Product ranking method, device, equipment and storage medium based on multidimensional analysis

Abstract

The present invention relates to artificial intelligence technology and discloses a product ranking method based on multidimensional analysis, comprising: constructing a product portrait based on product descriptions; obtaining user data and constructing a user portrait based on the user data; calculating the user's preference value for each product based on the user portrait and product portrait; calculating the user's attention weight for each performance indicator corresponding to each product based on the current product stage and user portrait of each product; obtaining the evaluation score of a user's similar user group for each of multiple products, and calculating the score weight for each product based on the evaluation score; calculating a comprehensive score for each product based on the preference value, attention weight, and score weight, and ranking the multiple products according to the comprehensive score. In addition, the present invention also relates to blockchain technology, and product descriptions can be stored in blockchain nodes. The present invention also proposes a product ranking device, equipment, and storage medium based on multidimensional analysis. The present invention can improve the accuracy of product rankings.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a product ranking method, device, electronic device, and computer-readable storage medium based on multidimensional analysis. Background Art

[0002] As people's needs continue to increase, a large number of products have emerged on the market for people to choose from, such as stocks, funds, options and other products, or related products such as quantitative solutions derived from stocks, funds, options and other products. In order to facilitate people's selection of massive products, multiple products are often ranked for people's reference.

[0003] Currently, the ranking methods of products on the market are relatively simple, usually using a single indicator to judge in ascending or descending order. However, different users may have different weight requirements for each indicator when ranking products. Therefore, this method neither takes into account the personal preferences of different users nor the role of each indicator in different stages of the product when ranking products, resulting in a low degree of personalization in the ranking results. Summary of the Invention

[0004] The present invention provides a product ranking method, device and computer-readable storage medium based on multidimensional analysis, the main purpose of which is to solve the problem of low accuracy in product ranking.

[0005] To achieve the above objectives, the present invention provides a product ranking method based on multidimensional analysis, comprising:

[0006] Obtain product descriptions of multiple products and multiple performance indicators corresponding to each product, and construct a product profile for each product based on the product descriptions;

[0007] Obtain user data of a target user, and construct a user profile of the target user based on the user data;

[0008] Calculating the target user's preference value for each product based on the user portrait and the product portrait;

[0009] Obtain the current product stage of each product, determine the performance indicator corresponding to the product stage as the stage indicator, and calculate the target user's attention weight for each stage indicator corresponding to each product based on the product stage and the user profile;

[0010] Obtaining evaluation scores of similar user groups of the target user for each of the multiple products, and calculating a score weight for each product based on the evaluation scores;

[0011] A comprehensive score for each product is calculated based on the preference value, the attention weight, and the score weight, and the multiple products are ranked according to the comprehensive score.

[0012] Optionally, constructing a product portrait for each product based on the product description includes:

[0013] Selecting one of the multiple products as a target product one by one, performing core semantic extraction on the product description of the target product to obtain product semantics;

[0014] Performing vector conversion on the product semantics to obtain a semantic vector;

[0015] The semantic vectors are concatenated to obtain a product portrait of the target product.

[0016] Optionally, extracting core semantics from the product description of the target product to obtain product semantics includes:

[0017] Performing convolution and pooling processing on the product description to obtain low-dimensional feature semantics of the product description;

[0018] Mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;

[0019] The high-dimensional feature semantics are screened using a preset activation function to obtain product semantics.

[0020] Optionally, the step of concatenating the semantic vectors to obtain a product portrait of the target product includes:

[0021] Counting the vector length of each vector in the semantic vector, and selecting the largest vector length as the target length;

[0022] Extending the vector length of each of the semantic vectors to the target length;

[0023] The extended semantic vectors are spliced ​​in column dimension to obtain a product portrait of the target product.

[0024] Optionally, calculating the target user's preference value for each product based on the user portrait and the product portrait includes:

[0025] Selecting one of the products as a target product from the multiple products one by one;

[0026] Calculate the distance between the user profile and the product profile corresponding to the target product using a preset distance algorithm;

[0027] The reciprocal of the distance value is determined as the preference value of the target user for the target product.

