CN110727801A - Ontology-based fuzzy evaluation search engine advertisement optimization method - Google Patents
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Abstract
The invention belongs to the field of data processing, in particular to a fuzzy evaluation search engine advertisement optimization method based on ontology, which comprises the following steps: s1: constructing a search engine advertisement semantic model of the ontology, describing search engine advertisement field knowledge at a semantic level and multiplexing and integrating the existing ontology model to realize information exchange and sharing; s2: based on a multi-attribute entropy weight fuzzy comprehensive evaluation algorithm, calculating attribute weights by using information entropy, constructing a membership matrix by using triangular fuzzy numbers, constructing a multi-attribute comment evaluation coordinate system according to an optimization target and an optimization rule, and calculating by using a fuzzy operator to obtain a final optimized subset. The technology can automatically realize the early work (rough optimization) of the search engine advertisement optimization, and complete the more advanced deep optimization to a certain extent, thereby effectively saving the labor cost for enterprises and improving the investment return rate of the search engine popularization.
Description
Technical Field
The invention belongs to the field of data processing, and particularly relates to a fuzzy evaluation search engine advertisement optimization method based on an ontology.
Background
The Search Engine advertisement has the characteristics of strong pertinence, low cost, high flexibility, easy release and the like compared with the traditional advertisement media, the advertiser mainly focuses on three aspects in the whole Search Engine advertisement putting process, namely ① creating advertisement ② monitoring data ③ to optimize the advertisement, and Search Engine advertisement Optimization (Search Engine Optimization) becomes the focus of the current advertiser in order to make the advertisement putting more effective.
Currently, relevant research focuses on both model-based and rule-based optimization. However, the advertisement promotion effect is a result of multi-attribute comprehensive influence, and the ideal advertisement delivery effect cannot be achieved by predicting a single attribute before advertisement delivery by adopting model-based optimization. Rule-based optimization is actually a process of comprehensively evaluating advertisement attributes according to optimization objectives and related optimization rules, screening out an optimization subset conforming to the rules, and implementing an optimization strategy. In the process, advertisement subsets obtained by comprehensive evaluation and screening are used as implementation objects of the optimization strategy, and the quality of the advertisement subsets directly influences the optimization effect. Today, an advertiser may have tens of thousands to hundreds of thousands of advertisements, and how to accurately locate a proper advertisement in a large amount of advertisement data to implement an optimization strategy has become a great challenge. Traditional SEO relies entirely on manual analysis, screening of data. The manual optimization method mainly has the following defects:
1) the manual analysis process often depends on field experience, and the subjectivity is strong.
2) Optimization goals typically involve multiple advertising attributes, and manual optimization approaches have difficulty in making accurate comprehensive assessments of the numerous attributes that have complex associations.
3) The fuzzy-described optimization rules have difficulty in establishing an accurate mapping relationship with the numerically-described advertisement attributes, which further increases the difficulty for the site-optimized subsets.
Disclosure of Invention
Aiming at the problems, the invention provides a search engine advertisement semantic model based on an ontology, aiming at describing search engine advertisement field knowledge and multiplexing and integrating the existing ontology model at a semantic level so as to realize information exchange and sharing; on the other hand, a search engine advertisement optimization strategy based on multi-attribute entropy weight fuzzy comprehensive evaluation is provided, the algorithm uses information entropy to calculate attribute weight, a triangular fuzzy number is used for constructing a membership matrix, a multi-attribute comment evaluation coordinate system is constructed according to an optimization target and an optimization rule, and a final optimization subset is obtained through fuzzy operator calculation, and the specific method comprises the following steps:
a fuzzy evaluation search engine advertisement optimization method based on ontology is characterized in that: the method comprises the following steps:
s1: constructing a search engine advertisement semantic model of the ontology, describing search engine advertisement field knowledge at a semantic level and multiplexing and integrating the existing ontology model, and realizing information exchange and sharing:
s2: based on a multi-attribute entropy weight fuzzy comprehensive evaluation algorithm, calculating attribute weights by using information entropy, constructing a membership matrix by using triangular fuzzy numbers, constructing a multi-attribute comment evaluation coordinate system according to an optimization target and an optimization rule, and calculating by using a fuzzy operator to obtain a final optimized subset.
