TW202046211A - System and implementation method thereof for optimizing consumption recommendation information and purchasing decisions - Google Patents

Abstract

The system and implementation method thereof for optimizing consumption recommendation information and purchasing decisions are disclosed. The system is configured to filter out other classification labels related to the label selection instructions based on the label selection instruction of one of the communication devices that have not purchased, so that the communication device can further select from the other classification labels that are filtered out, so as to enhance the efficiency of browsing commodity information, and reduce the flow of communication which is generated between the communication devices and the system. After the history of selecting the classification labels is captured by the system, a potential preference analysis will be performed to provide a consumer recommendation information or a personalized advertisement for the communication device, so as to find out the actual needs of consumers and improve the conversion rate of the website. Further, the system can generate a procurement decision recommendation based on the results of analysis results.

Description

Optimal system of consumption recommendation information and purchasing decision and its implementation method

The present invention is related to the technical fields of cloud computing, data mining (Data Mining), Data Warehouse (data warehousing) and analysis of usage behavior, and particularly refers to a method that can analyze the operation history records of users on e-commerce platforms before consumption to Generate consumer recommendation information that is more in line with the actual needs of users, and provide optimized purchase decision recommendations "Consumer recommendation information and purchase decision optimization system and its implementation method."

With the development of communication technology and the development of the Internet, many users will use computers or smartphones to make purchases on an e-commerce platform, and many merchants will analyze consumers’ consumption history, shopping cart records and products Browse history, and push a consumer recommendation information on the e-commerce platform to increase the purchase rate, or place a dynamic advertisement on the social platform to attract users to the e-commerce platform through the guidance of the social platform, and Add goods to the shopping cart or complete the purchase to increase the website conversion rate of the e-commerce platform.

However, whether e-commerce platforms create consumer recommendation information or create a personalized dynamic advertisement in an advertising system, most of them refer to the analysis results of consumers' consumption history, product page browsing history, and shopping cart records, and only get Know which products (and types) on the platform are more popular or unsalable. In other words, the analysis results considered by the merchants are only in line with consumers who have completed the transaction. As for potential consumers or visitors, it is difficult to understand the potential because they have not completed the consumption. The actual needs of consumers, and at the same time, it is difficult for the purchasing side to evaluate the purchase quantity of different products and their specifications from consumption records alone. Therefore, there are still problems of insufficient stocking or overstocking. Related previous cases such as the invention patent disclosure case of the Republic of China TW200411496 "Target Marketing System and Method", China Invention Patent Publication No. CN108053282A "Combined Information Push Method, Device and Terminal" and other previous cases are disclosed.

Furthermore, merchants usually need to change the settings of the advertising system or adjust the creatives and copywriting to test the effect of advertising, and then evaluate the advertising through advertising conversion rate, return on ad spend (ROAS), and click-through rate (CTR) Effectiveness, in other words, merchants must go through multiple tests of adjusting creatives or copywriting before they can obtain better advertising effects. However, this will make the merchants spend higher advertising costs.

Furthermore, when consumers browse e-commerce platforms, they can usually only classify products from a hierarchical tree structure (for example, if the main category is "fashion women", then "shirts", "shirts", "skirts", etc. Category is a sub-category of "Fashion Women"), to find the products you want to buy, but if users want to find products that meet specific conditions (such as specific colors, contexts, popular elements or origins), they can only One by one from the product pages corresponding to different sub-categories (the product page is a third-tier structure that belongs to the "sub-category"), find out whether there are products that meet their needs. In this way, if consumers cannot find those that meet their needs from the e-commerce platform For the goods they actually need, consumers use information devices to continuously send query requests to the servers of the e-commerce platform. This is an unnecessary waste. At the same time, the servers must frequently fetch data from the database. Will increase the processing burden of the system.

In summary, the current e-commerce system still has the above-mentioned problems to be improved. Based on this, how to provide a product query efficiency that can improve the effectiveness of consumer recommendation information or personalized advertising, and reduce the continuous delivery of information devices to the server The number of query requests sent, the information provision system that can understand the actual or potential needs of consumers, and optimize purchasing decisions are issues to be resolved.

