CN115700705A - Product analysis method and device, electronic equipment and readable storage medium - Google Patents
Product analysis method and device, electronic equipment and readable storage medium Download PDFInfo
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Abstract
The invention provides a product analysis method, a product analysis device, an electronic device and a readable storage medium. The product analysis method comprises the following steps: acquiring consultation record information of a target user in a preset time period; determining at least one second product related to the first product according to the consultation record information; determining a first consultation probability of a first product and a second consultation probability of a second product according to the consultation record information and N pieces of preset consultation information, wherein N is an integer greater than 0; and determining the difference information and the homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability. According to the technical scheme provided by the invention, the consultation session data of the same user in the same time period for the same type of products are subjected to statistical analysis, the intention of the same user in consulting the specific products and the potential competitive products is respectively obtained, the difference points and the homogeneous points of the specific products and the potential competitive products are determined, and the mining of the competitive product relationship among the products is realized.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a product analysis method, a product analysis device, an electronic device, and a readable storage medium.
Background
In the related technology, mining and comparing of competitive products are realized based on analysis of behavior information of all users and comment content of products by big data, and further attitude of the users to the products is determined. However, the comparison analysis of the product and the competitive product according to the data of all users is performed in a relatively simple way, so that the comparison accuracy is low.
Disclosure of Invention
The present invention has been made to solve at least one of the problems occurring in the prior art or the related art.
To this end, a first aspect of the invention provides a method of analyzing a product.
The second aspect of the invention also provides an analysis device for a product.
The third aspect of the present invention also provides an electronic device.
The fourth aspect of the present invention also provides an electronic apparatus.
The fifth aspect of the present invention also provides a readable storage medium.
In view of the above, a first aspect of the present invention provides a method for analyzing a product, including: acquiring consultation record information of a target user in a preset time period; determining at least one second product related to the first product according to the consultation record information; determining a first consultation probability of a first product and a second consultation probability of a second product according to the consultation record information and N pieces of preset consultation information, wherein N is an integer greater than 0; and determining the difference information and the homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability.
The product analysis method provided by the invention aims at a specific product (namely a first product), obtains a target user and time for initiating consultation on an e-commerce platform, obtains consultation record information of the target user in a preset time period (for example, 24 hours before and after consultation time) based on the time for initiating consultation, and determines at least one potential competitive product (namely a second product) which is the same product as the specific product in the consultation record information.
Further, according to the N pieces of consultation intentions (namely preset consultation information), carrying out statistical analysis on the consultation record information, obtaining the consultation intentions of the target user for consulting the characteristic products, which appear in the consultation record information, analyzing the preference degree (namely first consultation probability) of the target user for consulting the specific products, and the consultation intentions of the target user for consulting the potential competitive products, which appear in the consultation record information, and analyzing the preference degree (namely second consultation probability) of the target user for consulting the potential competitive products by using the consultation intentions.
Further, the target user's preference for a particular product and for potential bids is analyzed. From the N consulting intents, a difference point (i.e., difference information) at which the feature product cannot be replaced by the potential bid for the target user and a homogeneity point (i.e., homogeneity information) at which the feature product can be replaced by the potential bid are determined.
According to the method and the device, the consultation session data of the same user in the same time period for the same type of products are statistically analyzed, the consultation intentions of the user for consulting specific products and potential competitive products are respectively obtained, the preference degrees of the user for the consultation intentions of the user for the specific products and the potential competitive products are determined by utilizing the consultation intentions, further, the emotional trends of the same user for the specific products and the potential competitive products are analyzed, the competition relationship among the products is calculated, the difference point and the homogeneity point of the specific products and the potential competitive products are determined, and the mining of the competitive product relationship among the products is realized. Compared with the technical scheme that a large amount of data are obtained based on access to different users and search and evaluation content, and the competitive products are identified based on the data in the prior art, the method and the device for identifying the competitive products construct a product comparison combination with consistent decision behaviors and the same user, achieve consistency in competitive product comparison, and effectively improve accuracy of comparison between specific products and potential competitive products.
It should be noted that the N pieces of preset consultation information are effective consultation intentions of the product summarized in advance according to the model, are used for determining an intention of the user to make a certain reflection on the product in advance, and are a preparation state before taking action in the consumption behavior. The consultation purpose can be pre-sale problems such as product introduction, preferential policies and the like, and can also be post-sale problems such as logistics inquiry, how to return goods and the like.
According to the analysis method of the product provided by the invention, the following additional technical characteristics can be provided:
in the above technical solution, further, the step of determining at least one second product corresponding to the first product according to the consultation record information specifically includes: acquiring the category information of a first product; at least one second product in the counseling record information that is related to the first product is determined based on the category information.
In the technical scheme, the product combination closest to the category information of the specific product in the same category of products is obtained based on the category information of the specific product, and then at least one product in the product combinations appears from the consultation record information and serves as a potential competitive product. By carrying out statistical analysis on the consultation record information of the same type of products in a preset time period by the same user, potential competitive products related to the specific products are defined, the accuracy of selecting the potential competitive products is improved, the influence of useless products on the analysis of the specific products is reduced, and the accuracy of product analysis is further improved.
Specifically, the product category information includes brand, category, price, weight, color, size, material, origin, packaging, logistics, and the like, which is not limited in this application.
In any of the above technical solutions, further, the step of determining the first consultation probability of the first product according to the consultation record information and the N preset consultation information specifically includes: acquiring first counseling record information related to a first product in the counseling record information; determining a first frequency of occurrence of each preset consultation information from the first consultation record information; calculating a first consultation probability according to the first frequency and a first formula;
the first formula is:
wherein e is the first product, I e For all preset advisory information present in the first advisory record information, said I ek For the kth preset counseling information appearing in the first counseling record information, above A ek Is as followsThe probability of occurrence of the kth predetermined consultation information in a consultation record information, F (I) ek ) Frequency of occurrence of the kth predetermined counseling information, the above
K is the sum of the frequency of occurrence of all the preset consultation information, and belongs to {1,2,3, \8230;, N }.
In the technical scheme, the consultation record information is subjected to statistical analysis, the consultation record (namely the first consultation record information) of the target user for consulting the characteristic product is obtained, all consultation intentions of the target user for the specific product are determined by utilizing the consultation record information, and the frequency of each consultation intention and the sum of the frequencies of all consultation intentions are calculated.