Further, the step S1 includes the following steps:
s11: formalized definition of search engine advertisements, wherein the organization structure of the search engine advertisements is an advertisement instance set consisting of a plurality of advertisement instances; the advertisement instance is described by an advertisement attribute set, an advertisement data set and an advertisement behavior set triple;
s12: designing an organization structure of a search engine advertisement semantic model, and abstracting the semantic model into three top-level objects including a core attribute, a core object and a core behavior according to triple definitions of advertisement instances; meanwhile, in order to enhance the semantic representation capability of the model, Adfeature is extended to be used as a subclass of a core object to describe advertisement characteristics.
S13: constructing a search and talk engine advertisement semantic model of the ontology: the ontology construction method based on the spiral model is adopted, the semantic model of the search engine ontology is defined as a quintuple O (C, P, I, H, A), C represents a concept set, P represents an attribute set, I represents an instance set, H describes the hierarchical relationship of C, P, I, and A represents an inference rule set; s12, mapping the class, relation and organization structure in the semantic model into a concept set C, an attribute P, a hierarchical relation H and an inference rule A in the ontology model, and then filling an instance set I into the ontology semantic model.
Further, the step S2 includes the following steps:
s21: establishing a multi-factor comment index system:
(1) mapping the SEOBehavior class into an optimization target set T ═ T according to an optimization behavior knowledge set defined in a search engine advertisement body modeliI ∈ N }, where T isiThe ith optimization behavior object is in the SEAO model, and the ith optimization target is represented in the optimization target set T;
(2) optimizing a target TiThe technique defines the evaluation factor set U ═ U as being associated with SEORule by the hasSEOR attribute, while the hasDelatedData attribute describes the advertising data associated with SEORule1，…，Uj，…UqIn which U isjTo optimize the target TiThe corresponding j-th correlation factor;
(3) the DataType type defined in the SEAO model describes the data type of the search engine advertising effect data, and the data type is numerical type, composite type and nominal type respectively;
(4) the nominal data value field has an enumeration property, and a comment set V is defined as { Ture, False }; the set of numeric and coincidence data comments is defined as V ═ { High, Middle, Low };
(5) defining a multi-factor comment evaluation index system S ═<U+，T+>Wherein U is+Set of assessment factors, T, representing semantic description of the attached comment+Representing the optimization target with comment semantic description, then optimizing the target TiMulti-factor comment evaluation index system for evaluation factor set U
S22: establishing an evaluation matrix:
(1) with m advertisements, the evaluation matrix is initialized to
xhjA real value representing the h advertisement on the i effect attribute;
(2) normalizing the initialized matrix X to obtain a standard matrix omega
h∈[1，m]，m∈N*，j∈[1，k]，k∈N*
Wherein
S23: determining a weight vector: calculating objective weight according to each index by using an information entropy tool; using an expert investigation method to count subjective weights of all indexes; nominal index and T+The index does not participate in the weight calculation, and defines the weight vector of the factor set
S24: fuzzy comprehensive evaluation: the method comprises the following specific steps:
(1) initializing membership matrix
The technique uses Boolean functions and triangular fuzzy to calculate fuzzy membership, which is defined as follows:
wherein z is a sliding constant, q1Is the lower quartile, q2Is a median, q3Is the upper quartile, q4＝q3+1.5IQR, IQR is a quartering distance;
(2) will (x)hj) Substituting the formula (7) to calculate an evaluation membership degree matrix R,
(3) calculating a weighting matrix
Firstly, according to the membership degree matrix R, eliminating the samples with the membership degree of 0 in the KPI index rows and the nominal index rows. Then calculating a weighting matrix
(4) Combining the evaluation results
To weighting matrixCarrying out statistical summation to obtain a multi-factor comment evaluation coordinate system S of each advertisement instanceiEvaluation result vector A of
And sorting the result vector A, and selecting to obtain a final optimized subset by referring to an optimization strength threshold delta given by an expert.
