Saas system based on commodity exchange

Abstract

The invention relates to the technical field of computer application, and provides a saas system based on commodity exchange, and the system comprises a to-be-selected exchange commodity determination module which selects to-be-selected exchange commodities according to the maximum single commodity amount, the average commodity amount and the accumulated points of the shopping of a user; the commodity comment library module is used for updating the commodity comments of the external large shopping website to a commodity comment library; the judgment matrix generation module is used for generating a judgment matrix reflecting commodity attributes according to the comment information in the commodity comment library; the user tendency matrix generation module is used for carrying out decision tree classification on the current shopping commodity and the user registration information to generate a comparison matrix; and the analytic hierarchy model is used for calculating the judgment matrix and the comparison matrix and selecting recommended exchange commodities from the to-be-selected exchange commodities. According to the invention, a small online shopping saas system can obtain appropriate recommended points for commodity exchange only according to the current shopping commodity of the user without depending on the user characteristics generated by multiple times of shopping and multiple times of use of the platform by the user.

Description

Technical Field

[0001] The present invention relates to the field of computer application technology, and in particular to a SaaS system based on commodity exchange. Background Art

[0002] As a cloud platform for shopping, online shopping SaaS systems usually use the method of exchanging user points for goods to provide users with unexpected surprises during the shopping process in order to increase user stickiness to the platform and the frequency of product purchases, so as to enhance user loyalty, promote repeat purchases and improve user activity.

[0003] However, for small online shopping SaaS systems, users may seldom use them or have just started using them, or even have only used them once or twice. As a result, small online shopping SaaS systems are unable to build a user information database with sufficient data to perfectly match the recommended points redemption products with user information, resulting in the points product redemption being unable to fully play its role in improving user shopping experience.

[0004] Therefore, how to enable a small online shopping SaaS system to select recommended points redemption products that match the user based on the review information of external large shopping websites and the user's current shopping information, without relying on the user characteristics generated by multiple shopping and multiple use of the platform, is a technical problem that needs to be solved. Summary of the invention

[0005] To this end, the present invention provides a SaaS system based on commodity exchange, which adopts a hierarchical analysis model, so that the characteristics of the user's shopping commodities and the comments on the commodities on external large shopping websites can be comprehensively judged to determine the recommended points exchange commodities that match the user and meet the user's preferences, thereby realizing a small online shopping SaaS system that can obtain suitable recommended points exchange commodities based only on the user's shopping commodities, without relying on the user characteristics generated by multiple shopping and multiple use of the platform.

[0006] To achieve the above object, the present invention proposes a SaaS system based on commodity exchange, comprising:

[0007] The module for determining the goods to be exchanged is used to select multiple goods to be exchanged from the goods purchased by the user and the exchange goods library according to the highest single item amount, average amount of goods and accumulated points of the user's current shopping;

[0008] The product review library module is interconnected with external large shopping websites to match and filter product reviews on external large shopping websites and generate / update the product review library;

[0009] A judgment matrix generation module, connected to the to-be-selected exchange commodity determination module and the commodity review library module, for generating a judgment matrix reflecting commodity attributes according to the review information of the to-be-selected exchange commodity in the commodity review library;

[0010] A user preference matrix generation module, connected to the selected exchange commodity determination module, is used to perform decision tree classification on the shopping commodities and user registration information, and generate a comparison matrix reflecting the user's preference for the commodity attributes;

[0011] The hierarchical analysis model is connected with the judgment matrix generation module and the user tendency matrix generation module, and uses the hierarchical analysis method to calculate the judgment matrix and the comparison matrix, and selects the recommended exchange commodity from the multiple exchange commodities to be selected.

[0012] Further, the judgment matrix generation module includes a satisfaction judgment matrix unit, and the commodity attributes include commodity satisfaction;

[0013] The satisfaction judgment matrix unit uses a sentiment intensity analysis method to calculate the product satisfaction reflected in the review information, and generates a judgment matrix according to the product satisfaction.