In order to achieve the above objective, the present invention discloses an optimization system for consumption recommendation information and purchase decision, which includes a central processing module, a communication module, a product database, a classification label module, and an information acquisition module , An operation history database and a recommendation module are respectively linked to the central processing module.

Among them, the communication module mainly communicates with a communication device on the consumer or potential consumer; the commodity database can store multiple commodity information, and each commodity information is set to be associated with one or more classification labels; the classification label module can Store multiple classification labels, and also send a label selection command according to the communication device to select one of the classification labels, and extract other classification labels related to the label selection command from other classification labels that have not yet been selected to restrict The communication device can only further select from the extracted other classification tags; the information retrieval module can retrieve one or more tag selection commands sent by the communication device to select one or more classification tags before completing the consumption. And generate operation history information and store it in the operation history database; and the recommendation module can perform a potential preference analysis based on the operation history information to calculate consumption recommendation information for communication devices that have not been consumed, and at the same time analyze the information based on the potential preference As a result, and a purchasing decision parameter stored in the commodity database, a purchasing decision suggestion is generated.

Furthermore, the communication module can be connected to an advertising server, and the advertising server can use a third-party platform (such as a social platform or content aggregation) on a communication device classified into a target audience based on a target audience parameter. The platform) places a personalized advertisement that meets the consumption recommendation information, and the information capture module can also track a conversion event generated by the communication device visiting the e-commerce platform from a third-party platform, and the conversion event is associated with at least one classification label.

In order for your reviewer to have a clear understanding of the purpose, technical features and effects of the present invention after implementation, the following descriptions and illustrations are used for explanation, please refer to them.

Please refer to "Figure 1", which is a system architecture diagram of the present invention. The present invention discloses an optimization system 10 for consumer recommendation information and purchase decision, which itself can be a physical server or in the form of a virtual machine (VM) Run on a physical server, or run on a physical server in the form of a virtual dedicated server (VPS), but not limited to this. The system 10 includes: a central processing module 101, and a communication module 102, Commodity database 103, a classification label module 104, an information acquisition module 105, an operation history database 106 and a recommendation module 107 are respectively linked to the central processing module 101 for information, including: (1) Central processing The module 101 is used to run the optimization system 10 for consumption recommendation information and purchasing decision. It can drive the actions of the above-mentioned modules, and has the functions of logical operation, temporary storage of operation results, and storage of execution instruction positions, and it can be a central processing unit器(CPU). (2) The communication module 102 is used to communicate with one or more communication devices 20, and the communication module 102 may have a transmission interface of one of Wi-Fi, 3G/4G, Ethernet, or a combination thereof. (3) The commodity database 103 may be a database host, which stores multiple pieces of commodity information, and each commodity information is set to be associated with one or more classification tags. (4) The classification label module 104 can store multiple classification labels, and can also use a label selection command sent by the communication device 20 to select one of the classification labels as an index to read the classification labels of the commodity database 103. (5) The information capture module 105 is used to capture one or more tag selection commands sent by the communication device 20 to select one or more of the classification tags before completing consumption, and to generate an operation history information, and Stored in the operation history database 106, and the operation history database 106 can be a database host. (6) The recommendation module 107 is used to perform a potential preference analysis based on the aforementioned operation history information to calculate consumption recommendation information for the communication device 20, and at the same time, based on the results of the potential preference analysis and a purchase decision stored in the commodity database 103 Parameters, generate a purchase decision suggestion. A. The aforementioned potential preference analysis is a collaborative filtering (Collaborative Filtering, CF) algorithm. More specifically, it can be further divided into a user-based collaborative recommendation algorithm (User-based-CF) and a project-based Collaborative recommendation algorithm (Item-based-CF). B. For example, a user-based collaborative recommendation algorithm means that if user A likes the classification labels A, B, and C, and user B likes the classification labels A and B, the recommendation module 107 can infer that B Users may also like classification label C; and the item-based collaborative recommendation algorithm refers to the assumption that user A likes classification labels A, B, and C, user B likes classification labels A and C, and user C likes classification labels A, the recommendation module 107 can infer that the user who selects the classification label A will also want to purchase the products related to the classification label C. C. The aforementioned consumption recommendation information may include a suggested product, a potential preference category, and a potential preference category combination, and the information contained in the consumption recommendation information is associated with at least one category label stored in the category label module 104. D. Among them, the aforementioned purchasing decision parameters may include a purchase quantity trend, an inventory warning information, a commodity purchase price trend, a purchase cost trend, a report information, a sales quantity trend, a shopping cart record information, and The aforementioned categories of potential preferences.

