CN109785064A - A kind of mobile e-business recommended method and system based on Multi-source Information Fusion - Google Patents
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Abstract
The mobile e-business recommended method based on Multi-source Information Fusion that the invention discloses a kind of, include: S1: obtaining user information and corresponding consumption data from multiple information sources, user information and corresponding consumption data to acquisition pre-process, to obtain original evidence body, if the original evidence body is divided into Ganlei；S2: being based on the original evidence body, by radial basis function and neural network algorithm, calculates the evidence weight of every class data in original evidence body, the evidence weight is used to distinguish the recommendation value of every class data；S3: according to the evidence weight, information fusion is carried out to original evidence body with D-S evidence theory, to obtain new evidence body；S4: the new evidence body is handled using the Power Spectrum Estimation Method, to obtain recommendation decision.The present invention, which can effectively increase, recommends accuracy, indirect and coverage rate, improves recommendation service performance.
Description
Technical Field
The invention relates to the technical field of information fusion, in particular to a mobile electronic commerce recommendation method and system based on multi-source information fusion.
Background
As the consumption habits of users change, mobile electronic commerce has become a trend. However, continuously generating a large amount of data is not only inconvenient for consumers in searching for meaningful products, but also means that some products are rarely purchased. The lack of in-depth mining of user and resource information has become a major bottleneck restricting predictive analysis of mobile commerce recommendation systems.
The recommendation system invented to solve the above problems and widely used by the electronic commerce practitioners is now an important research topic of information science and decision support systems. Currently, research into recommendation systems generally includes content-based filtering (CBF), Collaborative Filtering (CF), and other data mining techniques, such as decision trees, association rules, and semantic methods.
The existing e-commerce recommendation system has two problems. First, e-commerce recommendation systems are not deep enough to mine the online behavior of consumers in multi-source mining. The recommendation system only concerns the information of the products and the shopping behavior of the consumer on the shopping platform, and therefore the accuracy of the recommendation is limited. Secondly, the existing mobile e-commerce recommendation system does not integrate the user location information, and the coverage of recommendation is limited.
Yager, Gregor and many other scholars have intensively studied the network trust model of multi-source information fusion framework, information classification, automatic reasoning, heterogeneous data processing, cloud computing and point-to-point (P2P) information fusion. Most information fusion models are JDL models established based on the department of defense, and meet the requirement of fusing multi-source information from four different processing levels. With the development of multi-source information fusion technology research, the multi-source information fusion technology has been used for aspects of pattern recognition, data mining, knowledge discovery and the like. However, there is less research into multi-source information fusion based on location in a mobile e-commerce recommendation system.
Disclosure of Invention
The invention aims to provide a mobile electronic commerce recommendation method and system based on multi-source information fusion, which comprises the steps of firstly, obtaining user information and data according to two types of information inside and outside a consumption platform; secondly, calculating a recommended evidence weight through a radial basis function and a neural network algorithm; modifying the evidence body by using the feasibility degree by using a D-S evidence theory to perform information fusion; and finally, comprehensively considering various factors such as timeliness, stability and comprehensiveness of the information, and processing the fusion result by adopting a power spectrum estimation method to finally obtain a recommendation decision. The invention can effectively increase the recommendation accuracy, the indirection and the coverage rate and improve the recommendation service performance.
To achieve the above object, with reference to fig. 1, the present invention provides a mobile electronic commerce recommendation method based on multi-source information fusion, the method comprising:
s1: acquiring user information and corresponding consumption data from a plurality of information sources, and preprocessing the acquired user information and the corresponding consumption data to acquire an original evidence body, wherein the original evidence body is divided into a plurality of classes;
s2: calculating the evidence weight of each type of data in the original evidence body through a radial basis function and a neural network algorithm based on the original evidence body, wherein the evidence weight is used for distinguishing the recommendation value of each type of data;
s3: performing information fusion on the original evidence body by using a D-S evidence theory according to the evidence weight to obtain a new evidence body;
s4: and processing the new evidence body by adopting a power spectrum estimation method to obtain a recommendation decision.
In a further embodiment, the step S1, the obtaining user information and corresponding consumption data from a plurality of information sources includes:
the user characteristic data and the high-frequency attention data of the user are extracted through the nickname-mobile phone number, and the high-frequency search data and the historical purchase information of the user are mined from the database of each shopping platform through the platform ID-mobile phone number.
In a further embodiment, in step S1, the preprocessing the acquired user information and the corresponding consumption data includes:
and representing, storing, integrating and managing the acquired user information and the corresponding consumption data by adopting a micro-format.
