CN106933954A - Search engine optimization technology is realized based on Decision Tree Algorithm - Google Patents

Search engine optimization technology is realized based on Decision Tree Algorithm Download PDF

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CN106933954A
CN106933954A CN201710046903.7A CN201710046903A CN106933954A CN 106933954 A CN106933954 A CN 106933954A CN 201710046903 A CN201710046903 A CN 201710046903A CN 106933954 A CN106933954 A CN 106933954A
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金平艳
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Sichuan Yonglian Information Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
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    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0255—Targeted advertisements based on user history
    • G06Q30/0256—User search
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    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/279—Recognition of textual entities
    • G06F40/289—Phrasal analysis, e.g. finite state techniques or chunking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis

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Abstract

Search engine optimization technology is realized based on Decision Tree Algorithm, kernel keyword, the corresponding data item of search keyword, such as national monthly volumes of searches, degree of contention and each clicking cost of estimation are determined according to business eventDeng, dimension-reduction treatment again is carried out to above-mentioned keyword set, each keyword First Five-Year Plan dimensional vector is represented, that is, increase homepage webpage number and total searched page number, and then is reduced to the four-dimension again by five dimensions, based on Decision Tree Algorithm, according to the object function of attribute SSelected from candidate attributeInput keyword attribute value, sorter model exports category result, the present invention considers attribute experience weight, the weight of training data attribute subordinate class and the cost produced by attribute misclassification, sorter model has the more preferable degree of accuracy, while facilitate subsequent prediction low with classification work, run time complexity, processing speed faster, with fine value, can be with fast lifting keyword ranking, so as to reach preferable web information flow target.