Further, the probability of occurrence of each consultation intention is calculated according to a formula, namely the preference degree of each consultation intention corresponding to the specific product corresponding to the target user. The method comprises the steps of determining the attention degree of a target user to a characteristic product by calculating the preference degree of the target user for the specific product, so as to realize the analysis of the emotion trend of the target user to the specific product and promote the optimization and the upgrade of the product.
In any of the above technical solutions, further, the step of determining a second advisory probability of the second product according to the advisory record information and the N preset advisory information specifically includes: acquiring second consultation record information related to a second product in the consultation record information; determining a second frequency of occurrence of each preset consultation information from the second consultation record information; calculating a second consultation probability according to the second frequency and a second formula;
the second formula is:
wherein j is a second product, i is j For all preset advisory information present in the second advisory record information, i above jk For the k-th preset counseling information appearing in the second counseling record informationA above-mentioned jk Is the probability of occurrence of the k-th predetermined counseling information in the second counseling record information, F (i) above jk ) Frequency of occurrence of the kth predetermined counseling information, the above
K is the sum of the frequency of occurrence of all the preset consultation information, and belongs to {1,2,3, \8230;, N }.
In the technical scheme, statistical analysis is carried out on the consultation record information, a consultation record (namely second consultation record information) of a target user for consulting potential competitive products is obtained, all consultation intentions of the target user for the potential competitive products are determined by utilizing the consultation record, and the frequency of each consultation intention and the sum of the frequency of all the consultation intentions are calculated.
Further, calculating the probability of the occurrence of each consultation intention according to a formula is the preference degree of the target user for the consultation intention of the potential competitive products. And determining the attention degree of the target user to the potential competitive products by calculating the preference degree of the consultation intention of the target user to the potential competitive products so as to realize the analysis of the emotional trend of the target user to the potential competitive products.
In any of the above technical solutions, further, the step of determining difference information and homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability specifically includes: determining a difference value between the first product and the second product according to the first consultation probability and the second consultation probability; and determining the difference information and the homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the difference value, the first consultation probability and the second consultation probability.
According to the technical scheme, the difference value between the specific product and the potential competitive products is determined according to the preference degree of the consultation intention of the target user for the specific product and the preference degree of the consultation intention for the potential competitive products. And analyzing a difference point at which the specific product cannot be replaced by the potential competitive product for the target user and a homogeneous point at which the characteristic product can be replaced by the potential competitive product by utilizing the difference value, the preference degree of the target user for the specific product and the preference degree of the target user for the potential competitive product. Compared with the analysis method for carrying out the competitive products based on the action chains of all users on the surface in the prior art, the method and the device have the advantages that the competitive products are more accurately analyzed through mental intelligence and motivation of the same user, the consistency of competitive product comparison is realized, and the accuracy of obtaining difference points and homogeneous points among products is improved.
In any of the above technical solutions, further, determining a difference value between the first product and the second product according to the first consultation probability and the second consultation probability specifically includes: based on the second consultation probability being equal to 0, the difference value is 0; calculating a difference value according to a third formula based on the second consultation probability not equal to 0;
the third formula is:
wherein L is ejk Is the difference value between the first product and the second product, A ek As the first consultation probability, a jk Is the second consultation probability.
In the technical scheme, the consulting records corresponding to the potential competitive products are subjected to statistical analysis, and if the frequency of occurrence of a certain consulting intention is 0, which indicates that the preference degree of the target user for the potential competitive products to the consulting intention is 0, the difference value between the specific product and the potential competitive products corresponding to the consulting intention is 0.
Further, if the frequency of occurrence of a certain consulting intention is not 0, the preference degree of the target user for the potential contestant for the consulting intention is not 0. And calculating the ratio of the preference degree of the target user for the specific product to the consultation intention to the preference degree of the potential competitive products to the consultation intention, and determining the difference value of all the consultation intentions for the difference value of the specific product and the potential competitive products. By calculating the difference value of the same consultation intention among the products, the accuracy of contrastive analysis of the specific products and the potential competitive products is effectively improved.
In any of the above technical solutions, further, the step of determining difference information and homogeneity information of the first product and the second product from N pieces of preset consulting information according to the difference value, the first consulting probability and the second consulting probability specifically includes: determining first consultation information meeting a first preset condition from the N preset consultation information; the first consultation information is used as difference information of a first product and a second product, wherein the first preset condition is that the absolute value of the difference between the difference value and the first threshold is greater than a second threshold, the first consultation probability is greater than a third threshold, and the second consultation probability is greater than the third threshold; determining second consultation information meeting a second preset condition from the N pieces of preset consultation information; taking the second consultation information as the homogeneity information of the first product and the second product, wherein the second preset condition is that the absolute value of the difference between the difference value and the first threshold is smaller than a fourth threshold, the first consultation probability is larger than a third threshold, and the second consultation probability is larger than the third threshold; wherein, the fourth threshold value is more than 0 and less than the second threshold value and less than 1.
In the technical scheme, the difference value between the difference value of any consultation intention and a first threshold is calculated, if the absolute value of the difference value is smaller than the difference threshold (namely, a second threshold), the frequency of the consultation intention appearing in the consultation record of the characteristic product is larger than a frequency threshold (namely, a third threshold), and the frequency of the consultation intention appearing in the consultation record of the potential product is larger than the frequency threshold, which indicates that the consultation intention is a difference point that a specific product cannot be replaced by the potential product for a target user.
Further, the difference between the difference value of any consulting intention and the first threshold is calculated, if the absolute value of the difference value is larger than the homogeneity threshold (namely, a fourth threshold), the consulting intention appears in the consulting record of the characteristic product more frequently than the frequency threshold, and the consulting record of the potential product more frequently than the frequency threshold, which indicates that the consulting intention is a homogeneous point that the specific product can be replaced by the potential product for the target user. The method and the device for determining the difference points and the homogeneous points among the products based on the same comparison standard realize the consistency of the comparison of the competitive products and improve the accuracy of selecting the difference points and the homogeneous points.
