CN114648391A - Online shopping information recommendation method - Google Patents
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Abstract
The invention provides an online shopping information recommendation method, which belongs to the technical field of online shopping commodity recommendation. Extracting corresponding influence factors from the data of purchased commodities, then extracting points of interest purchased by the user from the influence factors and the related attributes, carrying out measurement of variation trend and commodity data screening according to the processing related attributes and the recommended commodities to obtain performance attribute data of the recommended commodities, and then determining the recommended commodities by combining with the actual consumption capacity and habits of the user.
Description
Technical Field
The invention relates to the technical field of online shopping commodity recommendation, in particular to an online shopping information recommendation method.
Background
With the continuous expansion of the electronic commerce scale, the number and the types of the commodities are rapidly increased, and a customer needs to spend a great deal of time to find the commodity which the customer wants to buy. This process of browsing through large amounts of unrelated information and products will undoubtedly result in a constant loss of consumers who are overwhelmed by the problem of information overload.
The existing recommendation systems mainly recommend users by using historical purchase records of the users and social network relations of the users, and the methods can only passively predict and recommend commodities purchased by the users next time, and can rarely guide or attract the users to select to purchase some commodities. In the existing recommendation method, the user preference is analyzed by utilizing the similarity between purchased commodities, but the relationship among the purchased commodities is ignored, and the commodities are not independent but have a dependency relationship. Such as: when choosing to buy the jacket, we analyze whether the jacket is more harmonious with the purchased lower clothes, shoes and accessories, and then choose whether to buy the jacket. The commodity with high score is not combined with any other commodity to be high score, and the commodity with medium score is not combined with any other commodity to be medium score, so that the commodity with medium score is possibly combined to be high score. The combination of these commodities is not simply a linear addition, but a non-linear relationship, and some commodity combinations are even exponential increasing relationships. If it is considered that the purchased goods cannot be matched with any goods or matched with any goods in a moderate way, the user cannot use the purchased goods to the maximum extent, and the purchased goods can only become transparent.
In addition, while some recommendations are about collocation, artificial collocation criteria and picture convolution are used, and the resulting collocation combinations are subjectively and harmonically collocated commodity combinations and are not targeted to a particular target user.
Disclosure of Invention
The invention aims to provide an online shopping information recommendation method, which solves the technical problems mentioned in the background technology.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for recommending online shopping information comprises the following steps:
step 1: collecting purchased commodity data of a user and analyzing factor data influencing purchase;
step 2: designing and recommending a target commodity according to user information, and analyzing influence factors of the target commodity;
and step 3: calculating the relevant attributes and influence weights of commodity purchase according to the collected purchased commodity data of the user;
and 4, step 4: trend comparison is carried out on the relative attributes of the purchased commodities to obtain the quantity of all commodity performance data, and then the quantity of all commodity performance data is compared with the performance data of the recommended target commodity sold on the network to obtain the quantity of clustered commodity performance data;
and 5: summing the data quantity of the performance of the clustered commodities, and then combining the influence factors of the priceX k Namely accurately recommended goods.
Further, the specific process of step 1 is: collecting data set of commodity kinds purchased by usercaseIs shown as,mFor the number of commodity types, each commodity type comprises performance data, commodity attribution and price data of the commodity, and the data are collectedcaseFactoring into priceX k And performance dataY j Whereink =1,2,...,t,tIndicating the number of price influencing factors, whereinj =1,2,...,n,nNumber representing performance parameter, user-associated data sampleS i The corresponding recommended ordinal number data are respectivelyx ik Andy ij 。
further, the specific process of step 2 is: according to user basic informationInformation, time information and user daily behavior information are used for designing a target recommended commodity, then performance data of the target recommended commodity is obtained and becomes target performance data, and the corresponding value isy joFrom the influence of priceX k The related attribute directly influencing the target performance data is selected and set as。
Further, the specific process of step 3 is: selecting commodity category data set purchased by usercasePerformance data and associated attributes inThe corresponding data is expressed as case*Based on entropy weight method and data set case*Computing a commodity category datasetcaseSelected correlation attributesWeight of, distinguishThe influence degree on the performance data and the weight of the obtained related attribute are recorded as。
Further, the specific process of step 4 is: performing correlation attributesThe method comprises the steps of measuring relative variation trends and screening commodity data, wherein the commodity performance data are regarded as an N-dimensional variable set, and differences of different commodity performance data can be regarded asNResults of dimensional variable movement, change and development, based on the correlation attributesCalculating and measuring the original purchased commodity performance data of the user and the target commodity performance number to be recommendedAccording to the relative change trend of the relevant attributes, screening out the commodity performance data with the degree of the relative change trend of the relevant attributes of the commodity performance data to be recommended, and setting the commodity performance data to be the most similar to the relative change trend of the relevant attributes of the commodity performance data to be recommendedS * ={S 1 * ,S 2 * ,...,S p * },pThe amount of commodity performance data obtained for clustering.