Please refer to "Figure 2", which is a schematic diagram of the system information flow of the present invention. Please also refer to the system implementation flow chart of "Figure 3". The present invention discloses an implementation method of an optimization system for consumption recommendation information and purchasing decisions. It includes the following steps: (1) Setting classification labels ( step S1) : After a classification label module 104 is driven by a central processing module 101, it sets one or more classification labels associated with multiple items of product information, and stores them in one Commodity database 103; (2) Present selectable classification labels on the e-commerce platform ( step S2) : The central processing module 101 can be connected to an e-commerce platform through the communication module 102, and is connected to an e-commerce platform. The front-end interface presents multiple classification tags (T1~T6) for selection. Among them, the e-commerce platform itself can run on a Web Server; (3) Analyze the tag selection command ( step S3) : classification tag model The group 104 uses a label selection command generated by a communication device 20 to select one of the classification labels T1 as an index to read the commodity database 103, and extracts and label selection from other classification labels (T2~T6) that have not yet been selected Instruct other classification tags (T2, T4, T5) having an association relationship to restrict the communication device 20 from being able to further select from other extracted classification tags (T2, T4, T5); more specifically, the classification label module 104. The basis for judging whether the aforementioned classification labels (T1~T6) are related to each other is based on the classification label set for each product information in step S1 to confirm the product information (product A) associated with the selected classification label T1 , In step S1, whether it is set to be associated with other classification tags (T2, T4, T5), that is, as shown in the classification label association diagram in "Figure 4", if so, the communication device 20 is not allowed to be connected to The front-end interface selects irrelevant category tags (T3, T6); (4) Capture operation history ( step S4) : The central processing module 101 drives an information capture module 105 to perform the processing of the communication device 20 before consumption in step S3, one or more classification label (T1 ~ T6) for selecting the one or more transmitted commands for capturing the classification tags, and generates an operation history information, and to store an operation history database 106; ( 5) Perform potential preference analysis ( step S5) : The central processing module 101 drives a recommendation module 107 to perform a potential preference analysis based on the aforementioned operation history information, and calculates consumption recommendation information for the communication device 20, and at the same time according to The result of the potential preference analysis and a purchasing decision parameter stored in the commodity database 103 generate a purchasing decision suggestion.

Please refer to "Figure 5", which is a schematic diagram of the data table of the usage history database of the present invention, and please refer to "Figure 2" and "Figure 4" together. As shown in "Figure 5", the present invention The operation history information stored in the operation history database 106 may include a selection priority, a selection frequency, a selection percentage, and a browsing time generated by the communication device 20 due to the selection of each classification label (T1~T6). Therefore, the system can determine the potential behavior preferences of consumers (members or visitors) based on at least one data table T in the operation history database 106. For example, if the user first selects "Party" and then "Long Top" Category label, but the history database 106 does not record that the user browsed any products, did not add any products to the shopping cart, or did not directly purchase, it may represent the products corresponding to "party" and "long top" All are out of stock or not sold in accordance with the size/specification that the user wants to buy. At the same time, it can also be used with the information about the transfer rate or the inventory amount stored in the product database 103 to understand the purchase on the purchase side Is the amount insufficient.