It should be noted that, the first threshold is 1, and the absolute value obtained by subtracting 1 from the difference value is used as the judgment value of the difference point and the homogeneous point, and if the judgment value corresponding to any consultation intention is closer to 0, it indicates that the preference degrees of the user for the specific product and the potential competitive products for the consultation intention are more similar. And when the judgment value is smaller than the homogeneity threshold value and the occurrence frequency of the consultation intention is larger than the frequency threshold value for the characteristic product and the potential competitive product, determining that the consultation intention corresponding to the judgment value is the homogeneity point of the specific product and the potential competitive product. If the judgment value corresponding to any consultation intention is farther away from 0, the difference of the preference degrees of the users for the characteristic product and the potential product to the consultation intention is larger. And when the judgment value is greater than the difference threshold value, and the occurrence frequency of the consultation intention is greater than the frequency threshold value for the characteristic product and the potential competitive product, determining that the consultation intention is a difference point between the specific product and the potential competitive product. The sizes of the difference threshold and the homogeneity threshold are limited to ensure the consistency of the judgment logic, and the accuracy of product analysis is effectively improved. Wherein the homogeneity threshold value is greater than 0 and less than the difference threshold value and less than 1, the consistency of the judgment logic is ensured by limiting the difference threshold value and the homogeneity threshold value, and the accuracy of product analysis is improved.
The numerical values of the second threshold, the third threshold, and the fourth threshold are set according to actual conditions such as the number of required quality information and difference information, and the present application is not limited thereto.
In any of the above technical solutions, further, when the number of the second products is multiple, the method further includes: determining the similarity between the first product and each second product according to the first consultation probability and the second consultation probability; and determining a second product with the similarity larger than the similarity threshold from the plurality of second products.
In the technical scheme, when the number of the potential competitive products is multiple, the similarity of the consultation intention of the specific product and each potential competitive product is determined according to the preference degree of the target user on the consultation intention of the specific product and the preference degree of the consultation intention of the potential competitive products, and a plurality of similarity values are ranked, wherein the higher the similarity is, the smaller the difference of the consultation of the target user on the two products is, namely the more similar the potential competitive products and the characteristic products are on the basis of the preference of the target user; the lower the negative-positive similarity, the greater the difference between the target user's consultation on the two products, i.e., the more dissimilar the potential bid is to the particular product based on the target user's preferences. By comparing whether the consultation of the target user on the specific product is similar to the consultation of the potential competitive product, the attention difference of the target user on the two products is determined, and the accuracy of the analysis of the competitive power degree between the products is improved.
According to a second aspect of the present invention, there is provided an analysis device for a product, comprising: the acquisition module is used for acquiring the consultation record information of the target user in a preset time period; the first determining module is used for determining at least one second product related to the first product according to the consultation record information; the second determining module is used for determining a first consultation probability of the first product and a second consultation probability of the second product according to the consultation record information and N pieces of preset consultation information, wherein N is an integer larger than 0; and the third determining module is used for determining the difference information and the homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability.
In the technical scheme, the product analysis device comprises an acquisition module, a first determination module, a second determination module and a third determination module.
Specifically, the acquisition module is configured to acquire a target user and time for which consultation is initiated on the e-commerce platform for a specific product (i.e., a first product), and acquire consultation record information of the target user within a preset time period (e.g., 24 hours before and after the consultation time) based on the time for which consultation is initiated.
Further, the first determining module is configured to determine at least one potential bid (i.e., second product) for a product of the same category as the particular product in the advisory record information.
Further, the second determining module is configured to perform statistical analysis on the consultation record information according to the N consultation intentions (i.e., preset consultation information), obtain the consultation intentions of the target user in the consultation record information for consulting the specific product, analyze the preference degree (i.e., the first consultation probability) of the target user for the consultation intentions of the specific product, and analyze the preference degree (i.e., the second consultation probability) of the target user in the consultation record information for consulting the potential competitive products according to the consultation intentions of the target user in the consultation record information.
Further, the third determination module is configured to analyze the target user's preference for a particular product and for a potential contest. From the N consulting intents, a difference point (i.e., difference information) at which the feature product cannot be replaced by the potential contest for the target user and a homogeneity point (i.e., homogeneity information) at which the feature product can be replaced by the potential contest are determined.
According to the method and the device, the consultation session data of the same user in the same time period for the same type of products are statistically analyzed, the consultation intentions of the user for consulting specific products and potential competitive products are respectively obtained, the preference degrees of the user for the consultation intentions of the user for the specific products and the potential competitive products are determined by utilizing the consultation intentions, further, the emotional trends of the same user for the specific products and the potential competitive products are analyzed, the competition relationship among the products is calculated, the difference point and the homogeneity point of the specific products and the potential competitive products are determined, and the mining of the competitive product relationship among the products is realized. Compared with the technical scheme that a large amount of data are acquired based on access to different users and search evaluation content, and competitive products are identified based on the data in the prior art, the method and the device construct a product comparison combination with consistent decision behaviors and the same user, achieve consistency in competitive product comparison, and effectively improve accuracy of comparison between specific products and potential competitive products.
According to a third aspect of the invention, an electronic device is proposed, comprising the analysis means of the product proposed by the second aspect. Therefore, the electronic device has all the advantages of the product analysis device provided by the second aspect, and details are not repeated herein.
According to a fourth aspect of the present invention, there is provided an electronic apparatus comprising: a memory storing a program or instructions; a processor connected with the memory, the processor configured to implement the analysis method of the product proposed by the first aspect when executing the program or the instructions. Therefore, the electronic device has all the advantages of the product analysis method provided by the first aspect, and details are not repeated herein.
According to a fifth aspect of the present invention, a readable storage medium is proposed, on which a program or instructions are stored, which program or instructions, when executed by a processor, carry out the analysis method of the product proposed by the first aspect. Therefore, the readable storage medium has all the beneficial effects of the product analysis method provided by the first aspect, and is not described in detail again to avoid repetition.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 shows one of the flow diagrams of the method of analysis of a product according to one embodiment of the invention;
FIG. 2 is a second schematic flow chart of a product analysis method according to an embodiment of the present invention;
FIG. 3 is a third schematic flow chart of a product analysis method according to an embodiment of the present invention;
FIG. 4 shows a fourth flowchart of a method of analyzing a product according to an embodiment of the invention;
FIG. 5 shows a fifth flowchart of a method for analyzing a product according to an embodiment of the present invention;
FIG. 6 shows a sixth flowchart of a method of analyzing a product according to an embodiment of the invention;
FIG. 7 shows a seventh schematic flow chart of a method of analyzing a product according to an embodiment of the invention;
FIG. 8 shows an eighth schematic flow chart of a method for analyzing a product according to an embodiment of the present invention;
FIG. 9 shows a ninth schematic flow chart of a method of analyzing a product according to one embodiment of the present invention;
FIG. 10 is a schematic diagram illustrating a method of analyzing a product according to an embodiment of the present invention;
FIG. 11 illustrates one of the schematic block diagrams of a method of analyzing a product according to one embodiment of the present invention;
FIG. 12 shows a second schematic block diagram of a method of analyzing a product according to a specific embodiment of the invention;
FIG. 13 shows a schematic block diagram of an analysis apparatus for a product according to an embodiment of the invention;
fig. 14 shows a schematic block diagram of an electronic device of the invention.