[0028] Optionally, calculating the target user's attention weight for each stage indicator corresponding to each product according to the product stage and the user portrait includes:

[0029] Selecting one of the products from the multiple products one by one as a product to be analyzed;

[0030] Calculating the first matching degree between the user profile and each stage indicator corresponding to the product to be analyzed one by one;

[0031] Calculating the second matching degree between the product stage and each stage indicator corresponding to the product to be analyzed one by one;

[0032] One of the stage indicators is selected one by one from the multiple stage indicators corresponding to the product to be analyzed as the target indicator, and the sum of the first matching degree and the second matching degree corresponding to the target indicator is used as the attention weight of the target indicator.

[0033] Optionally, calculating the score weight of each product according to the evaluation score includes:

[0034] Selecting one product from the multiple products one by one as a product to be evaluated;

[0035] Calculating the sum of evaluation scores of all users in the similar user group on all products in the plurality of products to obtain a total score;

[0036] Counting the sum of the evaluation scores of each user in the similar user group on the product to be evaluated to obtain an independent product score;

[0037] The score weight of the product to be evaluated is obtained by dividing the independent product score by the total score.

[0038] In order to solve the above problems, the present invention further provides a product ranking device based on multidimensional analysis, the device comprising:

[0039] A profile building module is used to obtain product descriptions of multiple products and multiple performance indicators corresponding to each product, build a product profile for each product based on the product descriptions, obtain user data of target users, and build a user profile for the target users based on the user data;

[0040] A preference analysis module, configured to calculate the target user's preference value for each product based on the user profile and the product profile;

[0041] A weight analysis module is used to obtain the current product stage of each product, determine the performance indicator corresponding to the product stage as the stage indicator, and calculate the target user's attention weight for each stage indicator corresponding to each product based on the product stage and the user profile;

[0042] An evaluation analysis module is used to obtain evaluation scores of a user group similar to the target user for each of the multiple products, and calculate a score weight for each product based on the evaluation scores;

[0043] A product ranking module is used to calculate a comprehensive score for each product based on the preference value, the attention weight and the score weight, and to rank the multiple products according to the comprehensive score.

[0044] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0045] at least one processor; and,

[0046] a memory communicatively connected to the at least one processor; wherein,

[0047] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the above-mentioned product ranking method based on multi-dimensional analysis.

[0048] In order to solve the above problems, the present invention also provides a computer-readable storage medium, which stores at least one computer program. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned product ranking method based on multidimensional analysis.

[0049] The embodiment of the present invention can analyze products through three dimensions: product portrait, user portrait, and product stage, fully considering the user's own preference for the product and the user's attention weight for different performance indicators of the product at different stages, thereby improving the degree of personalization of the product ranking results to the user. At the same time, based on the evaluation score of each product by the user's similar user groups, the user's score weight for each product is analyzed, and then the user's preference value, attention weight, and score weight for each product are combined to calculate the comprehensive score of each product, so as to improve the accuracy of product ranking. Therefore, the product ranking method, device, electronic device, and computer-readable storage medium based on multidimensional analysis proposed in the present invention can solve the problem of low accuracy in product ranking. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1A flowchart of a product ranking method based on multidimensional analysis provided by one embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a process for constructing a product profile for each product according to an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a flow chart for calculating attention weights according to an embodiment of the present invention;

[0053] Figure 4 A functional module diagram of a product ranking device based on multi-dimensional analysis provided by one embodiment of the present invention;

[0054] Figure 5 A schematic structural diagram of an electronic device for implementing the product ranking method based on multi-dimensional analysis provided by an embodiment of the present invention.

[0055] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0056] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] The embodiment of the present application provides a product ranking method based on multidimensional analysis. The execution subject of the product ranking method based on multidimensional analysis includes but is not limited to at least one of the electronic devices such as the server, the terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the product ranking method based on multidimensional analysis can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0058] Reference Figure 1 FIG. 1 is a flow chart of a product ranking method based on multidimensional analysis according to an embodiment of the present invention. In this embodiment, the product ranking method based on multidimensional analysis includes:

[0059] S1. Obtain product descriptions of multiple products and multiple performance indicators corresponding to each product, and build a product profile for each product based on the product descriptions.