Further, the specific process of step 5 is: for clustering resultsS * ={S 1 * ,S 2 * ,...,S p * Weighting and clustering, then weighting and summing the clustering results to obtain a summation result which is the commodity performance data required to be recommended, and then combining the influence factors of the priceX k And obtaining the recommended specific commodity.
Further, in step 1, the influence factor of the priceX k And performance dataY j Using the feature variable training model as the data input of the training model, calculating the importance ranking standard of each feature variable, eliminating the least important K features, retaining the rest features, judging the number of the rest features to meet the purchasing requirement, and if the number of the rest features meets the purchasing requirement, determining the influence factor of the priceX k To select performance dataY j Is related to the attributeAnd if not, repeating the calculation.
Further, the weight distribution process of the correlation attribute based on the entropy weight method is as follows:
Step 3.2: by polarization methodNormalizing to obtain a new matrixThen to carry outThe normalization process, the range differentiation method, and the normalization method are as follows:
wherein,is composed ofToiGo to the firstsA column element;is composed ofTo (1)sColumns; in the same way, the method for preparing the composite material,is composed ofTo (1)iGo to the firstsThe elements of the column are, in turn,is composed ofNormalized values;
step 3.4: performance dataY j Is provided withsThe related attributes are set as influencing factorsX c For performance dataY j The influence weight of (a) is:
Due to the adoption of the technical scheme, the invention has the following beneficial effects:
the method extracts corresponding influence factors from the data of purchased commodities, extracts points of interest purchased by the user from the influence factors and related attributes, selects recommended commodity categories according to actual conditions, performs trend measurement and commodity data screening according to the processing of the related attributes and the recommended commodities to obtain performance attribute data of the recommended commodities, and then determines the recommended commodities by combining the actual consumption capacity and habits of the user.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings by way of examples of preferred embodiments. It should be noted, however, that the numerous details set forth in the description are merely for the purpose of providing the reader with a thorough understanding of one or more aspects of the present invention, which may be practiced without these specific details.
As shown in fig. 1, a method for recommending online shopping information includes the following steps:
step 1: data of purchased goods of a user is collected and factor data affecting the purchase is analyzed. Collecting data set of commodity kinds purchased by usercaseIs shown as,mFor the number of commodity types, each commodity type comprises performance data, commodity attribution and price data of the commodity, and the data setscaseFactoring into priceX k And performance dataY j Whereink =1,2,...,t,tIndicating the number of price influencing factors, whereinj =1,2,...,n,nNumber representing performance parameter, user-associated data sampleS i The corresponding recommended ordinal number data are respectivelyx ik Andy ij . The collected data is the data purchased by the user on the internet, and if the data of the purchase sold off line can be counted, the data can be counted together. The performance data is the purpose bought by the user, and then the actual quoted purchase price is the consumption level and income of the user at ordinary times.
Step 2: and designing and recommending the target commodity according to the user information, and analyzing influence factors of the target commodity. Designing a target recommended commodity according to the basic information, the time information and the daily behavior information of the user, then obtaining the performance data of the target recommended commodity to become target performance data, wherein the corresponding value isy joFrom the influence of priceX k The related attribute directly influencing the target performance data is selected and set as. Influence factor of priceX k And performance dataY j Using the feature variable training model as the data input of the training model, calculating the importance ranking standard of each feature variable, eliminating the least important K features, retaining the rest features, judging the number of the rest features to meet the purchasing requirement, and if the number of the rest features meets the purchasing requirement, determining the influence factor of the priceX k To select performance dataY j Is related to the attributeAnd if not, repeating the calculation.