Description

Search engine optimization technology is realized based on Decision Tree Algorithm
Technical field
The present invention relates to Semantic Web technology field, and in particular to realize search engine optimization based on Decision Tree Algorithm Technology.
Background technology
As Internet technology is continued to develop, network information is increased rapidly, and it is quickly accurate that search engine is increasingly becoming user Really search the main tool of information.Search engine optimization, referred to as popular saying be by website overall architecture, web page contents, Link in keyword and webpage carries out the Optimization Work of correlation, improves its row in Search Results in particular search engine Name, so as to improve website visiting amount, the technology of the final sales force or publicity capacity for lifting website, search engine optimization is not single List will consider algorithm and the ranking rule of search engine, it is also contemplated that the searching preferences of user, choose keyword that user commonly uses, The comfort level for standing in user perspective to experience web page browsing is all good search engine optimization principle.At present on search engine The theoretical research of optimization method, compared with horn of plenty, is respectively domain name such as black cap technology and white cap technology, search engine optimization strategy Strategy, webpage design planning strategy, keyword strategy and link policy.SEO is the optimisation strategy of keyword after all, existing Modern theoretical research and technology application both at home and abroad to keyword optimization is relatively more, but temporarily does not propose an effective method to simplify Key word analysis flow, also neither one perfect mechanism manage keyword optimisation strategy and progress.Based on the demand, this Invention provides one kind and realizes search engine optimization technology based on Decision Tree Algorithm.
The content of the invention
The technical problem that search engine optimization is realized in keyword optimization is directed to, the invention provides based on decision tree classification Algorithm realizes search engine optimization technology.
In order to solve the above problems, the present invention is achieved by the following technical solutions：
Step 1：Kernel keyword is determined according to business event, related keyword is collected using search engine, these are crucial Word has corresponding data items in a search engine, such as national monthly volumes of searches, degree of contention and each clicking cost (CPC) of estimation
Step 2：With reference to enterprise product and market analysis, the above-mentioned related keyword set for searching of dimensionality reduction is screened；
Step 3：For the keyword set after screening dimensionality reduction, by the corresponding page of search engine search keyword, this In record homepage webpage number and total searched page number, i.e. each keyword dimensionality reduction be four-dimensional again by five dimensional vectors.
Step 4：Based on Decision Tree Algorithm, classification treatment is carried out to above-mentioned keyword, its specific sub-step is as follows：
Step 4.1：Decision tree classifier model is constructed according to training dataset, its specific sub-step is as follows：
Step 4.1.1：If training is concentrated with X sample, attribute number is 4, i.e. n=(S1, S2, S3, S4), while division Attribute SiK class, wherein L are corresponded tor∈(L1, L2..., Lk), i ∈ (1,2,3,4), r ∈ (1,2 ..., k).Association area user set Set misclassification cost matrix C.
Step 4.1.2：Create root node G.
Step 4.1.3：If training dataset is sky, returns to node G and mark failure.
Step 4.1.4：If training data concentrates all records to belong to same category, such phenotypic marker node G.
Step 4.1.5：If candidate attribute is sky, return G is leafy node, concentrates most common labeled as training data Class.
Step 4.1.6：Object function f according to attribute S selects splitS from candidate attribute.
Step 4.1.7：Flag node G is attribute splitSi。
Step 4.1.8：Extended by node and meet condition for splitS=splitSiBranch and splitSi= splitSijSub-branch, if meeting one of following two conditions, just stops contributing.
4.1.8.1 it is assumed here that YiiFor training data concentrates splitS=splitSiSample set, if YiiIt is sky, Plus a leafy node, most common class is concentrated labeled as training data.
4.1.8.2 all examples belong to same class in this node.
Step 4.1.9：Situation in non-4.1.8.1 and 4.1.8.2, then recursive call step 1.1.6 to step 4.1.8.
Step 4.1.10：The decision tree classifier that preservation has been generated.
Step 4.2：Using above-mentioned sorter model, the keyword to being obtained in step 3 is classified, you can obtained most Good k classes；
Step 5：According to enterprise's concrete condition, comprehensive keyword efficiency optimization and value rate optimize, and selection is suitable crucial Word optimisation strategy reaches web information flow target.
Present invention has the advantages that：