Wherein, the correspondence between the reference numbers and the part names in fig. 13 and 14 is:
the analysis device of the product 1300 comprises a 1302 acquisition module, 1304 a first determination module, 1306 a second determination module, 1308 a third determination module, 1400 electronic equipment, 1402 memory and 1404 processor.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced otherwise than as specifically described herein and, therefore, the scope of the present invention is not limited by the specific embodiments disclosed below.
An analysis method of a product, an analysis apparatus of a product, an electronic device, and a readable storage medium according to some embodiments of the present invention are described below with reference to fig. 1 to 14.
Example 1:
as shown in fig. 1, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
102, acquiring consultation record information of a target user in a preset time period;
104, determining at least one second product related to the first product according to the consultation record information;
and step 108, determining difference information and homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability.
In the embodiment, a target user and time for which consultation is initiated on the e-commerce platform are acquired for a specific product (namely, a first product), consultation record information of the target user within a preset time period (for example, 24 hours before and after the consultation time) is acquired based on the time for initiating consultation, and at least one potential competitive product (namely, a second product) which is a product of the same category as the specific product is determined in the consultation record information.
Further, according to the N pieces of consultation intentions (namely preset consultation information), carrying out statistical analysis on the consultation record information, obtaining the consultation intentions of the target user for consulting the characteristic products, which appear in the consultation record information, analyzing the preference degree (namely first consultation probability) of the target user for consulting the specific products, and the consultation intentions of the target user for consulting the potential competitive products, which appear in the consultation record information, and analyzing the preference degree (namely second consultation probability) of the target user for consulting the potential competitive products by using the consultation intentions.
Further, the target user's preference for a particular product and for potential bids is analyzed. From the N consulting intents, a difference point (i.e., difference information) at which the feature product cannot be replaced by the potential contest for the target user and a homogeneity point (i.e., homogeneity information) at which the feature product can be replaced by the potential contest are determined.
According to the method, the consultation session data of the same user in the same time period for the same type of products are subjected to statistical analysis, the consultation intentions of the user for consulting the specific products and the potential competitive products are respectively obtained, the preference degree of the user for the consultation intentions of the specific products and the potential competitive products is determined by utilizing the consultation intentions, the emotional trends of the same user for the specific products and the potential competitive products are further analyzed, the competitive relationship among the products is calculated, the difference points and the homogeneous points of the specific products and the potential competitive products are determined, and the mining of the competitive product relationship among the products is realized. Compared with the technical scheme that a large amount of data are acquired based on access to different users and search evaluation content, and competitive products are identified based on the data in the prior art, the method and the device construct a product comparison combination with consistent decision behaviors and the same user, achieve consistency in competitive product comparison, and effectively improve accuracy of comparison between specific products and potential competitive products.
It should be noted that the N pieces of preset consulting information are consulting intentions of the product summarized in advance according to the model, are used for determining an intention of the user to make a certain reflection on the product in advance, and are a preparation state before taking action in the consuming behavior. The consultation intention can be pre-sale problems such as product introduction, preferential policies and the like, and can also be post-sale problems such as logistics inquiry, how to return goods and the like.
Example 2:
as shown in fig. 2, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
and step 210, determining difference information and homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability.
In this embodiment, based on the category information of the specific product, the product combination closest to the category information of the specific product in the same category of products is obtained, and then at least one product in the product combinations appearing from the consultation record information is used as a potential bid. By carrying out statistical analysis on the consultation record information of the same type of products in a preset time period by the same user, potential competitive products related to the specific products are defined, the accuracy of selecting the potential competitive products is improved, the influence of useless products on the analysis of the specific products is reduced, and the accuracy of product analysis is further improved.
Specifically, the product category information includes brand, category, price, weight, color, size, material, production area, package, logistics, and the like, which is not limited in this application.
In a specific embodiment, if the characteristic product is p40, according to the finest item information in the item information such as brand, category, price, weight, size and the like of p40, determining that the p40 related products include iphone11, millet 10, OPPO Reno3 Rro and the like, acquiring a consultation session of the p40 consulted by the target user in a preset time period, performing statistical analysis on the consultation session of the related products by the target user based on the consultation session, determining that the related products consulted by the target user are iphone11 in the preset time period, and determining that iphone11 is a potential competitive product of the p40 product of the specific product.
Example 3:
as shown in fig. 3, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
step 304, determining at least one second product related to the first product according to the consultation record information;
308, determining a first frequency of occurrence of each preset consultation information from the first consultation record information;
and step 314, determining difference information and homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability.
In this embodiment, the consulting record information is statistically analyzed, the consulting record (i.e. the first consulting record information) of the target user consulting the specific product is obtained, all consulting intentions of the target user aiming at the specific product are determined by using the consulting record information, and the frequency of occurrence of each consulting intention and the sum of the frequencies of occurrence of all consulting intentions are calculated.
Further, the probability of occurrence of each consultation intention is calculated according to a formula, namely the preference degree of each consultation intention corresponding to the specific product corresponding to the target user. The method comprises the steps of determining the attention degree of a target user to a characteristic product by calculating the preference degree of the target user for the specific product, so as to realize the analysis of the emotion trend of the target user to the specific product and promote the optimization and the upgrade of the product.
Specifically, the first formula is:
wherein e is the first product, I e For all preset advisory information present in the first advisory record information, said I ek For the kth preset counseling information appearing in the first counseling record information, above A ek The probability of the occurrence of the k-th predetermined counseling information in the first counseling record information, F (I) above ek ) Frequency of occurrence of the kth predetermined counseling information, the above
K is the sum of the frequency of occurrence of all the preset consultation information, and belongs to {1,2,3, \8230;, N }.