And step 3: and calculating the related attributes and the influence weight of the commodity purchase according to the collected purchased commodity data of the user. Selecting commodity category data set purchased by usercasePerformance data and associated attributes inThe corresponding data is expressed as case*Based on entropy weight method and data set case*Calculating a commodity category datasetcaseSelected correlation attributesWeight of (2), differentiationThe influence degree on the performance data and the weight of the obtained related attribute are recorded as。
Step 3.2: by polarization methodNormalizing to obtain a new matrixThen to carry outThe normalization process, the range differentiation method, and the normalization method are as follows:
wherein,is composed ofTo (1)iGo to the firstsA column element;is composed ofTo (1)sColumns; in the same way, the method for preparing the composite material,is composed ofTo (1)iGo to the firstsThe elements of the column are, in turn,is composed ofNormalized values;
step 3.4: performance dataY j Is provided withsSetting the influence factor according to the correlation attributeX c For performance dataY j The influence weight of (a) is:
And 4, step 4: and performing trend comparison on the relative attributes of the purchased commodities to obtain the quantity of all commodity performance data, and then comparing the quantity of all commodity performance data with the performance data of the recommended target commodity sold on the network to obtain the quantity of the clustered commodity performance data. Performing correlation attributesAnd (4) measuring relative change trend and screening commodity data. The specific process of measuring the change trend and screening the commodity data comprises the following steps: noting relevant attributes of target performance data for recommended goodsData ofTarget data, selecting a group of data relatively close to the target data from the cases as a comparison reference according to the difference rate, wherein the selection principle is to make the referenceThe smaller the difference from the value of the alignment property in O, the better. For each oneAdding a row and associated variablesDimension identical labelFor recording data corresponding to the relevant attributesRelative to the change trend of the benchmark, and then dividing the related attribute data of each commodity data in caseAnd a referenceEach attribute data is correspondingly compared one by one and marked ifThen, thenIs + or is-and the same comparison is carried out, and the related attributes of the target data are measuredRelative referenceThe relative trend of (2) is recorded as. Will be provided withAnd withThe corresponding elements in (1) are compared one by one and judgedAnd withRelative of middle correlation attributeWhether or not the change trends are the same, ifAndif both are + or-then willRewriting to 1, otherwise willRewritten to 0. According to each group of data, the label of the comparison result of the relative change trend of the related attributes of all the performance dataT i Are summed to obtainST i It is calculated as follows:
ST i has a value of [ 2 ]0,s]Is an integer of at mosts,iIs a positive integer.
According to calculationST i By integer numerical sizeST i Grade, the larger the value, the higher the grade, the same valueST i Dividing into the same grade, descending according to grade, and screening before taking outK 1Of a gradeST i Value subscript thereofiCorresponding tocaseThe performance data of the at least one sensor,K 1is a positive integer which is a multiple of,is recorded asS * ={S 1 * ,S 2 * ,...,S p * },pIs the number of performance data samples obtained.
The commodity performance data is regarded as an N-dimensional variable set, and the difference of different commodity performance data can be regarded asNResults of dimensional variable movements, changes and developments, according to the associated attributesCalculating and measuring the relative change trend of the related attributes of the original purchased commodity performance data of the user and the target commodity performance data to be recommended, screening out the commodity performance data which is closest to the degree of the relative change trend of the related attributes of the commodity performance data to be recommended, and setting the commodity performance data as the commodity performance data to be recommendedS * ={S 1 * ,S 2 * ,...,S p * },pThe amount of commodity performance data obtained for clustering.
And 5: summing the quantity of the performance data of the clustered commodities, and then combining the influence factors of the priceX k Namely accurately recommended goods. For clustering resultsS * ={S 1 * ,S 2 * ,...,S p * Weighting and clustering, then weighting and summing the clustering results to obtain a summation result which is the commodity performance data required to be recommended, and then combining the influence factors of the priceX k And obtaining the recommended specific commodity.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.