1, this algorithm can simplify key word analysis flow, and then reduce whole web information flow workload.
2, the run time complexity of this algorithm is low, and processing speed is faster.
3rd, this algorithm has bigger value.
4th, the ranking of website its keyword of fast lifting in a short time can be helped.
5th, for enterprise web site brings certain flow and inquiry, so as to reach preferable web information flow target.
6th, the degree of accuracy of this algorithm classification result more meets empirical value；
7th, first constitute model to use again, facilitate subsequent prediction with classification work.
8th, attribute experience weight had both been considered, weight and the attribute misclassification institute of training data attribute subordinate class had been combined again The cost of generation, makes sorter model have the more preferable degree of accuracy.
Brief description of the drawings
Fig. 1 realizes search engine optimization technical pattern flow chart based on Decision Tree Algorithm
Fig. 2 is based on the applicating flow chart of Decision Tree Algorithm
Specific embodiment
In order to solve the technical problem that search engine optimization is realized in keyword optimization, the present invention is carried out with reference to Fig. 1-Fig. 2 Describe in detail, its specific implementation step is as follows：
Step 1：Kernel keyword is determined according to business event, related keyword is collected using search engine, these are crucial Word has corresponding data items in a search engine, such as national monthly volumes of searches, degree of contention and each clicking cost (CPC) of estimation
Step 2：With reference to enterprise product and market analysis, the above-mentioned related keyword set for searching of dimensionality reduction is screened；
Step 3：For the keyword set after screening dimensionality reduction, by the corresponding page of search engine search keyword, this In record homepage webpage number and total searched page number, i.e. each keyword dimensionality reduction be four-dimensional, its specific meter again by five dimensional vectors Calculation process is as follows：
Here associative key number is m, existing following m × 5 matrix：
Ni、Ldi、CPCi、NiS、NiYIt is followed successively by monthly volumes of searches, degree of contention, the estimation of i-th corresponding this country of keyword Each clicking cost (CPC), homepage webpage number, total searched page number.
Dimensionality reduction is the four-dimension again, i.e.,
XI ∈ (1,2 ..., m)It is search efficiency, ZI ∈ (1,2 ..., m)It is value rate, as following formula：
Step 4：Based on Decision Tree Algorithm, classification treatment is carried out to above-mentioned keyword, its specific sub-step is as follows：
Step 4.1：Decision tree classifier model is constructed according to training dataset, its specific sub-step is as follows：
Step 4.1.1：If training is concentrated with X sample, attribute number is 4, i.e. n=(S1, S2, S3, S4), while division Attribute SiK class, wherein L are corresponded tor∈(L1, L2..., Lk), i ∈ (1,2,3,4), r ∈ (1,2 ..., k).Association area user set Misclassification cost matrix C is set, its specific calculating process is as follows：
Misclassification cost matrix C：
(ci1..., cik) it is the keyword i points of cost caused by each class；
Step 4.1.2：Create root node G.
Step 4.1.3：If training dataset is sky, returns to node G and mark failure.
Step 4.1.4：If training data concentrates all records to belong to same category, such phenotypic marker node G.
Step 4.1.5：If candidate attribute is sky, return G is leafy node, concentrates most common labeled as training data Class.
Step 4.1.6：Object function f according to attribute S selects splitS from candidate attribute, and its specific calculating process is such as Under：
Above formula n (S) is the number of attribute S correspondence class data objects, and m is total data object, cn(s)For n (S) is individual in class Data misclassification cost summation, α is the experience weight of attribute S, and α can be given by corresponding domain expert.
According to f from being arranged in order four attribute above to small greatly；
Step 4.1.7：Flag node G is attribute splitSi。
Step 4.1.8：Extended by node and meet condition for splitS=splitSiBranch and splitSi= splitSijSub-branch, if meeting one of following two conditions, just stops contributing.
4.1.8.1 it is assumed here that YiFor training data concentrates splitS=splitSiSample set, if YiIt is sky, plus A upper leafy node, most common class is concentrated labeled as training data.
4.1.8.2 all examples belong to same class in this node.
Step 4.1.9：Situation in non-4.1.8.1 and 4.1.8.2, then recursive call step 1.1.6 to step 4.1.8.
Step 4.1.10：The decision tree classifier that preservation has been generated.
Step 4.2：Using above-mentioned sorter model, the keyword to being obtained in step 3 is classified, you can obtained most Good k classes；
Step 5：According to enterprise's concrete condition, comprehensive keyword efficiency optimization and value rate optimize, and selection is suitable crucial Word optimisation strategy reaches web information flow target.