[0014] Further, the satisfaction judgment matrix unit includes a word set generation subunit and a satisfaction calculation subunit;

[0015] The word set generating subunit is used to generate a positive satisfactory comment set, a negative satisfactory comment set, a positive untrustworthy comment set, a negative untrustworthy comment set and a general comment set through a sentiment dictionary;

[0016] The satisfaction calculation subunit is used to classify the comment information by calculating the sentiment intensity analysis method based on the positive goodwill comment set, the negative goodwill comment set, the positive credible comment set, the negative credible comment set and the ordinary comment set, so as to generate a judgment matrix.

[0017] Furthermore, the satisfaction judgment matrix unit also includes a sentiment word set updating subunit;

[0018] The sentiment word set updating subunit is used to determine whether the high-frequency words that are not classified into the positive and kind comment set, the negative and kind comment set, the positive and credible comment set, the negative and credible comment set and the ordinary comment set are emphasis words. If not, they are classified into the positive and kind comment set, the negative and kind comment set, the positive and credible comment set, the negative and credible comment set and the ordinary comment set through cluster analysis.

[0019] Furthermore, the judgment matrix generation module further includes an attention judgment matrix unit, and the commodity attributes further include commodity attention;

[0020] The attention judgment matrix unit calculates the product attention reflected in the comment information by using a word frequency inverse text frequency method, and generates a judgment matrix according to the product attention.

[0021] Furthermore, the judgment matrix generation module further includes a practicality judgment matrix unit, and the commodity attributes further include commodity practicality;

[0022] The utility judgment matrix unit uses a neural network model to calculate the commodity utility reflected in the review information, and generates a judgment matrix according to the commodity utility.

[0023] In the above scheme, by using multiple models to determine the satisfaction, attention and practicality of the selected redemption products based on the product review information of external large shopping websites, it is possible to determine the satisfaction, attention and practicality values ​​of the recommended points redemption products even if the description information about the products on this website is not sufficient.

[0024] Furthermore, the hierarchical analysis model, whose target layer is the product recommendation score, the criterion layer is the product attributes, and the plan layer is the selected exchange product, is used to calculate the comparison matrix weight and the judgment matrix weight through the comparison matrix and the judgment matrix respectively, and calculate the product recommendation score through the comparison matrix weight and the judgment matrix weight.

[0025] Furthermore, the user tendency matrix generation module is used to perform decision tree classification according to the user registration information and the commodity types of the commodities purchased this time to generate the comparison matrix when the user purchases commodities exceeding the quantity threshold;

[0026] Alternatively, when the user purchases commodities whose quantity is less than or equal to the quantity threshold, a decision tree classification is performed according to the user registration information and the commodity type with the highest single item amount to generate the comparison matrix.

[0027] Furthermore, the user registration information includes gender and year of birth;

[0028] The user preference matrix is ​​used to perform decision tree classification into subdivision categories according to the gender, the birth year and the product type, and to generate the comparison matrix according to the correspondence between the subdivision categories and the product attributes.

[0029] In the above scheme, the number of goods purchased by the user is used to classify the user's gender, birth year and product type to generate a comparison matrix. This allows the user to accurately classify the types of goods purchased this time through a mature decision tree, and fully considers the user's preferences for satisfaction, attention and practicality.

[0030] Furthermore, the module for determining the commodities to be selected for redemption is used to select the commodities to be selected for redemption within a first set amount range according to the highest single item amount when the user purchases commodities exceeding the quantity threshold, and remove the commodities to be selected for redemption with an amount less than the amount corresponding to the accumulated points;

[0031] Or, when a user purchases goods less than or equal to a quantity threshold, the selected exchange goods within a second set amount range are selected based on the average amount of the goods, and the selected exchange goods with an amount less than the amount corresponding to the accumulated points are removed.

[0032] In the above scheme, by taking into account the different amounts of a user's single purchase, redemption commodities within different amount ranges are recommended to the user, thereby enhancing the user's sense of surprise.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] 1. By adopting the hierarchical analysis model, according to the characteristics of the user's shopping items and the comments on the items on external large shopping websites, we can comprehensively judge and determine the recommended points redemption items that match the user and meet the user's preferences. This enables a small online shopping SaaS system to obtain suitable recommended points redemption items based only on the user's shopping items this time, without relying on the user characteristics generated by multiple shopping and multiple use of the platform.