In the embodiment, if the effective consultation intention summarized in advance according to the model is five types of product image quality, product sound effect, preferential policy, logistics inquiry and how to return goods. The method comprises the steps of carrying out statistical analysis on consultation sessions of specific products, determining that three consultation intentions, namely product image quality, preferential policies and goods returning are presented in the consultation sessions of the specific products, wherein the frequency of presenting the product image quality is 2, the frequency of presenting the preferential policies is 3, the frequency of presenting the goods returning is 1, the sum of the frequency of presenting all corresponding consultation intentions of the specific products is 6, the preference degree of the product image quality is 2/6, the preference degree of the product sound effect is 0, the preference degree of the preferential policies is 3/6, the preference degree of logistics inquiry is 0, and the preference degree of the goods returning is 1/6.
Example 4:
as shown in fig. 4, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
step 402, acquiring consultation record information of a target user in a preset time period;
step 404, determining at least one second product related to the first product according to the consultation record information;
and step 414, determining the difference information and the homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability.
In this embodiment, the statistical analysis is performed on the consultation record information, a consultation record (i.e., second consultation record information) for the target user to consult the potential bidding products is obtained, all consultation intentions of the target user for the potential bidding products are determined by using the consultation record, and the frequency of occurrence of each consultation intention and the sum of the frequencies of occurrence of all consultation intentions are calculated.
Further, calculating the probability of the occurrence of each consultation intention according to a formula is the preference degree of the target user for the consultation intention of the potential competitive products. And determining the attention degree of the target user to the potential competitive products by calculating the preference degree of the consultation intention of the target user to the potential competitive products so as to realize the analysis of the emotional trend of the target user to the potential competitive products.
Specifically, the second formula is:
wherein j is a second product, i is j For all preset advisory information present in the second advisory record information, i above jk For the k-th preset consultation information appearing in the second consultation record information, the above-mentioned a jk Is the probability of the occurrence of the k-th preset counseling information in the second counseling record information, F (i) above jk ) Frequency of occurrence of the k-th preset counseling information, above
K is the sum of the frequency of occurrence of all the preset consultation information, and belongs to {1,2,3, \8230;, N }.
In the embodiment, if the effective consultation intention summarized in advance according to the model is five types of product image quality, product sound effect, preferential policy, logistics inquiry and how to return goods. The method comprises the steps of carrying out statistical analysis on consultation records of potential competitive products, determining that the appearing consultation intentions comprise product image quality and a preferential policy, wherein the frequency of the product image quality is 1, the frequency of the preferential policy is 4, the sum of the frequency of all preset consultation information is 5, the preference degree of the product image quality is 1/5, the preference degree of the product sound effect is 0, the preference degree of the preferential policy is 4/5, the preference degree of logistics inquiry is 0, and the preference degree of returned goods is 0.
Example 5:
as shown in fig. 5, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
502, acquiring consultation record information of a target user in a preset time period;
and step 510, determining difference information and homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the difference value, the first consultation probability and the second consultation probability.
In the embodiment, the difference value between the specific product and the potential competitive products is determined according to the preference degree of the consultation intention of the target user for the specific product and the preference degree of the consultation intention for the potential competitive products. And analyzing a difference point at which the specific product cannot be replaced by the potential competitive product for the target user and a homogeneous point at which the characteristic product can be replaced by the potential competitive product by utilizing the difference value, the preference degree of the target user for the specific product and the preference degree of the target user for the potential competitive product. Compared with the analysis method for carrying out the competitive products based on the action chains of all users in the prior art, the method has the advantages that the competitive products are more accurately analyzed through the mind and motivation of the same user, the consistency of the competitive product comparison is realized, and the accuracy of obtaining the difference points and the homogeneous points among the products is improved.
Example 6:
as shown in fig. 6, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
step 602, obtaining consultation record information of a target user in a preset time period;
and 614, determining difference information and homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the difference value, the first consultation probability and the second consultation probability.
In this embodiment, statistical analysis is performed on the consultation records corresponding to the potential bids, and if the frequency of occurrence of a certain consultation intention is 0, which indicates that the preference degree of the target user for the consultation intention of the potential bids is 0, the difference value between the consultation intention corresponding to the specific product and the potential bids is 0.
Further, if the frequency of occurrence of a certain consultation purpose is not 0, the preference degree of the target user for the consultation purpose for the potential bid is not 0. And calculating the ratio of the preference degree of the target user for the specific product to the consultation intention to the preference degree of the potential competitive products to the consultation intention, and determining the difference value of all the consultation intentions for the specific product and the potential competitive products. By calculating the difference value of the same consultation intention among the products, the accuracy of contrastive analysis of the specific products and the potential competitive products is effectively improved.
Specifically, the third formula is:
wherein L is ejk Is the difference value between the first product and the second product, A ek Is the first probability of consultationA above jk Is the second consultation probability.
Example 7:
as shown in fig. 7, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
step 702, acquiring consultation record information of a target user in a preset time period;
step 704, determining at least one second product related to the first product according to the consultation record information;
step 712, the first consulting information is used as the difference information of the first product and the second product.
In this embodiment, the difference between the difference value of any consulting intention and the first threshold is calculated, if the absolute value of the difference value is less than the difference threshold (i.e. the second threshold), the consulting intention appears more frequently in the consulting record of the characteristic product than the frequency threshold (i.e. the third threshold), and appears more frequently in the consulting record of the potential product than the frequency threshold, which indicates that the consulting intention is a difference point that the specific product cannot be replaced by the potential product for the target user.
The first preset condition is that the absolute value of the difference between the difference value and the first threshold is greater than a second threshold, the first consultation probability is greater than a third threshold, and the second consultation probability is greater than the third threshold.
It should be noted that, the first threshold is 1, and the absolute value obtained by subtracting 1 from the difference value is used as the judgment value of the difference point and the homogeneous point, and if the judgment value corresponding to any consultation intention is farther from 0, it indicates that the difference between the preference degrees of the user for the characteristic product and the potential product for the consultation intention is larger. And when the judgment value is greater than the difference threshold value, and the occurrence frequency of the consultation intention is greater than the frequency threshold value for the characteristic product and the potential competitive products, determining that the consultation intention is the difference point between the specific product and the potential competitive products. The sizes of the difference threshold and the homogeneity threshold are limited to ensure the consistency of the judgment logic and effectively improve the accuracy of product analysis.