Further, the ontology construction method based on the spiral model comprises the five steps of ① mapping objects in a knowledge set to be a concept set C of the ontology model according to the extracted hierarchical structure, ② constructing a property set P according to the extracted association relation, ③ extracting advertisement examples in a data source to establish an example set, ④ constructing an inference rule set according to the inference relation among the concept set, the property set and the example set, ⑤ evaluating the model according to an expert evaluation standard, and repeating the steps of 1-4 until a complete ontology model is obtained.
Further, w in the step S23jThe specific calculation steps are as follows:
(1) the entropy of the j index is calculated from the normalization matrix Ω:
wherein
(2) Calculate the difference of the jth index:
dj＝1-ej
(3) determining the entropy weight of the index j:
(4) determining the weight of the index j:
wj＝λjα+βj(1-α)，α∈[0，1]
wherein alpha is a regulatory factor, betajSubjective weights given to experts.
The invention provides a search engine advertisement semantic model based on an ontology, which aims to describe search engine advertisement field knowledge and multiplex and integrate the existing ontology model at a semantic level so as to realize information exchange and sharing; and on the other hand, a search engine advertisement optimization strategy based on multi-attribute entropy weight fuzzy comprehensive evaluation is provided, the algorithm uses the information entropy to calculate the attribute weight, a triangular fuzzy number is used for constructing a membership matrix, a multi-attribute comment evaluation coordinate system is constructed according to an optimization target and an optimization rule, and a final optimization subset is obtained through fuzzy operator calculation. The technology can automatically realize the early work (rough optimization) of the search engine advertisement optimization, and complete the more advanced deep optimization to a certain extent, thereby effectively saving the labor cost for enterprises and improving the investment return rate of the search engine popularization.
Drawings
FIG. 1 is an organizational structure of a search engine advertisement as defined by the present invention.
FIG. 2 is an organizational structure of a search engine advertising semantic model designed according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings.
The invention provides a fuzzy evaluation search engine advertisement optimization method based on a body, which comprises two technical cores:
(1) providing a search engine advertisement semantic model based on an ontology, aiming at describing search engine advertisement field knowledge at a semantic level and multiplexing and integrating the existing ontology model so as to realize information exchange and sharing;
(2) the algorithm uses the information entropy to calculate the attribute weight, utilizes the triangular fuzzy number to construct a membership matrix, constructs a multi-attribute comment evaluation coordinate system according to an optimization target and an optimization rule, and obtains a final optimization subset through fuzzy operator calculation.
Through the mutual combination of the two, the manual optimization process can be effectively simulated, the defects of the traditional manual optimization are avoided, and the automatic processing of the search engine advertisement optimization is realized. The specific implementation steps are as follows:
1. ontology-based search engine advertisement semantic model
The method for constructing the search engine advertisement semantic model of the ontology mainly comprises the following steps:
(11) the organization structure of the search engine advertisement is defined as shown in figure one. The formalization of a search engine advertisement is defined as follows:
definition 1: SEA ═ Adi|Adi＝<P，D，H>，1≤i≤n，n∈N+}
Definition 2: p ═ Domain priority, AdStructure }
Definition 3: d ═ Di|di＝<AD，BasicInfo，ED，TD，DT>，1≤i≤n，n∈N+}
Definition 4: h ═ CreateBehavior, SEOBehavior }
Where SEA is a set of advertisement instances, ad instances Adi are described by a triple of a set of ad attributes P, a set of ad data D, and a set of ad behaviors H. The advertisement attribute set P is composed of a domain concept knowledge set domainpriority and an advertisement structure knowledge set adstrucurepriority, as defined in definition 2; definition 3 gives a formal description of an advertisement data set D, wherein AD is attribute data, Basicinfo is basic information data, ED is effect data, TD is flow data, and DT is a data type; h is the set of behaviors imposed on top of the instance of the ad, including the set of ad creation behaviors CreatBehavior and the ad optimization behavior SEOBehavior, given by definition 4.