[0035] 2. Based on the number of goods purchased by users, the user's gender, birth year and product type are classified to generate a comparison matrix. This allows accurate classification of the types of goods purchased by users through a mature decision tree, fully considering the user's preferences for satisfaction, attention and practicality.

[0036] 3. Based on the different amounts of a user's single purchase, we can recommend redemption products in different amount ranges to users, enhancing their sense of surprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of the general structure of a SaaS system based on commodity exchange according to an embodiment of the present invention;

[0038] Figure 2 It is a schematic diagram of the general process of the SaaS system based on commodity exchange in an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of a judgment matrix generation process of a SaaS system based on commodity exchange in an embodiment of the present invention;

[0040] Figure 4It is a schematic diagram of the hierarchical analysis model structure of the SaaS system based on commodity exchange in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0043] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0044] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] like Figures 1 to 4 As shown, the present invention provides a SaaS system based on commodity exchange, which adopts a hierarchical analysis model, so that the characteristics of the user's shopping commodities and the comments on the commodities on external large shopping websites can be comprehensively judged to determine the recommended points exchange commodities that match the user and meet the user's preferences, thereby realizing a small online shopping SaaS system that can obtain suitable recommended points exchange commodities based only on the user's shopping commodities, without relying on the user characteristics generated by multiple shopping and multiple use of the platform.

[0046] in, Figure 1 This is a schematic diagram of the general structure of a SaaS system based on commodity exchange according to an embodiment of the present invention; Figure 2 It is a schematic diagram of the general process of the SaaS system based on commodity exchange in an embodiment of the present invention; Figure 3 A schematic diagram of a judgment matrix generation process of a SaaS system based on commodity exchange in an embodiment of the present invention; Figure 4It is a schematic diagram of the hierarchical analysis model structure of the SaaS system based on commodity exchange in an embodiment of the present invention.

[0047] like Figures 1 to 4 As shown, this embodiment proposes a SaaS system based on commodity exchange, including: a module for determining commodities to be exchanged, which is used to select multiple commodities to be exchanged from the commodities and exchange commodity libraries of the user's current shopping according to the purchase amount and accumulated points of the user's current shopping; a commodity review library module, which is interconnected with an external large shopping website, and is used to match and screen the commodity reviews of the external large shopping website, and generate / update the commodity review library; a judgment matrix generation module, which is connected to the module for determining commodities to be exchanged and the commodity review library module, and is used to generate a judgment matrix reflecting commodity attributes according to the review information of the commodities to be exchanged in the commodity review library; a user tendency matrix generation module, which is connected to the module for determining commodities to be exchanged, and is used to perform decision tree classification on the commodities and user registration information of this shopping, and generate a comparison matrix reflecting the user's tendency for the commodity attributes; a hierarchical analysis model, which is connected to the judgment matrix generation module and the user tendency matrix generation module, and uses the hierarchical analysis method to calculate the judgment matrix and the comparison matrix, and select recommended exchange commodities from the multiple commodities to be exchanged.

[0048] Specifically, by matching the brand name, category name and key functions of the products in the SaaS system with the products on the external large shopping websites, the product review information on the large shopping websites is automatically captured through crawler programs, third-party data collection tools or API interface calls, data cleaning and processing are performed, valuable information is extracted, duplicate data is removed, missing values ​​are processed, and data formats are converted to generate the product review library. Therefore, through the product review library, the problem that the review information of the small shopping SaaS system is insufficient and cannot fully reflect the attributes of the products is made up.

[0049] The large shopping websites may be JD.com, Taobao, etc.

[0050] The accumulated points include points given to customers, corresponding points obtained by customers from shopping, etc.

[0051] Furthermore, the judgment matrix generation module includes a satisfaction judgment matrix unit, and the product attributes include product satisfaction; the satisfaction judgment matrix unit uses a sentiment intensity analysis method to calculate the product satisfaction reflected in the review information, and generates a judgment matrix based on the product satisfaction.