In a further embodiment, in step S2, the method for calculating the evidence weight of each type of data in the original evidence body through the radial basis function and the neural network algorithm based on the original evidence body includes:
adopting a radial basis function neural network to calculate the evidence weight of each type of data in the original evidence body;
the hidden layer of the radial basis function neural network is a radial basis Gaussian layer, and the corresponding transfer function is a Gaussian function:
the output layer of the radial basis function neural network is a softmax layer, and the corresponding transfer function is as follows:
where c is the number of output classes, x is the neuron input, y is the neuron output, σjIs the variance, j is the number of inputs.
In a further embodiment, in step S3, the method for performing information fusion on the original evidence body by using the D-S evidence theory according to the evidence weight to obtain a new evidence body includes:
according to the D-S evidence theory, introducing evidence feasibility degree mu as a judgment standard to modify an original evidence body M, and calculating a basic probability distribution value of the modified original evidence body M according to the following formula:
M＝[m(A1)m(A2)…m(An)m(Θ)]
wherein,
fusing the modified original evidence body M according to a synthesis rule based on a local conflict distribution strategy to obtain a new evidence body, and calculating a basic probability distribution value of the new evidence body obtained after fusion by adopting the following formula:
when in useWhen the temperature of the water is higher than the set temperature, wherein A, B, C and D represent evidences, M represents a synthesis rule of the evidences, j represents the number of the evidences in the evidence body, theta represents an identification framework, k represents the conflict degree between the evidences, and M' represents the modified evidence body.
In a further embodiment, the influence factor of the evidence feasibility μ includes one or more of timeliness, stability and comprehensiveness of the information data.
In a further embodiment, the method further comprises:
the new evidence is normalized in response to synthesizing the new evidence every two original evidences.
In a further embodiment, in step S4, the processing the new evidence body by using a power spectrum estimation method to obtain the recommended decision means,
autocorrelation function according to user requirements and characteristic information x (n)The autocorrelation function represents the user requirements and characteristic information x of any two different users at different momentsN(n) and xN(n + m) of the power spectral density P, calculated from the correlationxx(ω)＝∑mΦxx(m)e-zωmThe power spectral density and the degree of correlation are a pair of fourier transforms.
Based on the method, the invention also provides a mobile electronic commerce recommendation system based on multi-source information fusion, and the system comprises:
a module for acquiring user information and corresponding consumption data from a plurality of information sources, and preprocessing the acquired user information and corresponding consumption data to acquire an original evidence body, wherein the original evidence body is divided into a plurality of classes;
a module for calculating an evidence weight of each type of data in the original evidence body based on the original evidence body through a radial basis function and a neural network algorithm, wherein the evidence weight is used for distinguishing a recommendation value of each type of data;
a module for performing information fusion on the original evidence body by using a D-S evidence theory according to the evidence weight to obtain a new evidence body;
and the module is used for processing the new evidence body by adopting a power spectrum estimation method to obtain a recommendation decision.
Compared with the prior art, the technical scheme of the invention has the following remarkable beneficial effects:
1) according to the two types of information inside and outside the consumption platform, user information and data are obtained, and the diversification of data sources is ensured.
2) The method has the advantages that the position information, the social platform comments, the product information and the user information are used in combination with the radial basis function neural network and the D-S evidence theory, so that the recommendation accuracy is effectively improved, and the accuracy defect that the conventional recommendation method only focuses on products and shopping information of users on a consumption platform and is not combined with other information of the users is overcome;
3) the feasibility is adopted to modify the evidence body for information fusion, so that the robustness and the one-ticket veto of the D-S theory are avoided;
4) the combination of the two aspects of modifying the evidence body by adopting the feasibility degree and distributing the synthesis rule according to the local conflict can improve the recommendation accuracy, reduce the response time of the recommendation system, increase the recommendation coverage rate and improve the recommendation service performance.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
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The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a mobile e-commerce recommendation method based on multi-source information fusion according to the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
With reference to fig. 1, the present invention provides a mobile electronic commerce recommendation method based on multi-source information fusion, where the method includes:
s1: the method comprises the steps of obtaining user information and corresponding consumption data from a plurality of information sources, and preprocessing the obtained user information and the corresponding consumption data to obtain an original evidence body, wherein the original evidence body is divided into a plurality of classes.
The information sources comprise cloud data, databases of various shopping platforms, webpage information and video information browsed by users, user data provided by search engines, chatting record data of the users, position information, voice, pictures, videos and the like stored on the mobile terminals of the users.
The invention provides that information is preferably acquired from all information sources which can acquire user information so as to comprehensively know the requirements of consumers and recommend products according to the requirements. Especially, the position information, the position of the consumer and the shopping behavior of the consumer have close relation, and in order to enable the recommendation system to play a larger role, the mobile e-commerce recommendation system can select to integrate the user position information, namely, a multi-source information fusion method is introduced into the integration of the position and historical behavior information.