[0060] In an embodiment of the present invention, the products include actual commodity products (such as mobile phones, computers, food, furniture, etc.), virtual digital products (stocks, funds, options, etc.), and derivatives of virtual digital products (such as quantitative solutions for stocks, funds, options, etc.).

[0061] In detail, the product description includes descriptions such as the product name, product content, applicable groups, precautions, etc.; the performance indicators refer to the measurement indicators of various performance corresponding to each product. For example, the performance indicators of electronic products such as mobile phones and computers can be usage fluency, device memory, device usage cycle, etc.; the performance indicators of products such as stocks, funds, and options can be yield rate, drawdown rate, Karma ratio, etc.

[0062] Specifically, computer statements with data capture functions (such as Java statements, Python statements, etc.) can be used to capture product descriptions of the multiple products and multiple performance indicators corresponding to each product from a predetermined data storage area, where the data storage area includes but is not limited to a database, a blockchain node, and a network cache.

[0063] In one of the practical application scenarios of the present invention, since each product description contains a large amount of content information, directly using the product description to perform product ranking analysis will occupy a large amount of computing resources, resulting in low analysis efficiency.

[0064] In an embodiment of the present invention, each of the product descriptions may be analyzed, and a product portrait of each product may be generated based on key content within the product description.

[0065] In the embodiment of the present invention, Figure 2 As shown, the product portrait of each product is constructed based on the product description, including:

[0066] S21, selecting one of the multiple products as a target product one by one, performing core semantic extraction on the product description of the target product to obtain product semantics;

[0067] S22, performing vector conversion on the product semantics to obtain a semantic vector;

[0068] S23. Concatenate the semantic vectors to obtain a product portrait of the target product.

[0069] In the embodiment of the present invention, the target product may be selected from the multiple products in sequence, or the target product may be selected from the multiple products randomly without replacement.

[0070] In an embodiment of the present invention, a pre-built semantic analysis model may be used to extract core semantics from the product description of the target product to obtain product semantics.

[0071] In detail, the semantic analysis model includes but is not limited to an NLP (Natural Language Processing) model and an HMM (Hidden Markov Model).

[0072] For example, a pre-built semantic analysis model is used to perform convolution, pooling and other operations on the product description of the target product to extract the low-dimensional feature expression of the product description, and then the extracted low-dimensional feature expression is mapped to a pre-built high-dimensional space to obtain the high-dimensional feature expression of the low-dimensional feature, and the high-dimensional feature expression is selectively output using a preset activation function to obtain product semantics.

[0073] In the embodiment of the present invention, extracting core semantics from the product description of the target product to obtain product semantics includes:

[0074] Performing convolution and pooling processing on the product description to obtain low-dimensional feature semantics of the product description;

[0075] Mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics;

[0076] The high-dimensional feature semantics are screened using a preset activation function to obtain product semantics.

[0077] In detail, the product description can be convolved and pooled through a semantic analysis model to reduce the data dimension of the product description, thereby reducing the computing resources occupied when analyzing the product description and improving the efficiency of core semantic extraction.

[0078] Specifically, a preset mapping function may be used to map low-dimensional feature semantics to a pre-constructed high-dimensional space. The mapping function includes a Gaussian Radial Basis Function function and a Gaussian function in the MATLAB library.

[0079] For example, if the low-dimensional feature semantics is a point in a two-dimensional plane, the two-dimensional coordinates of the point in the two-dimensional plane can be calculated using a mapping function to convert the two-dimensional coordinates into three-dimensional coordinates, and the calculated three-dimensional coordinates can be used to map the point to a pre-constructed three-dimensional space to obtain the high-dimensional feature semantics of the low-dimensional feature semantics.