Claims (8)
1. A method for recommending online shopping information is characterized by comprising the following steps:
step 1: collecting purchased commodity data of a user and analyzing factor data influencing purchase;
step 2: designing and recommending a target commodity according to user information, and analyzing influence factors of the target commodity;
and step 3: calculating the relevant attributes and influence weights of commodity purchase according to the collected purchased commodity data of the user;
and 4, step 4: trend comparison is carried out on the relative attributes of the purchased commodities to obtain the quantity of all commodity performance data, and then the quantity of all commodity performance data is compared with the performance data of the recommended target commodity sold on the network to obtain the quantity of clustered commodity performance data;
and 5: summing the data quantity of the performance of the clustered commodities, and then combining the influence factors of the priceX k Namely accurately recommended goods.
2. The online shopping information recommending method according to claim 1, characterized in that: the specific process of the step 1 is as follows: collecting data set of commodity kinds purchased by usercaseIs shown as,mThe number of commodity types per unitEach commodity type includes performance data, commodity attribution and price data of the commodity, and the data setcaseFactoring into priceX k And performance dataY j Whereink =1,2,...,t,tIndicating the number of price influencing factors, whereinj =1,2,..., n,nNumber representing performance parameter, user-associated data sampleS i The corresponding recommended ordinal number data are respectivelyx ik Andy ij 。
3. the online shopping information recommendation method according to claim 1, characterized in that: the specific process of the step 2 is as follows: designing a target recommended commodity according to the basic information, the time information and the daily behavior information of the user, then obtaining the performance data of the target recommended commodity to become target performance data, wherein the corresponding value isy joFrom the influence of priceX k Selecting the related attribute directly influencing the target performance data, and setting the related attribute as。
4. The online shopping information recommendation method according to claim 1, characterized in that: the specific process of the step 3 is as follows: selecting commodity category data set purchased by usercasePerformance data and associated attributes inThe corresponding data is expressed as case*Based on entropy weight method and data set case*Computing a commodity category datasetcaseSelected correlation attributesWeight of, distinguishThe influence degree on the performance data and the weight of the obtained related attribute are recorded as。
5. The online shopping information recommendation method according to claim 1, characterized in that: the specific process of the step 4 is as follows: performing correlation attributesThe commodity performance data is regarded as an N-dimensional variable set, and the difference of different commodity performance data can be regarded asNResults of dimensional variable movement, change and development, based on the correlation attributesCalculating and measuring the relative change trend of the related attributes of the original purchased commodity performance data of the user and the target commodity performance data to be recommended, screening out the commodity performance data which is closest to the degree of the relative change trend of the related attributes of the commodity performance data to be recommended, and setting the commodity performance data as the commodity performance data to be recommendedS * ={S 1 * ,S 2 * ,...,S p * },pThe amount of commodity performance data obtained for clustering.
6. The online shopping information recommendation method according to claim 1, characterized in that: the specific process of the step 5 is as follows: for clustering resultsS * ={S 1 * ,S 2 * ,...,S p * Weighting and clustering, then weighting and summing the clustering results to obtain a summation result which is the commodity performance data required to be recommended, and then combining the influence factors of the priceX k And obtaining the recommended specific commodity.
7. The online shopping information recommendation method according to claim 3, characterized in that: in step 1, the influence factor of the priceX k And performance dataY j Using the feature variable training model as the data input of the training model, calculating the importance ranking standard of each feature variable, eliminating the least important K features, retaining the rest features, judging the number of the rest features to meet the purchasing requirement, and if the number of the rest features meets the purchasing requirement, determining the influence factor of the priceX k To select performance dataY j Is related to the attributeAnd if not, repeating the calculation.