(12) The organizational structure of the semantic model of the search engine advertisement is designed, as shown in figure two. According to the triple definition of the advertisement, the semantic model is abstracted into three top-level objects of a core attribute, a core object and a core behavior. Meanwhile, in order to enhance the semantic representation capability of the model, Adfeature is expanded to be used as a subclass of a core object for describing advertisement characteristics.
(13) And constructing an ontology semantic model. And adopting a body construction method based on a spiral model. The search engine ontology model is defined as five-tuple O ═ C, (P, I, H, a), C represents a concept set, P is an attribute set, I is an instance set, H describes the hierarchical relationship of C, P, I, and a is an inference rule set. The class, relation and organizational structure in the semantic model are mapped into a concept set C, an attribute P, a hierarchical relation H and an inference rule A in the ontology model. Instance set A is then populated into the onto-model. The specific model construction steps are as follows:
a) defining the definition category of the ontology, and determining the category of the ontology: considering first the possibility of reusing existing ontologies, no more sophisticated search engine advertising ontology has been proposed. The search engine advertisement ontology should therefore have the ability to describe both search engine advertisement domain knowledge and to integrate existing ontologies as a top-level ontology.
b) Collecting a data source and extracting a knowledge set: and extracting to obtain a knowledge set according to a representation frame defined by the search engine advertisement semantic model.
c) The ontology model building method based on the spiral model mainly comprises the five steps of ① mapping objects in a knowledge set into a concept set C of the ontology model according to an extracted hierarchical structure, ② building a property set P according to an extracted incidence relation, ③ extracting advertisement examples in a data source to build an example set, ④ building an inference rule set according to an inference relation among the concept set, the property set and the example set, ⑤ evaluating the model according to an expert evaluation standard, and repeating 1-4 steps until a complete ontology model is obtained
2. Multi-attribute entropy weight fuzzy comprehensive evaluation algorithm
The algorithm mainly comprises the following steps:
(21) establishing a multi-factor comment coordinate system: mapping the SEOBehavior class into an optimization target set T ═ T according to an optimization behavior knowledge set defined in a search engine advertisement body modeliL 1 ∈ N }, where TiIn the SEAO model is the ith optimization behavior object, which represents the ith optimization objective in the optimization objective set T. Optimizing a target TiThe technique defines the evaluation factor set U ═ U as being associated with SEORule by the hasSEOR attribute, while the hasDelatedData attribute describes the advertising data associated with SEORule1，…，Uj，…UqIn which U isjTo optimize the target TiThe corresponding j-th correlation factor. The DataType class defined in the SEAO model describes the data types of the search engine advertisement effectiveness data, numeric, composite, and nominal, respectively. The nominal data value field has an enumeration property, and a comment set V is defined as { Ture, False }; the numeric and coincidence data panel is defined as V ═ High, Middle, Low. Defining a multi-factor comment evaluation index system S ═<U+，T+>Wherein U is+Set of assessment factors, T, representing semantic description of the attached comment+Representing the optimization target with comment semantic description, then optimizing the target TiMulti-factor comment evaluation index system for evaluation factor set U
For example: comment evaluation index system<High display, low consumption, medium impact amount, medium conversion amount and medium conversion rate>Wherein U is+＝<High exhibition, low consumption, middle striking amount and transfer amount>，T+＝<Medium conversion rate>。
(22) Establishing an evaluation matrix:
with m advertisements, the evaluation matrix is initialized to
xhjRepresenting the true value of the h ad on the i-th performance attribute. Normalizing the initialized matrix X to obtain a standard matrix omega
Wherein
(23) Determining a weight vector
The entropy characterizes the purity of any sample set. The larger the information entropy of the evaluation factor is, the larger the variation degree of the value is, the more information is provided in the comprehensive evaluation process, and the larger the weight of the index is. Therefore, the objective weight of each index is calculated by using the information entropy tool. On the other hand, subjective weights of the respective indices are counted using an expert survey method. Due to the nominal index and T+The index has semantic duality, so the index does not participate in weight calculation. Defining a factor set weight vectorwjThe specific calculation steps are as follows:
a) the entropy of the j index is calculated from the normalization matrix Ω:
wherein
b) Calculate the difference of the jth index:
dj＝1-ej
c) determining the entropy weight of the index j:
d) determining the weight of the index j:
wj＝λjα+βj(1-α)，α∈[0，1]
wherein alpha is a regulatory factor, betajSubjective weights given to experts.