Claims (2)

1. search engine optimization technology is realized based on Decision Tree Algorithm, the present invention relates to Semantic Web technology field, specifically It is related to realize search engine optimization technology based on Decision Tree Algorithm, it is characterized in that, comprise the following steps：
Step 1：Kernel keyword is determined according to business event, related keyword is collected using search engine, these keywords exist There are corresponding data items in search engine, such as national monthly volumes of searches, degree of contention and each clicking cost of estimationDeng
Step 2：With reference to enterprise product and market analysis, the above-mentioned related keyword set for searching of dimensionality reduction is screened；
Step 3：For the keyword set after screening dimensionality reduction, by the corresponding page of search engine search keyword, remember here Dimensionality reduction is four-dimensional again by five dimensional vectors for record homepage webpage number and total searched page number, i.e. each keyword, and it was specifically calculated Journey is as follows：
Here associative key number is m, existing followingMatrix：
、、、、It is followed successively by monthly volumes of searches, degree of contention, the estimation of i-th corresponding this country of keyword Each clicking cost（CPC）, homepage webpage number, total searched page number dimensionality reduction again
It is the four-dimension, i.e.,
It is search efficiency,It is value rate, as following formula：
Step 4：Based on Decision Tree Algorithm, classification treatment is carried out to above-mentioned keyword, its specific sub-step is as follows：
Step 4.1：Decision tree classifier model is constructed according to training dataset, its specific sub-step is as follows：
Step 4.1.1：If training is concentrated with X sample, attribute number is 4, i.e.,, while dividing category PropertyK class has been corresponded to, wherein, , , phase Close field user and set misclassification cost matrix C
Step 4.1.2：Create root node G
Step 4.1.3：If training dataset is sky, returns to node G and mark failure
Step 4.1.4：If training data concentrates all records to belong to same category, such phenotypic marker node G
Step 4.1.5：If candidate attribute is sky, return G is leafy node, and most common class is concentrated labeled as training data
Step 4.1.6：Object function f according to attribute S is selected from candidate attribute
Step 4.1.7：Flag node G is attribute
Step 4.1.8：Extended by node and meet condition and beBranch and Sub-branch, if meeting one of following two conditions, just stops contributing
4.1.8.1 it is assumed here thatFor training data is concentratedSample set, ifIt is sky, plus A upper leafy node, most common class is concentrated labeled as training data
4.1.8.2 all examples belong to same class in this node
Step 4.1.9：Situation in non-4.1.8.1 and 4.1.8.2, then recursive call step 1.1.6 to step 4.1.8
Step 4.1.10：The decision tree classifier that preservation has been generated
Step 4.2：Using above-mentioned sorter model, the keyword to being obtained in step 3 is classified, you can obtain optimal k Class；
Step 5：According to enterprise's concrete condition, comprehensive keyword efficiency optimization and value rate optimize, and select suitable keyword excellent Change strategy and reach web information flow target.
2. search engine optimization technology is realized based on Decision Tree Algorithm according to claim 1, it is characterized in that, with Specific calculating process in the upper step 4 is as follows：
Step 4：Based on Decision Tree Algorithm, classification treatment is carried out to above-mentioned keyword, its specific sub-step is as follows：
Step 4.1：Decision tree classifier model is constructed according to training dataset, its specific sub-step is as follows：
Step 4.1.1：If training is concentrated with X sample, attribute number is 4, i.e.,, while dividing category PropertyK class has been corresponded to, wherein, , , it is related Field user sets misclassification cost matrix C, and its specific calculating process is as follows：
Misclassification cost matrix C：
For the keyword i points of cost caused by each class；
Step 4.1.2：Create root node G
Step 4.1.3：If training dataset is sky, returns to node G and mark failure
Step 4.1.4：If training data concentrates all records to belong to same category, such phenotypic marker node G
Step 4.1.5：If candidate attribute is sky, return G is leafy node, and most common class is concentrated labeled as training data
Step 4.1.6：Object function f according to attribute S is selected from candidate attribute, its specific calculating process is as follows：
Above formulaIt is the number of attribute S correspondence class data objects,QUOTE is total data object,For in classIndividual data misclassification cost summation,It is the experience weight of attribute S,Can be given by corresponding domain expert
According toFrom being arranged in order four attribute above to small greatly；
Step 4.1.7：Flag node G is attribute
Step 4.1.8：Extended by node and meet condition and beBranch and Sub-branch, if meeting one of following two conditions, just stops contributing
4.1.8.1 it is assumed here thatFor training data is concentratedSample set, ifIt is sky, adds One leafy node, most common class is concentrated labeled as training data
4.1.8.2 all examples belong to same class in this node
Step 4.1.9：Situation in non-4.1.8.1 and 4.1.8.2, then recursive call step 1.1.6 to step 4.1.8
Step 4.1.10：The decision tree classifier that preservation has been generated
Step 4.2：Using above-mentioned sorter model, the keyword to being obtained in step 3 is classified, you can obtain optimal k Class.
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CN108595508A (en) * 2018-03-22 2018-09-28 佛山市顺德区中山大学研究院 A kind of adaptive index construction method and system based on Suffix array clustering
CN108776679A (en) * 2018-05-30 2018-11-09 百度在线网络技术（北京）有限公司 A kind of sorting technique of search term, device, server and storage medium
CN109214117A (en) * 2018-10-15 2019-01-15 南京天洑软件有限公司 A kind of intelligent industrial algorithm for design based on value network
CN109464199A (en) * 2017-09-07 2019-03-15 西门子医疗有限公司 Determine the method and data processing unit for adjusting the classification data for checking agreement
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109464199A (en) * 2017-09-07 2019-03-15 西门子医疗有限公司 Determine the method and data processing unit for adjusting the classification data for checking agreement
CN108595508A (en) * 2018-03-22 2018-09-28 佛山市顺德区中山大学研究院 A kind of adaptive index construction method and system based on Suffix array clustering
CN108595508B (en) * 2018-03-22 2020-11-13 佛山市顺德区中山大学研究院 Adaptive index construction method and system based on suffix array
CN108776679A (en) * 2018-05-30 2018-11-09 百度在线网络技术（北京）有限公司 A kind of sorting technique of search term, device, server and storage medium
CN109214117A (en) * 2018-10-15 2019-01-15 南京天洑软件有限公司 A kind of intelligent industrial algorithm for design based on value network
CN116467059A (en) * 2023-04-21 2023-07-21 哈尔滨有初科技有限公司 Data processing system and method based on distributed computing

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Application publication date: 20170707