[0080] Mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space can improve the classifiability of the low-dimensional features, thereby improving the accuracy of filtering features from the obtained high-dimensional feature semantics to obtain product semantics.

[0081] In an embodiment of the present invention, a preset activation function can be used to calculate the output value of each feature semantics in the high-dimensional feature semantics, and the feature semantics whose output value is greater than a preset output threshold are selected as product semantics. The activation function includes but is not limited to a sigmoid activation function, a tanh activation function, and a relu activation function.

[0082] For example, there are feature semantics A, feature semantics B and feature semantics C in the high-dimensional feature semantics. The activation function is used to calculate feature semantics A, feature semantics B and feature semantics C respectively, and the output value of feature semantics A is 80, the output value of feature semantics B is 30, and the output value of feature semantics C is 70. When the output threshold is 50, feature semantics A and feature semantics C are output as the product semantics of the target product.

[0083] In an embodiment of the present invention, the product semantics may be vector-converted using a preset vector conversion model to obtain a first semantic vector. The vector conversion model includes but is not limited to a word2vec model and a Bert model.

[0084] In the embodiment of the present invention, the step of concatenating the semantic vectors to obtain a product portrait of the target product includes:

[0085] Counting the vector length of each vector in the semantic vector, and selecting the largest vector length as the target length;

[0086] Extending the vector length of each of the semantic vectors to the target length;

[0087] The extended semantic vectors are spliced ​​in column dimension to obtain a product portrait of the target product.

[0088] In detail, since the lengths of the semantic vectors may be different, in order to perform vector concatenation on the semantic vectors, the vector lengths of the semantic vectors need to be unified.

[0089] In the embodiment of the present invention, the vector length of each semantic vector is counted, and vectors with shorter vector lengths are extended according to the maximum vector length, so that the lengths of all the semantic vectors are the same.

[0090] For example, the first semantic vector is [11, 36, 22] and the second semantic vector is [14, 25, 31, 27]. Statistics show that the vector length of the target vector of the first semantic vector is 3, and the second vector length of the second semantic vector is 4. The second vector length is greater than the vector length of the target vector. In this case, the first semantic vector can be vector extended using preset parameters (such as 0) until the vector length of the target vector is equal to the preset standard vector length, thereby obtaining the extended first semantic vector [11, 36, 22, 0].

[0091] In the embodiment of the present invention, the two vectors may be merged in the column dimension by adding corresponding column elements in the two vectors.

[0092] For example, the first semantic vector is [11, 36, 22, 0] and the second semantic vector is [14, 25, 31, 27]. The elements of the corresponding columns in the semantic vectors can be added to obtain the product portrait [25, 61, 53, 27].

[0093] In another embodiment of the present invention, the corresponding column elements in the two vectors may be displayed in parallel to generate a matrix using the two vectors, thereby achieving column dimension merging between the vectors.

[0094] For example, if the first semantic vector is [11, 36, 22, 0] and the second semantic vector is [14, 25, 31, 27], the elements of the corresponding columns in the semantic vectors can be displayed in parallel to obtain the matrix And use this matrix as the product portrait of the target product.

[0095] S2. Obtain user data of the target user, and construct a user profile of the target user based on the user data.

[0096] In the embodiment of the present invention, the user data includes but is not limited to the user's name, age, occupation, address and other information.

[0097] In detail, the user data may be uploaded in advance by the target user.

[0098] Specifically, the step of constructing a user portrait of the target user based on the user data is consistent with the step of constructing a product portrait of each product based on the product description in S1, and will not be repeated here.

[0099] S3. Calculate the target user's preference value for each product based on the user portrait and the product portrait.

[0100] In an embodiment of the present invention, since the user portrait can express key information related to the target user, and the product portrait can express key information related to each product, the preference value of the target user for each product can be obtained directly based on the user portrait and the product portrait.

[0101] In the embodiment of the present invention, calculating the target user's preference value for each product based on the user portrait and the product portrait includes:

[0102] Selecting one of the products as a target product from the multiple products one by one;

[0103] Calculate the distance between the user profile and the product profile corresponding to the target product using a preset distance algorithm;

[0104] The reciprocal of the distance value is determined as the preference value of the target user for the target product.