In continuation, please continue to refer to "Figure 6", which is a schematic diagram of the association of classification labels in another embodiment (1) of the present invention. This embodiment is compatible with the technical categories disclosed in "Figure 1" ~ "Figure 5" Same, the main difference is that the multiple classification labels (T1~T6) stored in the classification label module 104 can be divided into multiple main category labels (M1~M3...) and multiple sub-category labels (T1'~T6') , And each sub-category label (T1'~T6') belongs to at least one of the main category labels (M1~M3...). More specifically, this embodiment performs step S3 as shown in "Figure 3" , The classification label module 104 uses the communication device 20 to select one of the sub-classification labels T1' to generate a label selection command, and extracts the label selection command from other sub-classification labels (T2'~T6') that have not yet been selected Other sub-category tags (T2', T4', T5') that have an association relationship to restrict the communication device 20 from being able to further select from the extracted other sub-category tags (T2', T4', T5'), and this The classification label module 104 of the embodiment has a basis for judging whether the sub-classification labels (T1'~T6') are related to each other, which is similar to the judgment method of step S3, and will not be repeated here; After the embodiment is implemented accordingly, the information capture module 105 can also capture one or more sub-category tags (T1', T2', T4', T5') to which the main category label (M1) belongs when performing step S4. , M3), to help the recommendation module 107, when performing step S5, include the names and usage times corresponding to the main classification tags (M1, M3) into the calculation basis for performing potential preference analysis.

Please refer to "Figure 7", which is a system architecture diagram of another embodiment (2) of the present invention, and please refer to the system implementation flowchart of another embodiment (2) of "Figure 8", this implementation The example is similar to the technology of the embodiment disclosed in "Figure 1" ~ "Figure 5". The main difference is that the communication module 102 can also communicate with an advertising server 30, and after step S5 is executed, it can continue. Perform the following steps: (2) Create dynamic advertisement parameters ( step S6) : After the communication module 102 establishes communication with the advertisement server 30, the advertisement server 30 creates and stores a merchant number, a product catalog, and a corresponding product information. One or more product identification codes, a product link URL, a product image, one or more product classification tags, and a target audience parameter, and a target audience group based on the operation history information of the multiple communication devices 20 is established. A. The aforementioned target audience grouping refers to the operation history information collected by the information acquisition module 105, and the recommendation module 107 calculates an audience similarity between consumers and approximates the audience similarity. The user groups are classified into the same target audience groupings; in addition, the advertising server 30 can also calculate the aforementioned target audience groupings based on the behavior of an application program of multiple communication devices 20 on a social group platform or a content aggregation platform. A member database 108 of the optimization system 10 that reads consumption recommendation information and purchase decision-making can be used to link the member information of the social platform or content aggregation platform with the email, phone number, and serial platform stored in the member database 108 The membership numbers are matched to achieve more accurate placement of personalized advertisements when step S7 is executed. B. The aforementioned target audience parameters may include: viewed products, added to shopping carts, not completed purchases, added or excluded users who have purchased a specific product combination, returned to e-commerce platforms, added or excluded URLs that have visited specific websites Parameters such as keywords, a specific time interval, top ten hot-selling products, spot products, free codes, clearance discounts, etc. C. More specifically, the target audience parameters can be set to "have visited the e-commerce platform in the past 30 days", "has been added to the shopping cart but not completed the purchase", "has viewed a specific product but has not been added to the shopping cart", etc. , But not limited to this. D. The aforementioned advertising server 30 may be an advertising server running a social platform (such as Facebook) or a content aggregation platform (such as Google Ads, Yahoo Gemini, or Bing Ads), but it is not limited to this. E. The information acquisition module 105 of this step must embed an event tracking code (Event Tracking Code) with a custom parameter attached, so that the advertisement server 30 can directly receive the association after the collection of the operation history information is completed Regarding the operation history information of the event tracking code, if the advertising server 30 is an ad server on the Facebook platform as an example, the aforementioned event tracking code can be a base program adapted to a Facebook Pixel supported by the Facebook platform Code (Facebook Pixel Code), the event tracking code must be embedded in the website code of one or more website pages (product link URLs) to be tracked, but they are not limited to this. F. In this embodiment, in a better case, the information acquisition module 105 in this step can further embed a global site tag to track the URL of the webpage visited by the user after visiting the e-commerce platform And the headline, so that the advertisement server 30 can use this information to establish the aforementioned target audience segment. (3) Placing personalized advertisements on third-party platforms ( step S7) : According to the target audience parameters, the advertisement server 30 browses an e-commerce platform, a social group platform, or a content aggregation platform, and is classified as a target audience group. The communication device 20 places a personalized advertisement on a third-party platform (such as a social networking platform or a content aggregation platform) that meets consumer recommendation information, and is aimed at the communication device 20 that browses an e-commerce platform or a social networking platform or a content aggregation platform. The information acquisition module 105 can also track a conversion event generated by the communication device 20 visiting an e-commerce platform through a social platform or a content aggregation platform, and the conversion event is associated with at least one classification label (T1~T6). (4) Among them, the aforementioned conversion event can refer to the event that the communication device 20 visits the e-commerce platform through the guidance of the personalized advertisement placed on the third-party platform, or the event that the purchase is completed after visiting the e-commerce platform, or the visit to the e-commerce platform. The event of adding to the shopping cart after the business platform, or the event of completing member registration after visiting the e-commerce platform, or the event of browsing specific product links after visiting the e-commerce platform, but not limited to this.