The numerical values of the second threshold and the third threshold are set according to actual conditions such as the number of pieces of required homogeneity information and difference information, and the present application is not limited thereto.
Example 8:
as shown in fig. 8, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
808, determining a difference value between the first product and the second product according to the first consultation probability and the second consultation probability;
step 812, the second consulting information is used as the homogeneity information of the first product and the second product.
In this embodiment, the difference between the difference value of any consulting intention and the first threshold is calculated, if the absolute value of the difference value is greater than the homogeneity threshold (i.e. the fourth threshold), the consulting intention appears more frequently in the consulting record of the characteristic product than the frequency threshold, and appears more frequently in the consulting record of the potential product than the frequency threshold, which indicates that the consulting intention is a homogeneous point that the specific product can be replaced by the potential product for the target user. The method and the device for determining the difference points and the homogeneous points among the products based on the same comparison standard realize the consistency of the comparison of the competitive products and improve the accuracy of selecting the difference points and the homogeneous points.
The second preset condition is that the absolute value of the difference between the difference value and the first threshold is smaller than a fourth threshold, the first consultation probability is larger than a third threshold, and the second consultation probability is larger than the third threshold; wherein, the fourth threshold value is more than 0 and less than the second threshold value and less than 1.
It should be noted that, the first threshold is 1, and the absolute value obtained by subtracting 1 from the difference value is used as the judgment value of the difference point and the homogeneous point, and if the judgment value corresponding to any consultation intention is closer to 0, it indicates that the preference degrees of the user for the consultation intentions are more similar for a specific product and a potential competitive product. And when the judgment value is smaller than the homogeneity threshold value and the occurrence frequency of the consultation intention is larger than the frequency threshold value for the characteristic product and the potential competitive product, determining that the consultation intention corresponding to the judgment value is the homogeneity point of the specific product and the potential competitive product.
Wherein the homogeneity threshold value is more than 0 and less than the difference threshold value is less than 1, the consistency of the judgment logic is ensured by limiting the difference threshold value and the homogeneity threshold value, and the accuracy of product analysis is improved.
The numerical values of the third threshold and the fourth threshold are set according to actual conditions such as the number of pieces of required homogeneity information and difference information, and the present application is not limited thereto.
Example 9:
as shown in fig. 9, according to an embodiment of the present invention, there is provided a method of analyzing a product, the method including:
in step 912, a second product with a similarity greater than the similarity threshold is determined from the plurality of second products.
In this embodiment, when the number of the potential bids is multiple, determining the similarity of the consultation intentions of the specific product and each potential bid according to the preference degree of the target user for the consultation intentions of the specific product and the preference degree of the consultation intentions of the potential bids, and ranking the similarity values, wherein the higher the similarity is, the smaller the difference of the consultation of the target user for the two products is, namely, the more similar the potential bids and the characteristic products are based on the preference of the target user; conversely, the lower the similarity, the greater the difference between the target user's consultation with the two products, i.e., the more dissimilar the potential contestant is to the particular product based on the target user's preferences. By comparing whether the consultation of the target user on the specific product is similar to the consultation of the potential competitive product, the attention difference of the target user on the two products is determined, and the accuracy of the analysis of the competitive power degree between the products is improved.
In a specific embodiment, the fact that televisions B and C are both competitive products of the televisions A and have similar competitive relationships is obtained through statistical analysis of consultation record information of the televisions A, but the target user pays attention to the television A because of image quality, pays attention to the television B because of image quality, and the television C has an acoustic effect, so that the competitive relationships of the television B and the television A are closer to those of the target user C, the similarity of the television A and the televisions B and C is calculated and analyzed respectively, and the higher the similarity is, the greater the degree of competitiveness of the two products in homogenization is.
Example 10:
as shown in fig. 10, according to an embodiment of the present invention, there is provided a method for analyzing a product, the method including:
In this embodiment, as shown in fig. 11 and 12, based on a specific product, a consultation record in a preset time period of a potential competitive product combination is acquired, and the consultation record is aimed at the specific product, for example, the product P e Acquiring a target user initiating consultation and time T, and based on the time T, in the time periods of front and back T, namely [ T-T, T + T]Advisory quadruplets of all other similar products within a time period, e.g. 24 hours before and after time T<P e ，p j ，S e ，s j >In which P is e Specific products for consultation, p j For the corresponding product of the consultation, S e For consulting a particular product P e Is recorded as information s j For consulting corresponding products p j The recording information of (2).
Further, based on product combination<P e ,p j >All the record information pairs obtain corresponding preset consultation information, namely corresponding intention, and construct corresponding hexahydric groups<P e ，p j ，S e ，s j ，I e ，i j >Wherein, I e For consulting a particular product P e Is recorded in e Corresponding intention, i j For consulting corresponding products p j Is recorded in j And then define potential bids based on the consulting intent of the same user for different products in the same category within the proximate segment.
Further, a preference vector is constructed based on the statistical distribution of the consultation intentions of the specific product and the potential contestant, and the similarity relation of the specific product and the potential contestant is calculated based on the preference vector. Specifically, as shown in FIG. 11, for<P e ，p j >All recorded information pairs of<S e ，s j >All intentions of<I e ，i j >Constructing the respectiveConsultation intention preference vector { A e }＝{A ek I k =1,2,3, \8230;, n } and { a [ (+ ]) j }＝{a jk L k =1,2,3, \8230;, n }. Specifically, A ek ＝F(I ek )/ΣF(I e ) Wherein A is ek For consulting a particular product P e The k-th consultation intention preference of, A e For consulting a particular product P e Preference for all consulting intentions of, I ek For consulting a particular product P e The kth consultation intention of F (I) e ) For consulting a particular product P e K-th consultation intention I ek Frequency of occurrence of (1), Σ F (I) e ) For consulting a particular product P e Frequency of all consultation intentions, further, a jk ＝F(i jk )/ΣF(i j ) Wherein a is jk For consulting corresponding products p j The k-th consultation intention preference of a j For consulting corresponding products p j Preference of all consulting intentions of i jk For consulting corresponding products p j The k-th counseling intention of F (i) jk ) For consulting corresponding products p j K consultation intention i jk Frequency of occurrence of (a), Σ F (i) j ) To a corresponding product p j The sum of the frequency of all consultation intentions.