The acquisition method comprises the following steps: the user characteristic data and the high-frequency attention data of the user are extracted through a nickname-mobile phone number, the high-frequency search data and historical purchase information of the user are mined from the database of each shopping platform through a platform ID-mobile phone number, and the like.
The method and the system aim at multi-source information fusion and decision recommendation in mobile electronic commerce, and effectively increase recommendation accuracy by using position information, social platform comments, product information and user information. The method overcomes the accuracy defect that the conventional recommendation method only focuses on products and shopping information of the user on a consumption platform and does not combine other information of the user.
In a further embodiment, in step S1, the preprocessing the acquired user information and the corresponding consumption data includes:
and representing, storing, integrating and managing the acquired user information and the corresponding consumption data by adopting a micro-format.
The acquired user information and the corresponding consumption data are preprocessed, so that the acquired data format is unified, and the user can read and/or process the data by a machine conveniently.
S2: and calculating the evidence weight of each type of data in the original evidence body through a radial basis function and a neural network algorithm based on the original evidence body, wherein the evidence weight is used for distinguishing the recommendation value of each type of data.
In particular, the invention calculates the evidence weight of each type of data in the original evidence body by using an improved radial basis function neural network.
The hidden layer of the radial basis function neural network is a radial basis Gaussian layer, and the corresponding transfer function is a Gaussian function:
the output layer of the radial basis function neural network is a softmax layer, and the corresponding transfer function is as follows:
where c is the number of output classes, x is the neuron input, y is the neuron output, σjIs the variance, j is the number of inputs.
S3: and according to the evidence weight, performing information fusion on the original evidence body by using a D-S evidence theory to obtain a new evidence body.
In order to avoid the robustness and the one-vote rejection of the D-S theory, based on the D-S evidence theory, the evidence feasibility mu is introduced as a judgment standard to modify the original evidence body M, and the basic probability distribution value of the modified original evidence body M is calculated according to the following formula:
M＝[m(A1)m(A2)…m(An)m(Θ)]
wherein,
preferably, the influence factor of the evidence feasibility μ includes one or more of timeliness, stability and comprehensiveness of the information data.
After the original evidence body M is modified, the modified original evidence body M is fused according to a synthesis rule based on a local conflict distribution strategy to obtain a new evidence body, and a basic probability distribution value of the new evidence body obtained after fusion is calculated by adopting the following formula:
when in useWhen the temperature of the water is higher than the set temperature, wherein A, B, C and D represent evidences, M represents a synthesis rule of the evidences, j represents the number of the evidences in the evidence body, theta represents an identification framework, k represents the conflict degree between the evidences, and M' represents the modified evidence body.
By adopting the feasibility degree to modify the evidence body and combining the two aspects of distributing and synthesizing rules according to local conflicts, the recommendation accuracy can be improved, the response time of a recommendation system can be reduced, and the recommendation coverage rate can be increased.
Preferably, the new evidence is normalized in response to synthesizing the new evidence every two original evidences for subsequent processing.
S4: and comprehensively considering various factors such as timeliness, stability and comprehensiveness of the information, and processing the new evidence body by adopting a power spectrum estimation method to obtain a recommendation decision.
Specifically, the autocorrelation function according to the user's requirement and the characteristic information x (n)The autocorrelation function represents the user requirements and characteristic information x of any two different users at different momentsN(n) and xN(n + m) of the power spectral density P, calculated from the correlationxx(ω)＝∑mΦxx(m)e-zωmSaid power spectral densityThe degree of correlation is a pair of fourier transforms.
Based on the method, the invention also provides a mobile electronic commerce recommendation system based on multi-source information fusion, which comprises the following modules:
1) the module is used for acquiring user information and corresponding consumption data from a plurality of information sources, and preprocessing the acquired user information and the corresponding consumption data to acquire an original evidence body, wherein the original evidence body is divided into a plurality of classes.
2) And the module is used for calculating the evidence weight of each type of data in the original evidence body through a radial basis function and a neural network algorithm based on the original evidence body, and the evidence weight is used for distinguishing the recommendation value of each type of data.
3) And the module is used for performing information fusion on the original evidence body by applying a D-S evidence theory according to the evidence weight so as to obtain a new evidence body.
4) And the module is used for processing the new evidence body by adopting a power spectrum estimation method to obtain a recommendation decision.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily defined to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.