In conclusion, after this embodiment is implemented, personalized advertisements can be placed on the social platform (such as Facebook’s dynamic wall) or content aggregation platform (such as presented on the Google search engine or Google’s partners) that the communication device 20 browses. Platform), in order to achieve the purpose of remarketing advertising based on pre-consumption behavior analysis, and to alleviate the problem that the conventional merchants can only adjust the advertising material or copy several times and spend high advertising costs. Increase conversion rate (CVR) and reduce consumer acquisition cost (CAC).

Please refer to "Figure 9", which is a system architecture diagram of another embodiment (3) of the present invention. As shown in the figure, this embodiment and "Figure 1" ~ "Figure 5" or "Figure 7" ~ The technology of the embodiment disclosed in "Figure 8" is similar, the main difference is that the optimization system 10 for consumption recommendation information and purchase decision of this embodiment may further include a consumption history data linked to the central processing module 101. The library 109 stores a consumption history information D5 of a plurality of communication devices 20 that have completed consumption. According to this, in this embodiment, the information is retrieved after step S3 in "Figure 3" or "Figure 8" is completed The module 105 can first retrieve the cancellation fee history information D5 from the consumption history database 109, and use it as a calculation basis for the recommendation module 107 to perform potential preference analysis when step S5 is executed. According to this, the present embodiment can use The previous operating behavior and the consumption history of the user who completed the consumption are taken into consideration in the analysis of potential preferences.

In summary, after the present invention is implemented, it can at least help consumers browse and find suitable product information on e-commerce platforms more efficiently, and reduce the number of continuous query requests sent by the communication device to the server. Beneficial effects, it can also understand the actual or potential needs of consumers, and provide consumers with consumer recommendation information that meets their needs, or allow consumers to guide consumers to meet their needs through personalized advertisements placed on third-party platforms The product page of the e-commerce platform can also achieve the beneficial effects of optimizing purchasing decisions.

The above are only the preferred embodiments of the present invention, and are not intended to limit the scope of the present invention; anyone who is familiar with this technique can make equal changes and modifications without departing from the spirit and scope of the present invention. Should be covered in the scope of the patent of the present invention.

To sum up, the present invention has patent requirements such as "industrial applicability", "novelty" and "progressiveness"; the applicant filed an application for a patent for invention with the Bureau of Patent in accordance with the provisions of the Patent Law.