Further, as shown in FIG. 11, based on { A } e And { a } and j calculates similar cosine coefficients, specifically,
wherein R is ejk Similar cosine coefficients for the kth counseling intent for a particular product and corresponding product. According to the particular product P e Obtaining its corresponding product p j Cosine coefficient vector of { R } ej }＝{R ejk L k =1,2,3, \8230;, n }, wherein R ej To be directed at P e And p j All consulting the intended similar cosine coefficients. To R ejk In a sequence of R ejk The higher the two products are, the more similar the user's preferences and vice versa.
Further, a difference value is constructed based on preference vectors of the specific product and the corresponding product, andand acquiring difference points and homogeneous points based on the difference values and the threshold values of the frequency. Specifically, as shown in FIG. 12, for P e And p j Based on its corresponding preference vector { A e And { a } and j constructing a corresponding difference value vector { L } ej }＝{L ejk I k =1,2,3, \8230;, n }, wherein L ej Is P e And p j All consulting intention difference values of (1), L ejk Is P e And p j The k-th consulting intention difference value. For L ejk When a is jk When equal to 0, L ejk Equal to 0; when a is jk When the concentration of the carbon dioxide is more than 0,
further, a similarity judgment vector { U } is constructed based on the difference values ej }＝{U ejk ＝|L ejk -1| | k =1,2,3, \8230;, n }, wherein U ej Is P e And p j All consulting intentions of similar judgment value, U ejk Is P e And p j The similarity judgment value of the kth consultation intention. For each element U in the vector ejk If it satisfies U ejk >Td, and F (I) ek )>Tf，F(i jk )>Tf, then consult with intention I ek Is P e And p j The difference points of (1); if U is satisfied ejk <Ts, and F (I) ek )>Tf,，F(i jk )>Tf, then consult with intention I ek Is P e And p j Wherein Td is a variance threshold, ts is a homogeneity threshold, tf is a frequency threshold, and 1>Td>Ts>0。
Compared with the prior art, a large amount of data is acquired based on access and search, and the competitive products are identified based on the data, namely the competitive product identification can be performed only on the surface based on the action chain of the user, and the mind and motivation of the user cannot be acquired to perform more accurate identification on the competitive products. And acquiring attitudes of different characteristics of the competitive products based on the evaluation content, and acquiring the preference of the user. Then, the evaluation is from a user who is actually used, but the user who is used cannot be determined to be the same user for different products, and the content based on the evaluation cannot be determined to be based on the same comparison standard. Therefore, the consistency cannot be achieved in the comparison of the competitive products. According to the method, on the E-commerce platform, the same user consults session data of the same commodity on line in the same time period, a product comparison combination with decision behaviors consistent with the user is constructed by obtaining the intention of a consultation product combination, and the similarity of the same different products on consultation is generated on the basis of intention statistical data, so that the competition relationship among the products is calculated, and the homogeneity point and the difference point are obtained, and the competitive product mining is realized.
Example 11:
as shown in fig. 13, according to an embodiment of the second aspect of the present invention, there is provided an analysis apparatus 1300 for a product, including: an obtaining module 1302, configured to obtain consultation record information of a target user within a preset time period; a first determining module 1304, configured to determine at least one second product related to the first product according to the consultation record information; a second determining module 1306, configured to determine, according to the consultation record information and N pieces of preset consultation information, a first consultation probability of the first product and a second consultation probability of the second product, where N is an integer greater than 0; a third determining module 1308, configured to determine difference information and homogeneity information of the first product and the second product from the N pieces of preset consulting information according to the first consulting probability and the second consulting probability.
In this embodiment, the apparatus 1300 for analyzing a product includes an obtaining module 1302, a first determining module 1304, a second determining module 1306, and a third determining module 1308.
Specifically, the obtaining module 1302 is configured to obtain, for a specific product (i.e., a first product), a target user and a time for which a consultation is initiated on the e-commerce platform, and obtain consultation record information of the target user within a preset time period (e.g., 24 hours before and after the consultation time) based on the time for which the consultation is initiated.
Further, the first determining module 1304 is configured to determine at least one potential bid (i.e., second product) for a product of the same category as the particular product in the advisory record information.
Further, the second determining module 1306 is configured to perform statistical analysis on the consultation record information according to the N consultation intentions (i.e., preset consultation information), obtain the consultation intentions of the target user for consulting the specific product appearing in the consultation record information, and analyze the preference degree (i.e., a first consultation probability) of the target user for the consultation intentions of the specific product and the consultation intentions of the target user for consulting the potential bid product appearing in the consultation record information by using the consultation intentions, and analyze the preference degree (i.e., a second consultation probability) of the target user for the consultation intentions of the potential bid product by using the consultation intentions.
Further, the third determination module 1308 is configured to analyze the target user's preference for a particular product and for potential bids. From the N consulting intents, a difference point (i.e., difference information) at which the feature product cannot be replaced by the potential bid for the target user and a homogeneity point (i.e., homogeneity information) at which the feature product can be replaced by the potential bid are determined.
According to the method, the consultation session data of the same user in the same time period for the same type of products are subjected to statistical analysis, the consultation intentions of the user for consulting the specific products and the potential competitive products are respectively obtained, the preference degree of the user for the consultation intentions of the specific products and the potential competitive products is determined by utilizing the consultation intentions, the emotional trends of the same user for the specific products and the potential competitive products are further analyzed, the competitive relationship among the products is calculated, the difference points and the homogeneous points of the specific products and the potential competitive products are determined, and the mining of the competitive product relationship among the products is realized. Compared with the technical scheme that a large amount of data are acquired based on access to different users and search evaluation content, and competitive products are identified based on the data in the prior art, the method and the device construct a product comparison combination with consistent decision behaviors and the same user, achieve consistency in competitive product comparison, and effectively improve accuracy of comparison between specific products and potential competitive products.
Example 12:
according to an embodiment of the third aspect of the present invention, an electronic device is proposed, which comprises the analysis apparatus of the product proposed by the second aspect. Therefore, the electronic device has all the beneficial effects of the product analysis device provided by the second aspect, and is not repeated to avoid repetition.
Example 13:
as shown in fig. 14, according to an embodiment of the fourth aspect of the present invention, an electronic device 1400 is proposed, comprising: a memory 1402, the memory 1402 storing a program or instructions; the processor 1404, coupled to the memory 1402, is configured to execute the program or instructions to implement the product analysis method of the first aspect. Therefore, the electronic device has all the advantages of the product analysis method provided by the first aspect, and details are not repeated herein.