[0105] Specifically, the calculating the distance value between the user profile and the product profile corresponding to the target product by using a preset distance algorithm includes:

[0106] The distance value between the user profile and the product profile corresponding to the target product is calculated using the following distance value algorithm:

[0107]

[0108] Where D is the distance value, x is the user portrait, y i is the i-th product portrait, and α is the preset coefficient.

[0109] S4. Obtain the current product stage of each product, determine the performance indicator corresponding to the product stage as the stage indicator, and calculate the target user's attention weight for each stage indicator corresponding to each product based on the product stage and the user portrait.

[0110] In the embodiment of the present invention, the current product stage refers to data such as the life cycle and operation stage of each product.

[0111] In detail, the step of obtaining the current product stage of each product is consistent with the step of obtaining product descriptions of multiple products in S1, and will not be described in detail here.

[0112] Furthermore, the CREATEINDEX function in the SQL library can be used to query the preset stage-performance indicator data table to determine that the performance indicator corresponding to the product stage is the stage indicator, wherein the stage-performance indicator data table stores multiple performance indicators and information about the product stage to which each performance indicator belongs.

[0113] In detail, the performance indicator corresponding to the current product stage of each product can be queried from the stage-performance indicator data table, and the performance indicator corresponding to the current product stage of each product can be determined as the stage indicator of the product.

[0114] In one practical application scenario of the present invention, when a product is in different product stages, the user's attention to each performance indicator corresponding to the product is also inconsistent.

[0115] For example, there is a fund product A, which corresponds to a developability index and a current rate of return index. The developability index refers to the future development expectations of the fund product A, and the current rate of return index refers to the current rate of return of the fund. Therefore, in the early stage of the fund product, users are more concerned about the product's developability index, while in the later stage, users are more concerned about the product's current rate of return index.

[0116] Therefore, the embodiment of the present invention can perform analysis based on the product stage and the user portrait to obtain the target user's attention weight for each of the multiple performance indicators of the product when each product is in the current product stage, wherein the greater the attention weight, the more attention the target user pays to the performance indicator.

[0117] In the embodiment of the present invention, Figure 3 As shown, the calculation of the target user's attention weight for each stage indicator corresponding to each product based on the product stage and the user portrait includes:

[0118] S21, selecting one of the multiple products one by one as a product to be analyzed;

[0119] S22. Calculate the first matching degree between the user profile and each stage indicator corresponding to the product to be analyzed one by one;

[0120] S23, calculating the second matching degree between the product stage and each stage indicator corresponding to the product to be analyzed one by one;

[0121] S24 , selecting one of the multiple stage indicators corresponding to the product to be analyzed as a target indicator one by one, and taking the sum of the first matching degree and the second matching degree corresponding to the target indicator as the attention weight of the target indicator.

[0122] Specifically, calculating the first matching degree between the user profile and each stage indicator corresponding to the product to be analyzed one by one includes:

[0123] The first matching degree between the user profile and each stage indicator corresponding to the product to be analyzed is calculated one by one using the following matching algorithm:

[0124]

[0125] Where P is the first matching degree, x is the user portrait, y i is the i-th product portrait, and α is the preset coefficient.

[0126] Specifically, the step of calculating the second matching degree between the product stage and each stage indicator corresponding to the product to be analyzed one by one is consistent with the step of calculating the first matching degree between the user portrait and each stage indicator corresponding to the product to be analyzed one by one, and will not be repeated again.

[0127] S5. Obtain evaluation scores of similar user groups to the target user for each of the multiple products, and calculate a score weight for each product based on the evaluation scores.

[0128] In the embodiment of the present invention, the evaluation score is a value obtained by evaluating and scoring each of the multiple products by each user in the user group similar to the target user. The larger the evaluation score, the higher the user's preference for the product.