(24) Fuzzy comprehensive evaluation
a) Initializing membership matrix
The technique uses Boolean functions and triangular fuzzy to calculate fuzzy membership, which is defined as follows:
wherein q is1Is the lower quartile, q2Is a median, q3Is the upper quartile, q4＝q3+1.5IQR, IQR is a quartering distance.
Will (x)hj) Substituting the formula (7) to calculate an evaluation membership degree matrix R,
b) calculating a weighting matrix
Firstly, according to the membership degree matrix R, eliminating the samples with the membership degree of 0 in the KPI index rows and the nominal index rows. Then calculating a weighting matrix
c) Combining the evaluation results
To weighting matrixCarrying out statistical summation to obtain a multi-factor comment evaluation coordinate system S of each advertisement instanceiEvaluation result vector A of
And sorting the result vector A, and selecting to obtain a final optimized subset by referring to an optimization strength threshold delta given by an expert.
Claims (5)
1. A fuzzy evaluation search engine advertisement optimization method based on ontology is characterized in that: the method comprises the following steps:
s1: constructing a search engine advertisement semantic model of the ontology, describing search engine advertisement field knowledge at a semantic level and multiplexing and integrating the existing ontology model to realize information exchange and sharing;
s2: based on a multi-attribute entropy weight fuzzy comprehensive evaluation algorithm, calculating attribute weights by using information entropy, constructing a membership matrix by using triangular fuzzy numbers, constructing a multi-attribute comment evaluation coordinate system according to an optimization target and an optimization rule, and calculating by using a fuzzy operator to obtain a final optimized subset.
2. The ontology-based fuzzy evaluation search engine advertisement optimization method of claim 1, wherein: the step S1 includes the following steps:
s11: formalized definition of search engine advertisements, wherein the organization structure of the search engine advertisements is an advertisement instance set consisting of a plurality of advertisement instances; the advertisement instance is described by an advertisement attribute set, an advertisement data set and an advertisement behavior set triple;
s12: designing an organization structure of a search engine advertisement semantic model, and abstracting the semantic model into three top-level objects including a core attribute, a core object and a core behavior according to triple definitions of advertisement instances; meanwhile, in order to enhance the semantic representation capability of the model, Adfeature is extended to be used as a subclass of a core object to describe advertisement characteristics.
S13: constructing a search and talk engine advertisement semantic model of the ontology: the ontology construction method based on the spiral model is adopted, the semantic model of the search engine ontology is defined as a quintuple O (C, P, I, H, A), C represents a concept set, P represents an attribute set, I represents an instance set, H describes the hierarchical relationship of C, P, I, and A represents an inference rule set; s12, mapping the class, relation and organization structure in the semantic model into a concept set C, an attribute P, a hierarchical relation H and an inference rule A in the ontology model, and then filling an instance set I into the ontology semantic model.