8. The online shopping information recommendation method according to claim 4, characterized in that: the weight distribution process of the correlation attribute based on the entropy weight method comprises the following steps:
Step 3.2: by polarization methodNormalizing to obtain a new matrixThen to carry outThe normalization treatment is carried out on the mixture,the range differentiation and normalization methods are as follows:
wherein,is composed ofToiGo to the firstsA column element;is composed ofTo (1)sA column; in the same way, the method for preparing the composite material,is composed ofTo (1)iGo to the firstsThe number of column elements is such that,is composed ofNormalized values;
step 3.3: thenTo middlesInformation entropy definition of correlation attributesComprises the following steps:
step 3.4: performance dataY j Is provided withsThe related attributes are set as influencing factorsX c For performance dataY j The influence weight of (a) is:
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Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103412948A (en) * | 2013-08-27 | 2013-11-27 | 北京交通大学 | Cluster-based collaborative filtering commodity recommendation method and system |
CN106897911A (en) * | 2017-01-10 | 2017-06-27 | 南京邮电大学 | A kind of self adaptation personalized recommendation method based on user and article |
US20180053142A1 (en) * | 2016-08-19 | 2018-02-22 | Stitch Fix, Inc. | Systems and methods for improving recommendation systems |
CN109064285A (en) * | 2018-08-02 | 2018-12-21 | 西北大学 | A kind of acquisition commercial product recommending sequence and Method of Commodity Recommendation |
CN110827129A (en) * | 2019-11-27 | 2020-02-21 | 中国联合网络通信集团有限公司 | Commodity recommendation method and device |
CN111242732A (en) * | 2020-01-09 | 2020-06-05 | 北京慧博科技有限公司 | Commodity recommendation model-based recommendation method |
CN111815415A (en) * | 2020-07-14 | 2020-10-23 | 北京邮电大学 | Commodity recommendation method, system and equipment |
CN111861679A (en) * | 2020-08-04 | 2020-10-30 | 深圳市创智园知识产权运营有限公司 | Commodity recommendation method based on artificial intelligence |
CN112085549A (en) * | 2019-06-14 | 2020-12-15 | 上海逸宅网络科技有限公司 | Commodity recommendation method for E-commerce platform based on data processing technology |
CN112131480A (en) * | 2020-09-30 | 2020-12-25 | 中国海洋大学 | Personalized commodity recommendation method and system based on multilayer heterogeneous attribute network representation learning |
CN112667899A (en) * | 2020-12-30 | 2021-04-16 | 杭州智聪网络科技有限公司 | Cold start recommendation method and device based on user interest migration and storage equipment |
-
2022
- 2022-05-18 CN CN202210538239.9A patent/CN114648391B/en active Active
Patent Citations (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103412948A (en) * | 2013-08-27 | 2013-11-27 | 北京交通大学 | Cluster-based collaborative filtering commodity recommendation method and system |
US20180053142A1 (en) * | 2016-08-19 | 2018-02-22 | Stitch Fix, Inc. | Systems and methods for improving recommendation systems |
CN106897911A (en) * | 2017-01-10 | 2017-06-27 | 南京邮电大学 | A kind of self adaptation personalized recommendation method based on user and article |
CN109064285A (en) * | 2018-08-02 | 2018-12-21 | 西北大学 | A kind of acquisition commercial product recommending sequence and Method of Commodity Recommendation |
CN112085549A (en) * | 2019-06-14 | 2020-12-15 | 上海逸宅网络科技有限公司 | Commodity recommendation method for E-commerce platform based on data processing technology |
CN110827129A (en) * | 2019-11-27 | 2020-02-21 | 中国联合网络通信集团有限公司 | Commodity recommendation method and device |
CN111242732A (en) * | 2020-01-09 | 2020-06-05 | 北京慧博科技有限公司 | Commodity recommendation model-based recommendation method |
CN111815415A (en) * | 2020-07-14 | 2020-10-23 | 北京邮电大学 | Commodity recommendation method, system and equipment |
CN111861679A (en) * | 2020-08-04 | 2020-10-30 | 深圳市创智园知识产权运营有限公司 | Commodity recommendation method based on artificial intelligence |
CN112131480A (en) * | 2020-09-30 | 2020-12-25 | 中国海洋大学 | Personalized commodity recommendation method and system based on multilayer heterogeneous attribute network representation learning |
CN112667899A (en) * | 2020-12-30 | 2021-04-16 | 杭州智聪网络科技有限公司 | Cold start recommendation method and device based on user interest migration and storage equipment |
Non-Patent Citations (4)
Title |
---|
R LIAN: "The construction of personalized web page recommendation system in e-commerce", 《RESTRICTIONS APPLY》 * |
仲秋雁等: "考虑用户兴趣和能力的众包任务推荐方法", 《系统工程理论与实践》 * |
王冬梅: "面向延时敏感投资者的KIA产品众筹周期与价格决策", 《系统工程学报》 * |
石力: "社区电商用户复购行为预测及推荐算法研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
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