[0129] In detail, the similar user group is a plurality of users having user portraits that are highly similar to the target user. For example, the similar user group is a plurality of users of the same age as the target user, or the similar user group is a plurality of users having the same occupation as the target user.

[0130] Specifically, the step of obtaining the evaluation score of each of the multiple products by a user group similar to the target user is consistent with the step of obtaining the product descriptions of the multiple products in S1, and will not be described in detail here.

[0131] In the embodiment of the present invention, the step of calculating the score weight of each product according to the evaluation score includes:

[0132] Selecting one product from the multiple products one by one as a product to be evaluated;

[0133] Calculating the sum of evaluation scores of all users in the similar user group on all products in the plurality of products to obtain a total score;

[0134] Counting the sum of the evaluation scores of each user in the similar user group on the product to be evaluated to obtain an independent product score;

[0135] The score weight of the product to be evaluated is obtained by dividing the independent product score by the total score.

[0136] For example, the multiple products include product A and product B, and the similar user group includes user a and user b, wherein user a's evaluation score for product A is 10, user a's evaluation score for product B is 40, user b's evaluation score for product A is 25, and user b's evaluation score for product B is 25. It can be seen that when product A is selected as the product to be evaluated, the sum of the evaluation scores of all users in the similar user group for all products in the multiple products is 100 (total score), and the sum of the evaluation scores of each user in the similar user group for the product to be evaluated (product A) is 35 (independent product score). Therefore, the score weight of the product to be evaluated (product A) is 0.35.

[0137] In an embodiment of the present invention, since the target user has a limited number of uses and evaluations of products, there may be some products among the multiple products that the target user has not used or evaluated. Therefore, the evaluation scores of similar user groups of the target user for each of the multiple products can be obtained, thereby expanding the amount of data that can be analyzed, so as to facilitate the analysis of the target user's preference for all products among the multiple products (i.e., score weights).

[0138] S6. Calculate a comprehensive score for each product based on the preference value, the attention weight, and the score weight, and rank the multiple products according to the comprehensive score.

[0139] In one embodiment of the present invention, the preference value, the attention weight and the score weight can be added together, and the summed value can be used as the comprehensive score of each product, and then the multiple products can be ranked in descending order according to the comprehensive score.

[0140] In another embodiment of the present invention, the preference value, the attention weight and the score weight can be weighted and summed according to a preset ratio, and the value after the weighted sum is used as the comprehensive score of each product, and then the multiple products are ranked in descending order according to the comprehensive score.

[0141] The embodiment of the present invention can analyze products through three dimensions: product portrait, user portrait, and product stage. It fully considers the user's own preference for the product and the user's attention weight for different performance indicators of the product at different stages, thereby improving the degree of personalization of the product ranking results to the user. At the same time, based on the evaluation score of each product by the user's similar user groups, the user's score weight for each product is analyzed, and then the user's preference value, attention weight, and score weight for each product are combined to calculate the comprehensive score of each product, so as to improve the accuracy of product ranking. Therefore, the product ranking method based on multidimensional analysis proposed in the present invention can solve the problem of low accuracy in product ranking.

[0142] like Figure 4 , which is a functional module diagram of a product ranking device based on multi-dimensional analysis provided by one embodiment of the present invention.

[0143] The product ranking device 100 based on multidimensional analysis described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the product ranking device 100 based on multidimensional analysis can include a profile building module 101, a preference analysis module 102, a weight analysis module 103, an evaluation analysis module 104, and a product ranking module 105. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0144] In this embodiment, the functions of each module / unit are as follows:

[0145] The portrait construction module 101 is used to obtain product descriptions of multiple products and multiple performance indicators corresponding to each product, construct a product portrait for each product based on the product descriptions, obtain user data of a target user, and construct a user portrait of the target user based on the user data;

[0146] The preference analysis module 102 is used to calculate the target user's preference value for each product based on the user portrait and the product portrait;

[0147] The weight analysis module 103 is used to obtain the current product stage of each product, determine the performance indicator corresponding to the product stage as the stage indicator, and calculate the target user's attention weight for each stage indicator corresponding to each product based on the product stage and the user profile;

[0148] The evaluation analysis module 104 is configured to obtain evaluation scores of the target user's similar user groups for each of the multiple products, and calculate a score weight for each product based on the evaluation scores;

[0149] The product ranking module 105 is configured to calculate a comprehensive score for each product based on the preference value, the attention weight, and the score weight, and to rank the multiple products according to the comprehensive score.