Claims (9)
1. A mobile electronic commerce recommendation method based on multi-source information fusion is characterized by comprising the following steps:
s1: acquiring user information and corresponding consumption data from a plurality of information sources, and preprocessing the acquired user information and the corresponding consumption data to acquire an original evidence body, wherein the original evidence body is divided into a plurality of classes;
s2: calculating the evidence weight of each type of data in the original evidence body through a radial basis function and a neural network algorithm based on the original evidence body, wherein the evidence weight is used for distinguishing the recommendation value of each type of data;
s3: performing information fusion on the original evidence body by using a D-S evidence theory according to the evidence weight to obtain a new evidence body;
s4: and processing the new evidence body by adopting a power spectrum estimation method to obtain a recommendation decision.
2. The method for recommending mobile electronic commerce based on multi-source information fusion of claim 1, wherein in step S1, the step of obtaining user information and corresponding consumption data from multiple information sources comprises:
the user characteristic data and the high-frequency attention data of the user are extracted through the nickname-mobile phone number, and the high-frequency search data and the historical purchase information of the user are mined from the database of each shopping platform through the platform ID-mobile phone number.
3. The multi-source information fusion-based mobile e-commerce recommendation method of claim 1, wherein in step S1, the preprocessing of the obtained user information and the corresponding consumption data includes:
and representing, storing, integrating and managing the acquired user information and the corresponding consumption data by adopting a micro-format.
4. The mobile e-commerce recommendation method based on multi-source information fusion of claim 1, wherein in step S2, the method for calculating the evidence weight of each type of data in the original evidence body through the radial basis function and the neural network algorithm based on the original evidence body includes:
adopting a radial basis function neural network to calculate the evidence weight of each type of data in the original evidence body;
the hidden layer of the radial basis function neural network is a radial basis Gaussian layer, and the corresponding transfer function is a Gaussian function:
the output layer of the radial basis function neural network is a softmax layer, and the corresponding transfer function is as follows:
where c is the number of output classes, x is the neuron input, y is the neuron output, σjIs the variance, j is the number of inputs.
5. The mobile electronic commerce recommendation method based on multi-source information fusion of claim 1, wherein in step S3, the method for performing information fusion on the original evidence body by using D-S evidence theory according to the evidence weight to obtain a new evidence body comprises:
according to the D-S evidence theory, introducing evidence feasibility degree mu as a judgment standard to modify an original evidence body M, and calculating a basic probability distribution value of the modified original evidence body M according to the following formula:
M＝[m(A1)m(A2)…m(An)m(Θ)]
wherein m (A)m)＝{μm′(Am)|m＝1,2,…,n},
Fusing the modified original evidence body M according to a synthesis rule based on a local conflict distribution strategy to obtain a new evidence body, and calculating a basic probability distribution value of the new evidence body obtained after fusion by adopting the following formula:
when in useWhen the temperature of the water is higher than the set temperature, wherein A, B, C and D are shown in the tableShowing evidence, wherein M represents a synthesis rule of the evidence, j represents the number of the evidence in the evidence body, theta represents a recognition framework, k represents the degree of conflict between the evidence, and M' represents the modified evidence body.
6. The multi-source information fusion-based mobile e-commerce recommendation method according to claim 5, wherein the influence factors of the evidence feasibility μ include one or more of timeliness, stability and comprehensiveness of information data.
7. The multi-source information fusion-based mobile e-commerce recommendation method according to claim 1 or 5, further comprising:
the new evidence is normalized in response to synthesizing the new evidence every two original evidences.
8. The mobile e-commerce recommendation method based on multi-source information fusion of claim 1, wherein in step S4, the processing of the new evidence body by using the power spectrum estimation method to obtain the recommendation decision means,
autocorrelation function according to user requirements and characteristic information x (n)The autocorrelation function represents the user requirements and characteristic information x of any two different users at different momentsN(n) and xN(n + m) of the power spectral density P, calculated from the correlationxx(ω)＝∑mΦxx(m)e-zωmThe power spectral density and the degree of correlation are a pair of fourier transforms.
9. A mobile e-commerce recommendation system based on multi-source information fusion, the system comprising:
a module for acquiring user information and corresponding consumption data from a plurality of information sources, and preprocessing the acquired user information and corresponding consumption data to acquire an original evidence body, wherein the original evidence body is divided into a plurality of classes;
a module for calculating an evidence weight of each type of data in the original evidence body based on the original evidence body through a radial basis function and a neural network algorithm, wherein the evidence weight is used for distinguishing a recommendation value of each type of data;
a module for performing information fusion on the original evidence body by using a D-S evidence theory according to the evidence weight to obtain a new evidence body;
and the module is used for processing the new evidence body by adopting a power spectrum estimation method to obtain a recommendation decision.
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