3. The ontology-based fuzzy evaluation search engine advertisement optimization method of claim 1, wherein: the step S2 includes the following steps:
s21: establishing a multi-factor comment index system:
(1) mapping the SEOBehavior class into an optimization target set T ═ T according to an optimization behavior knowledge set defined in a search engine advertisement body modeli|i∈N*Where T isiIn the SEAO model is the ith optimizationBehavior objects, representing the ith optimization objective in the optimization objective set T;
(2) optimizing a target TiThe technique defines the evaluation factor set U ═ U as being associated with SEORule by the hasSEOR attribute, while the hasDelatedData attribute describes the advertising data associated with SEORule1，…，Uj，…UqIn which U isjTo optimize the objective, TiThe corresponding j-th correlation factor;
(3) the DataType type defined in the SEAO model describes the data type of the search engine advertising effect data, and the data type is numerical type, composite type and nominal type respectively;
(4) the nominal data value field has an enumeration property, and a comment set V is defined as { Ture, False }; the set of numeric and coincidence data comments is defined as V ═ { High, Middle, Low };
(5) defining a multi-factor comment evaluation index system S ═<U+，T+>Wherein U is+Set of assessment factors, T, representing semantic description of the attached comment+Representing the optimization target with comment semantic description, then optimizing the target TiMulti-factor comment evaluation index system for evaluation factor set U
S22: establishing an evaluation matrix:
(1) with m advertisements, the evaluation matrix is initialized to
(2) normalizing the initialized matrix X to obtain a standard matrix omega
Wherein
S23: determining a weight vector: calculating objective weight according to each index by using an information entropy tool; using an expert investigation method to count subjective weights of all indexes; nominal index and T+The index does not participate in the weight calculation, and defines the weight vector of the factor set
S24: fuzzy comprehensive evaluation: the method comprises the following specific steps:
(1) initializing membership matrix
The technique uses Boolean functions and triangular fuzzy to calculate fuzzy membership, which is defined as follows:
wherein z is a sliding constant, q1Is the lower quartile, q2Is a median, q3Is the upper quartile, q4＝q3+1.5IQR, IQR is a quartering distance;
(2) will (x)hj) Evaluation by substituting formula (7)The matrix of degrees of membership R is,
(3) calculating a weighting matrix
Firstly, according to the membership degree matrix R, eliminating the samples with the membership degree of 0 in the KPI index rows and the nominal index rows. Then calculating a weighting matrix
(4) Combining the evaluation results
To weighting matrix
Carrying out statistical summation to obtain a multi-factor comment evaluation coordinate system S of each advertisement instanceiEvaluation result vector A of
And sorting the result vector A, and selecting to obtain a final optimized subset by referring to an optimization strength threshold delta given by an expert.
4. The ontology-based fuzzy evaluation search engine advertisement optimization method of claim 2, wherein the ontology construction method based on the spiral model is divided into five steps, ① mapping objects in a knowledge set to be a concept set C of the ontology model according to the extracted hierarchical structure, ② constructing an attribute set P according to the extracted association relationship, ③ extracting advertisement examples in a data source to establish an example set, ④ constructing an inference rule set according to the inference relationship among the concept set, the attribute set and the example set, ⑤ evaluating the model according to an expert evaluation standard, and repeating the steps 1-4 until a complete ontology model is obtained.
5. The ontology-based fuzzy evaluation search engine advertisement optimization method of claim 3, wherein: w in said step S23jThe specific calculation steps are as follows:
(1) the entropy of the j index is calculated from the normalization matrix Ω:
(2) Calculate the difference of the jth index:
dj＝1-ej
(3) determining the entropy weight of the index j:
(4) determining the weight of the index j:
wj＝λjα+βj(1-α)，α∈[0，1]
wherein alpha is a regulatory factor, betajSubjective weights given to experts.
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| CN112417160B (en) * | 2020-11-11 | 2023-07-25 | 北京机电工程总体设计部 | Semantic knowledge association method oriented to index system |
| CN117950787B (en) * | 2024-03-22 | 2024-05-31 | 成都赛力斯科技有限公司 | Advertisement display method and device, electronic equipment and storage medium |
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