[0150] In detail, each module in the product ranking device 100 based on multi-dimensional analysis in the embodiment of the present invention adopts the same Figures 1 to 3 The product ranking method based on multidimensional analysis described in the preceding text uses the same technical means and can produce the same technical effects, so I will not go into details here.

[0151] like Figure 5FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a product ranking method based on multi-dimensional analysis according to an embodiment of the present invention.

[0152] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and executable on the processor 10, such as a product ranking program based on multi-dimensional analysis.

[0153] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and lines, and executing programs or modules stored in the memory 11 (for example, executing a product ranking program based on multi-dimensional analysis, etc.), as well as calling data stored in the memory 11, to perform various functions of the electronic device and process data.

[0154] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Furthermore, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a product ranking program based on multidimensional analysis, but can also be used to temporarily store data that has been output or is to be output.

[0155] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0156] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0157] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0158] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0159] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0160] The product ranking program based on multi-dimensional analysis stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When executed in the processor 10, it can achieve the following:

[0161] Obtain product descriptions of multiple products and multiple performance indicators corresponding to each product, and construct a product profile for each product based on the product descriptions;

[0162] Obtain user data of a target user, and construct a user profile of the target user based on the user data;

[0163] Calculating the target user's preference value for each product based on the user portrait and the product portrait;

[0164] Obtain the current product stage of each product, determine the performance indicator corresponding to the product stage as the stage indicator, and calculate the target user's attention weight for each stage indicator corresponding to each product based on the product stage and the user profile;

[0165] Obtaining evaluation scores of similar user groups of the target user for each of the multiple products, and calculating a score weight for each product based on the evaluation scores;

[0166] A comprehensive score for each product is calculated based on the preference value, the attention weight, and the score weight, and the multiple products are ranked according to the comprehensive score.

[0167] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the drawings, which will not be repeated here.

[0168] Furthermore, if the modules / units integrated into the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0169] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0170] Obtain product descriptions of multiple products and multiple performance indicators corresponding to each product, and construct a product profile for each product based on the product descriptions;

[0171] Obtain user data of a target user, and construct a user profile of the target user based on the user data;

[0172] Calculating the target user's preference value for each product based on the user portrait and the product portrait;

[0173] Obtain the current product stage of each product, determine the performance indicator corresponding to the product stage as the stage indicator, and calculate the target user's attention weight for each stage indicator corresponding to each product based on the product stage and the user profile;

[0174] Obtaining evaluation scores of similar user groups of the target user for each of the multiple products, and calculating a score weight for each product based on the evaluation scores;

[0175] A comprehensive score for each product is calculated based on the preference value, the attention weight, and the score weight, and the multiple products are ranked according to the comprehensive score.

[0176] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0177] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0178] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0179] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0180] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0181] Blockchain, as used in this article, refers to a novel application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each block contains information about a batch of online transactions, used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0182] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0183] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Terms such as "first" and "second" are used to indicate names and do not imply any particular order.

[0184] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A product ranking method based on multidimensional analysis, characterized in that: The method comprises: Obtain product descriptions of multiple products and multiple performance indicators corresponding to each product, and construct a product profile for each product based on the product descriptions; Obtain user data of a target user, and construct a user profile of the target user based on the user data; Calculating the target user's preference value for each product based on the user portrait and the product portrait; Obtain the current product stage of each product, determine the performance indicator corresponding to the product stage as the stage indicator according to a preset stage-performance indicator data table, select one of the multiple products one by one as the product to be analyzed, calculate the first matching degree between the user profile and each stage indicator corresponding to the product to be analyzed one by one, calculate the second matching degree between the product stage and each stage indicator corresponding to the product to be analyzed one by one, select one of the multiple stage indicators corresponding to the product to be analyzed one by one as the target indicator, and use the sum of the first matching degree and the second matching degree corresponding to the target indicator as the attention weight of the target indicator; Obtaining evaluation scores of similar user groups of the target user for each of the multiple products, and calculating a score weight for each product based on the evaluation scores; A comprehensive score for each product is calculated based on the preference value, the attention weight, and the score weight, and the multiple products are ranked according to the comprehensive score.

