A (en) | Searching method and system | |
| CN100514337C (en) | Association information generating system of key words and generation method thereof | |
| CN101876981B (en) | A kind of method and device building knowledge base | |
| CN103729359B (en) | A kind of method and system recommending search word | |
| CN102023989B (en) | Information retrieval method and system thereof | |
| CN102236663B (en) | Query method, query system and query device based on vertical search | |
| CN102760138B (en) | Classification method and device for user network behaviors and search method and device for user network behaviors | |
| CN100452042C (en) | Digital string fuzzy match method | |
| CN102722498B (en) | Search engine and implementation method thereof | |
| CN104933239A (en) | Hybrid model based personalized position information recommendation system and realization method therefor | |
| CN102968465B (en) | Network information service platform and the search service method based on this platform thereof | |
| CN102722499B (en) | Search engine and implementation method thereof | |
| CN102708130A (en) | Scalable engine that computes user micro-segments for offer matching | |
| CN103399883A (en) | Method and system for performing personalized recommendation according to user interest points/concerns | |
| CN101650717A (en) | Method and system for saving storage space of database | |
| CN103136262A (en) | Information retrieval method and device | |
| CN103092943A (en) | Method of advertisement dispatch and advertisement dispatch server | |
| CN105630827A (en) | Information processing method and system, and auxiliary system | |
| CN102982023A (en) | Method and device for providing search suggestion | |
| CN103123632A (en) | Determining method for searching headword and device of searching headword, searching method and searching equipment | |
| CN103984705A (en) | Search result displaying method, device and system | |
| CN104951460A (en) | Ranking parameter value determination method and device based on keyword clustering | |
| CN103942268A (en) | Method and device for combining search and application and application interface | |
| CN111597219B (en) | Searching method and system based on relationship attribute | |
| CN103927177A (en) | Characteristic-interface digraph establishment method based on LDA model and PageRank algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: DE Ref document number: 1183540 Country of ref document: HK |
|
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| REG | Reference to a national code |
Ref country code: HK Ref legal event code: GR Ref document number: 1183540 Country of ref document: HK |