[0052] Furthermore, the satisfaction judgment matrix unit includes a word set generation subunit, a satisfaction calculation subunit and a matrix generation subunit; the word set generation subunit is used to generate a positive satisfied comment set, a negative satisfied comment set, a positive untrustworthy comment set, a negative untrustworthy comment set and an ordinary comment set through a sentiment dictionary; the satisfaction calculation subunit is used to classify the comment information based on the positive goodwill comment set, the negative goodwill comment set, the positive credible comment set, the negative credible comment set and the ordinary comment set through a sentiment intensity analysis method to generate a judgment matrix.

[0053] Furthermore, the satisfaction judgment matrix unit also includes a sentiment word set updating subunit; the sentiment word set updating subunit is used to judge whether the high-frequency words that are not classified into the positive and kind comment set, the negative and kind comment set, the positive and credible comment set, the negative and credible comment set and the ordinary comment set are emphasis words, if not, they are classified into the positive and kind comment set, the negative and kind comment set, the positive and credible comment set, the negative and credible comment set and the ordinary comment set through cluster analysis.

[0054] Specifically, a method for generating a judgment matrix reflecting the satisfaction is as follows: compare all review word sets of large shopping websites with the positive satisfied review set, negative satisfied review set, positive untrustworthy review set, negative untrustworthy review set and ordinary review set of the HowNet sentiment dictionary to obtain the positive satisfied review set, negative satisfied review set, positive untrustworthy review set, negative untrustworthy review set and ordinary review set of a certain commodity attribute Cj. If there are words in the high-frequency word set Aj that do not belong to any of the above sets, determine whether they are emphasized words. If so, delete them from the high-frequency word set Aj. Otherwise, classify them into the positive goodwill review set, the negative goodwill review set, the positive credible review set, the negative credible review set and the ordinary review set through cluster analysis.

[0055] Determine whether the number of words in the positive and satisfied review set, the negative and satisfied review set, the positive and untrustworthy review set, the negative and untrustworthy review set, and the ordinary review set differ within the set number. If not, delete the words in the larger set until the number of words in each set differs within the set number. The set number is set according to the specificity level of the product, and the specificity level is determined according to the total amount of the category to which the product belongs. For example, toilet paper and paper towels are daily necessities. There are more than 80 items of daily necessities in the SaaS system. Since 80 is greater than 40, its specificity level is the lowest, and the set number corresponds to 20. Therefore, by adjusting the word difference of the positive and kind review set, the negative and kind review set, the positive and credible review set, the negative and credible review set, and the ordinary review set through the specificity level, it is ensured that the scoring will not be inaccurate due to the difference in the number of words while meeting the product specificity characteristics.

[0056] The comment information of each selected redemption commodity in the commodity comment database is classified into positive satisfied comment set, negative satisfied comment set, positive untrustworthy comment set, negative untrustworthy comment set and ordinary comment set by sentiment intensity analysis method to obtain satisfaction Pi. The satisfaction Pi∈{2,-2,1,-1,0}, 2,-2,1,-1,0 corresponds to the largest number of positive satisfied comment set, negative satisfied comment set, positive untrustworthy comment set, negative untrustworthy comment set and ordinary comment set, and the sentiment intensity of the selected redemption commodity is determined. For example, if the comment information of a certain paper towel selected redemption commodity in the commodity comment database is classified into the most words in the negative goodwill comment set, then the satisfaction of the certain paper towel commodity is -2.

[0057] The ratio of the satisfaction of the two selected exchange commodities is used as the element of the judgment matrix unit. This step is a conventional judgment matrix construction method of the hierarchical analysis model, which will not be repeated here.

[0058] It should be noted that the sentiment intensity analysis method is a support vector machine model (SVM), which is trained by a large number of product reviews on large shopping websites, so that it can be used to classify the review information in the product review library.

[0059] Furthermore, the judgment matrix generation module also includes an attention judgment matrix unit, and the product attributes include product attention; the attention judgment matrix unit uses a word frequency inverse text frequency method to calculate the product attention reflected in the comment information, and generates a judgment matrix based on the product attention.

[0060] Specifically, the term frequency inverse text frequency method is to multiply the term frequency (TF) and inverse document frequency (IDF) of the selected redemption product to obtain its satisfaction, so as to combine the importance of the word in the document with its prevalence in the entire product review library to obtain a more accurate measurement value.