2. The product ranking method based on multidimensional analysis according to claim 1, characterized in that: The step of constructing a product profile for each product based on the product description includes: Selecting one of the multiple products as a target product one by one, performing core semantic extraction on the product description of the target product to obtain product semantics; Performing vector conversion on the product semantics to obtain a semantic vector; The semantic vectors are concatenated to obtain a product portrait of the target product.

3. The product ranking method based on multidimensional analysis according to claim 2, characterized in that: The core semantic extraction of the product description of the target product to obtain product semantics includes: Performing convolution and pooling processing on the product description to obtain low-dimensional feature semantics of the product description; Mapping the low-dimensional feature semantics to a pre-constructed high-dimensional space to obtain high-dimensional feature semantics; The high-dimensional feature semantics are screened using a preset activation function to obtain product semantics.

4. The product ranking method based on multidimensional analysis according to claim 2, characterized in that: The step of concatenating the semantic vectors to obtain a product portrait of the target product includes: Counting the vector length of each vector in the semantic vector, and selecting the largest vector length as the target length; Extending the vector length of each of the semantic vectors to the target length; The extended semantic vectors are spliced ​​in column dimension to obtain a product portrait of the target product.

5. The product ranking method based on multidimensional analysis according to claim 1, characterized in that: Calculating the target user's preference value for each product based on the user portrait and the product portrait includes: Selecting one of the products as a target product from the multiple products one by one; Calculate the distance between the user profile and the product profile corresponding to the target product using a preset distance algorithm; The reciprocal of the distance value is determined as the preference value of the target user for the target product.

6. The product ranking method based on multidimensional analysis according to any one of claims 1 to 5, characterized in that: Calculating the score weight of each product according to the evaluation score includes: Selecting one product from the multiple products one by one as a product to be evaluated; Calculating the sum of evaluation scores of all users in the similar user group on all products in the plurality of products to obtain a total score; Counting the sum of the evaluation scores of each user in the similar user group on the product to be evaluated to obtain an independent product score; The score weight of the product to be evaluated is obtained by dividing the independent product score by the total score.

7. A product ranking device based on multidimensional analysis, characterized in that: The device comprises: A profile building module is used to obtain product descriptions of multiple products and multiple performance indicators corresponding to each product, build a product profile for each product based on the product descriptions, obtain user data of target users, and build a user profile for the target users based on the user data; A preference analysis module, configured to calculate the target user's preference value for each product based on the user profile and the product profile; A weight analysis module is used to obtain the current product stage of each product, determine the performance indicator corresponding to the product stage as the stage indicator according to a preset stage-performance indicator data table, select one of the multiple products one by one as the product to be analyzed, calculate the first matching degree between the user profile and each stage indicator corresponding to the product to be analyzed one by one, calculate the second matching degree between the product stage and each stage indicator corresponding to the product to be analyzed one by one, select one of the multiple stage indicators corresponding to the product to be analyzed as the target indicator one by one, and use the sum of the first matching degree and the second matching degree corresponding to the target indicator as the attention weight of the target indicator; An evaluation analysis module is used to obtain evaluation scores of a user group similar to the target user for each of the multiple products, and calculate a score weight for each product based on the evaluation scores; A product ranking module is used to calculate a comprehensive score for each product based on the preference value, the attention weight and the score weight, and to rank the multiple products according to the comprehensive score.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the product ranking method based on multidimensional analysis according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the product ranking method based on multidimensional analysis according to any one of claims 1 to 6 is implemented.

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