Example 14:
according to a fifth aspect of the present invention, a readable storage medium is proposed, on which a program or instructions are stored, which program or instructions, when executed by a processor, carry out the analysis method of the product proposed by the first aspect. Therefore, the readable storage medium has all the advantages of the product analysis method provided by the first aspect, and redundant description is omitted for avoiding repetition.
In the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of another like element in a process, method, article, or apparatus that comprises the element. Further, it should be noted that the scope of the methods and apparatuses in the embodiments of the present application is not limited to performing the functions in the order illustrated or discussed, but may include performing the functions in a substantially simultaneous manner or in a reverse order based on the functions recited, e.g., the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. In addition, features described with reference to certain examples may be combined in other examples.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the methods of the embodiments of the present application.
In the description herein, the description of the terms "one embodiment," "some embodiments," "specific embodiments," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method of analyzing a product, comprising:
acquiring consultation record information of a target user in a preset time period;
determining at least one second product related to the first product according to the consultation record information;
determining a first consultation probability of the first product and a second consultation probability of the second product according to the consultation record information and N pieces of preset consultation information, wherein N is an integer greater than 0;
and determining difference information and homogeneity information of the first product and the second product from N pieces of preset consultation information according to the first consultation probability and the second consultation probability.
2. The method for analyzing products according to claim 1, wherein the step of determining at least one second product corresponding to the first product according to the information of the consultation record specifically comprises:
acquiring the category information of the first product;
and determining at least one second product related to the first product in the consultation record information according to the category information.
3. The method for analyzing a product according to claim 1, wherein the step of determining the first consultation probability of the first product according to the consultation record information and N pieces of preset consultation information includes:
acquiring first consultation record information related to the first product in the consultation record information;
determining a first frequency of occurrence of each preset consultation information from the first consultation record information;
calculating the first consultation probability according to the first frequency and a first formula;
the first formula is:
wherein e is the first product, I e For all the preset counseling information appearing in the first counseling record information, above I ek For the kth preset counseling information appearing in the first counseling record information, above A ek Is the first consultThe k-th probability of occurrence of the predetermined advisory information in the advisory information, F (I) above ek ) For the frequency of occurrence of the kth preset counseling information, the above
K is the sum of the frequency of occurrence of all the preset consultation information, and belongs to {1,2,3, \8230;, N }.
4. The product analysis method according to claim 1, wherein the step of determining a second consultation probability for the second product according to the consultation record information and the N pieces of preset consultation information specifically includes:
acquiring second consultation record information related to the second product in the consultation record information;
determining a second frequency of occurrence of each of the preset counseling information from the second counseling record information;
calculating the second consultation probability according to the second frequency and a second formula;
the second formula is:
wherein j is the second product, i is j For all the preset consultation information appearing in the second consultation record information, i jk A is the k-th preset counseling information appearing in the second counseling record information jk The probability of occurrence of the kth preset consultation information in the second consultation record information is F (i) jk ) For the frequency of occurrence of the kth preset counseling information, the above
K is the sum of the frequency of occurrence of all the preset consultation information, and belongs to {1,2,3, \8230;, N }.
5. The method for analyzing a product according to any one of claims 1 to 4, wherein the step of determining the difference information and the homogeneity information of the first product and the second product from the N pieces of preset consulting information according to the first consulting probability and the second consulting probability specifically comprises:
determining a difference value between the first product and the second product according to the first consultation probability and the second consultation probability;
and determining the difference information and the homogeneity information of the first product and the second product from N pieces of preset consultation information according to the difference value, the first consultation probability and the second consultation probability.
6. The method for analyzing a product according to claim 5, wherein the determining a difference value between the first product and the second product according to the first consultation probability and the second consultation probability includes:
based on the second advisory probability being equal to 0, the difference value being 0;
calculating the difference value according to a third formula based on the second consultation probability not being equal to 0;
the third formula is:
wherein L is ejk Is the difference value of the first product and the second product, above A ek Is the first probability of consultation, above a jk Is the second consultation probability.
7. The method for analyzing a product according to claim 5, wherein the step of determining the difference information and the homogeneity information of the first product and the second product from the N pieces of preset consulting information according to the difference value, the first consulting probability and the second consulting probability specifically comprises:
determining first consultation information meeting a first preset condition from the N pieces of preset consultation information;
using the first consultation information as the difference information of the first product and the second product, wherein the first preset condition is that the absolute value of the difference between the difference value and a first threshold is greater than a second threshold, the first consultation probability is greater than a third threshold, and the second consultation probability is greater than the third threshold;
determining second consultation information meeting a second preset condition from the N preset consultation information;
using the second advisory information as the homogeneity information of the first product and the second product, wherein the second preset condition is that the absolute value of the difference between the difference value and the first threshold is smaller than a fourth threshold, the first advisory probability is greater than the third threshold, and the second advisory probability is greater than the third threshold;
wherein 0 < the fourth threshold < the second threshold < 1.
8. The method for analyzing a product according to claim 1, wherein when the number of the second products is plural, the method further comprises:
determining the similarity of the first product and each second product according to the first consultation probability and the second consultation probability;
determining a second product from the plurality of second products for which the similarity is greater than a similarity threshold.
9. An apparatus for analyzing a product, comprising:
the acquisition module is used for acquiring the consultation record information of the target user in a preset time period;
the first determining module is used for determining at least one second product related to the first product according to the consultation record information;
the second determining module is used for determining a first consultation probability of the first product and a second consultation probability of the second product according to the consultation record information and N pieces of preset consultation information, wherein N is an integer larger than 0;
and the third determining module is used for determining the difference information and the homogeneity information of the first product and the second product from the N pieces of preset consultation information according to the first consultation probability and the second consultation probability.
10. An electronic device, comprising:
an apparatus for analyzing a product according to claim 9.
11. An electronic device, comprising:
a memory storing a program or instructions;
a processor connected to the memory, the processor when executing the program or instructions performing a method of analyzing a product according to any of claims 1 to 8.
12. A readable storage medium on which a program or instructions are stored, characterized in that said program or instructions, when executed by a processor, implement the steps of the method of analysis of a product according to any one of claims 1 to 8.
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