[0061] Term frequency (TF) indicates the frequency of a word appearing in the text of the product review library. It is a normalized frequency, that is, the number of occurrences divided by the total number of words in the article to prevent the frequency from being biased towards long files. The more times a word appears in a document, the more important it is in the document. However, there is a problem with simply using term frequency as a measure of importance, that is, it will be biased towards overly common words and ignore some more meaningful words.

[0062] Inverse Document Frequency (IDF) indicates the prevalence of keywords and is used to measure the prevalence of a word in the entire product review database. The Inverse Document Frequency (IDF) is obtained by dividing the total number of documents in the corpus by the number of documents containing the word, and then taking the logarithm of the quotient. It is understandable that if a word appears in many documents, its inverse document frequency will be low; otherwise, it will be high. The purpose of inverse document frequency is to reduce the weight of common words and increase the weight of rare words, thereby highlighting their importance.

[0063] The satisfaction level of a certain redemption product is divided by the total satisfaction level of all redemption products to obtain a normalized satisfaction level.

[0064] The ratio of the normalized satisfaction values ​​of the two selected exchange commodities is used as the element of the judgment matrix unit. This step is a conventional judgment matrix construction method of the hierarchical analysis model, which will not be repeated here.

[0065] Furthermore, the judgment matrix generation module also includes a practicality judgment matrix unit, and the product attributes include product practicality; the practicality judgment matrix unit uses a neural network model to calculate the product practicality reflected in the review information, and generates a judgment matrix based on the product practicality.

[0066] Specifically, the neural network model adopts the neural network technology of the text evaluation knowledge base of Wien Consulting Company, and by establishing rules, the neural network model has an accuracy rate of about 95% in classifying and identifying practical text evaluation information.

[0067] More specifically, the evaluation classification data used by Wien Consulting for training neural network models was quantified as follows: points were given according to the number of practicality category words contained in the review information, with each practicality category word getting 1 point, positive evaluations getting positive points, and negative evaluations getting negative points, and the practicality being the sum of the absolute values ​​of the positive and negative evaluation scores.

[0068] The utility of a certain exchange commodity to be selected is divided by the total utility of all exchange commodities to be selected to obtain a normalized utility value. The ratio of the normalized utility values ​​of two exchange commodities to be selected is used as an element of the judgment matrix unit. This step is a conventional judgment matrix construction method of the hierarchical analysis model, which will not be repeated here.

[0069] In the above scheme, by using multiple models to determine the satisfaction, attention and practicality of the selected redemption products based on the product review information of external large shopping websites, it is possible to determine the satisfaction, attention and practicality values ​​of the recommended points redemption products even if the description information about the products on this website is not sufficient.

[0070] Further, see Figure 3 The hierarchical analysis model, whose target layer is the commodity recommendation score, the criterion layer is the commodity attributes, and the solution layer is the selected exchange commodity, is used to calculate the comparison matrix weight and the judgment matrix weight through the comparison matrix and the judgment matrix respectively, and calculate the commodity recommendation score through the comparison matrix weight and the judgment matrix weight.

[0071] Specifically, the hierarchical analysis model performs a normalization test on the comparison matrix weights and the judgment matrix weights, that is, calculates the maximum characteristic root, the consistency index CI and the test coefficient CR. If the test coefficient CR is less than 0.1, the comparison matrix weights and the judgment matrix weights pass the consistency test, otherwise the product review library is updated, and the sentiment word set update subunit updates each sentiment word set.

[0072] The number of keywords of the secondary risk with the most corresponding keywords in the financial negative vocabulary level library is deleted according to the keywords with the most repeated words in the secondary risk keywords.

[0073] Furthermore, the user propensity matrix is ​​used to perform decision tree classification based on the user registration information and the types of goods purchased this time to generate the comparison matrix when the user purchases goods exceeding the quantity threshold; the user propensity matrix is ​​also used to perform decision tree classification based on the user registration information and the type of goods with the highest amount in the current shopping to generate the comparison matrix when the user purchases goods less than or equal to the quantity threshold.

[0074] Furthermore, the user registration information includes gender and birth year; the user preference matrix is ​​used to perform decision tree classification into subcategories according to the gender, the birth year and the product type, and generate the comparison matrix according to the correspondence between the subcategories and the product attributes.

[0075] Specifically, the decision tree classification is XGBoost, which is a machine learning algorithm based on the gradient boosting decision tree GBDT (Gradient Boosting Decision Tree), which is trained based on residuals by using the gradient descent method. After the first decision tree is trained according to the data and target variables, the residual of the tree is calculated according to the difference between the predicted value and the actual value. The second tree is trained using the residual as the target, and the subsequent decision trees are trained on the basis of the residual of the previous round of decision trees. The purpose of data regression is achieved by continuously reducing the residual generated during the training process. The final prediction value is the target value predicted by the first tree and the product of the learning rate (Learning Rate) and the residual of all other trees.

[0076] In this embodiment, preference arrays of gender, birth year, product category, satisfaction, attention and practicality are used to train XGBoost so that a small online shopping SaaS system can accurately classify and determine user preferences.

[0077] First, the gender of the shopping user is determined to be consistent with the gender of the user's registration information through multiple classifications of the commodity types. If consistent, the gender is classified according to the birth year of the user's registration information. Each birth year corresponds to a set of satisfaction, attention and practicality scores, and the ratio of the two commodity attributes is used as an element of the comparison matrix. For example, male users born in the 1980s scored 1, 1, and 2 for satisfaction, attention, and practicality. Therefore, it is highly likely that toilet paper products will not be recommended as redemption products.

[0078] In the above scheme, the number of goods purchased by the user is used to classify the user's gender, birth year and product type to generate a comparison matrix. This allows the user to accurately classify the types of goods purchased this time through a mature decision tree, and fully considers the user's preferences for satisfaction, attention and practicality.

[0079] Furthermore, the purchase amount includes the highest single item amount and the average item amount; the module for determining the items to be exchanged is used to select the items to be exchanged within a first set amount range according to the highest single item amount when the user purchases items exceeding a quantity threshold; the module for determining the items to be exchanged is also used to select the items to be exchanged within a second set amount range according to the average item amount when the user purchases items less than or equal to a quantity threshold; the module for determining the items to be exchanged is also used to remove the items to be exchanged whose amounts are less than the amount corresponding to the accumulated points.

[0080] In the above scheme, by taking into account the different amounts of a user's single purchase, redemption commodities within different amount ranges are recommended to the user, thereby enhancing the user's sense of surprise.

[0081] It is understandable that the use of the hierarchical analysis model allows the characteristics of the user's shopping products and the comments on the products on external large shopping websites to comprehensively determine the recommended points redemption products that match the user and meet the user's preferences, realizing that a small online shopping SaaS system can only be based on the user's shopping products this time, without relying on the user characteristics generated by multiple shopping and multiple use of the platform. The user's gender, birth year and product type are classified according to the number of products purchased by the user to generate a comparison matrix, and the user's shopping product types are accurately classified through a mature decision tree, fully considering the user's preferences for satisfaction, attention and practicality. According to the different amounts of users' single shopping, it is possible to recommend redemption products of different amount ranges to users, enhancing the user's sense of surprise.

[0082] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0083] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A SaaS system based on commodity exchange, characterized in that: include: The module for determining the goods to be exchanged is used to select multiple goods to be exchanged from the goods purchased by the user and the exchange goods library according to the highest single item amount, average amount of goods and accumulated points of the user's current shopping; The product review library module is interconnected with external large shopping websites to match and filter product reviews on external large shopping websites and generate / update the product review library; A judgment matrix generation module, connected to the to-be-selected exchange commodity determination module and the commodity review library module, for generating a judgment matrix reflecting commodity attributes according to the review information of the to-be-selected exchange commodity in the commodity review library; A user preference matrix generation module, connected to the selected exchange commodity determination module, is used to perform decision tree classification on the shopping commodities and user registration information, and generate a comparison matrix reflecting the user's preference for the commodity attributes; The hierarchical analysis model is connected with the judgment matrix generation module and the user tendency matrix generation module, and uses the hierarchical analysis method to calculate the judgment matrix and the comparison matrix, and selects the recommended exchange commodity from the multiple exchange commodities to be selected.

2. The SaaS system based on commodity exchange according to claim 1 is characterized in that: The judgment matrix generation module includes a satisfaction judgment matrix unit, and the commodity attributes include commodity satisfaction; The satisfaction judgment matrix unit uses a sentiment intensity analysis method to calculate the product satisfaction reflected in the review information, and generates a judgment matrix according to the product satisfaction.

3. The SaaS system based on commodity exchange according to claim 2 is characterized in that: The satisfaction judgment matrix unit includes a word set generation subunit and a satisfaction calculation subunit; The word set generation subunit is used to generate a positive satisfactory comment set, a negative satisfactory comment set, a positive untrustworthy comment set, a negative untrustworthy comment set and a general comment set through a sentiment dictionary; The satisfaction calculation subunit is used to classify the comment information by calculating the sentiment intensity analysis method based on the positive goodwill comment set, the negative goodwill comment set, the positive credible comment set, the negative credible comment set and the ordinary comment set, so as to generate a judgment matrix.

4. The SaaS system based on commodity exchange according to claim 3 is characterized in that: The satisfaction judgment matrix unit also includes a sentiment word set updating subunit; The sentiment word set updating subunit is used to determine whether the high-frequency words that are not classified into the positive and kind comment set, the negative and kind comment set, the positive and credible comment set, the negative and credible comment set and the ordinary comment set are emphasis words. If not, they are classified into the positive and kind comment set, the negative and kind comment set, the positive and credible comment set, the negative and credible comment set and the ordinary comment set through cluster analysis.

5. The SaaS system based on commodity exchange according to claim 1 is characterized in that: The judgment matrix generation module further includes an attention judgment matrix unit, and the commodity attributes further include commodity attention; The attention judgment matrix unit calculates the product attention reflected in the comment information by using a word frequency inverse text frequency method, and generates a judgment matrix according to the product attention.

6. The SaaS system based on commodity exchange according to claim 1 is characterized in that: The judgment matrix generation module further includes a practicality judgment matrix unit, and the commodity attributes further include commodity practicality; The utility judgment matrix unit uses a neural network model to calculate the commodity utility reflected in the review information, and generates a judgment matrix according to the commodity utility.

7. The SaaS system based on commodity exchange according to claim 1 is characterized in that: The hierarchical analysis model, whose target layer is the product recommendation score, the criterion layer is the product attributes, and the plan layer is the selected exchange product, is used to calculate the comparison matrix weight and the judgment matrix weight through the comparison matrix and the judgment matrix respectively, and calculate the product recommendation score through the comparison matrix weight and the judgment matrix weight.

8. The SaaS system based on commodity exchange according to claim 1 is characterized in that: The user tendency matrix generation module is used to perform decision tree classification according to the user registration information and the commodity types of the commodities purchased this time to generate the comparison matrix when the user purchases commodities exceeding the quantity threshold; Alternatively, when the user purchases commodities whose quantity is less than or equal to the quantity threshold, a decision tree classification is performed according to the user registration information and the commodity type with the highest single item amount to generate the comparison matrix.

9. The SaaS system based on commodity exchange according to claim 8 is characterized in that: The user registration information includes gender and birth year; The user preference matrix is ​​used to perform decision tree classification into subdivision categories according to the gender, the birth year and the product type, and to generate the comparison matrix according to the correspondence between the subdivision categories and the product attributes.

10. The SaaS system based on commodity exchange according to claim 9 is characterized in that: The module for determining the commodities to be selected for exchange is used to select the commodities to be selected for exchange within a first set amount range according to the highest single item amount when the user purchases commodities exceeding the quantity threshold, and remove the commodities to be selected for exchange with an amount less than the amount corresponding to the accumulated points; Or, when a user purchases goods less than or equal to a quantity threshold, the selected exchange goods within a second set amount range are selected based on the average amount of the goods, and the selected exchange goods with an amount less than the amount corresponding to the accumulated points are removed.

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