10: Optimization system for consumer recommendation information and purchasing decision 101: Central Processing Module 102: Communication module 103: Commodity Database 104: Classification label module T1~T6: Classification label M1~M3: Main category label T1’~T6’: Sub-category label 105: Information Capture Module 106: Operation history database T: data sheet 107: recommended module 108: Member Database 109: Consumption History Database D5: Consumption history information 20: Communication device 30: Advertising server D1: Available classification labels D2: Label selection command D3: Other classification labels associated with the selected label D4: Consumer recommendation information generated based on pre-consumption behavior D4’: Consumption recommendation information generated by incorporating consumption history S1: Set classification label S2: Present available classification labels on the e-commerce platform S3: Analysis tag selection command S4: Capture operation history S5: Perform potential preference analysis S6: Establish dynamic advertising parameters S7: Put personalized ads on third-party platforms

Figure 1 is a system architecture diagram of the present invention. Figure 2 is a schematic diagram of the system information flow of the present invention. Figure 3 is a flowchart of the system implementation of the present invention. Figure 4 is a schematic diagram of the classification label association of the present invention. Figure 5 is a schematic diagram of the data table of the usage history database of the present invention. Figure 6 is a schematic diagram of classification label association according to another embodiment (1) of the present invention. Figure 7 is a system architecture diagram of another embodiment (2) of the present invention. Figure 8 is a system implementation flowchart of another embodiment (2) of the present invention. Figure 9 is a schematic diagram of the system architecture and information flow of another embodiment (3) of the present invention.

10: Optimization system for consumer recommendation information and purchasing decision

101: Central Processing Module

102: Communication module

103: Commodity Database

104: Classification label module

105: Information Capture Module

106: Operation history database

107: recommended module

20: Communication device

D1: Available classification labels

D2: Label selection command

D3: Other classification labels associated with the selected label

D4: Consumer recommendation information generated based on pre-consumption behavior

Claims (10)

A system for optimizing consumption recommendation information and purchasing decisions for generating a consumption recommendation information corresponding to a communication device, the system includes: A central processing module, and a communication module, a product database, a classification label module, an information retrieval module, an operation history database and a recommendation module are respectively linked to the central processing module ； The communication module is used to communicate with the communication device; The product database stores multiple pieces of product information, and each of the product information is set to be associated with one or more classification labels; The classification label module is used for storing a plurality of the classification labels, and also for a label selection command sent according to the communication device to select one of the classification labels, and extract the same from the other classification labels that have not yet been selected. The label selection instruction has the associated other classification labels, so as to restrict the communication device to only further select other extracted classification labels; The information retrieval module is used to retrieve one or more tag selection commands sent by the communication device to select one or more of the classification tags before completing consumption, and to generate an operation history information, and store it in The operating history database; and The recommendation module is used to perform a potential preference analysis based on the operation history information to calculate the consumption recommendation information for the communication device, and at the same time based on the result of the potential preference analysis and a purchase decision parameter stored in the product database , Generate a purchase decision suggestion. For example, the optimization system for consumer recommendation information and purchase decision-making in the first item of the patent application, wherein the operation history information is a selection priority order, a selection frequency, a selection frequency percentage, and a browse for each classification label of the communication device One or a combination of time, and the operation history information further includes a number of views of the product information. For example, the optimization system for consumer recommendation information and purchase decision-making in the first item of the scope of patent application, in which the communication module is also used to communicate with an advertising server, which is used to store a product catalog, one or A plurality of product identification codes and a target audience parameter also store a target audience segment generated based on the operation history information of the plurality of communication devices. For example, the optimization system for consumer recommendation information and purchase decision-making in item 3 of the scope of patent application, in which the advertising server also provides for browsing an e-commerce platform, a social platform or a content aggregation platform based on the target audience parameters. And the communication device classified into the target audience group will place a personalized advertisement that meets the consumption recommendation information. For example, the optimization system for consumer recommendation information and purchase decision in the fourth item of the patent application, wherein the information acquisition module is also used to track a conversion event generated by the communication device visiting the e-commerce platform through a third-party platform, The information acquisition module is embedded with an event tracking program code attached with a custom parameter, and the conversion event is associated with at least one classification label. An implementation method of an optimization system for consumer recommendation information and purchase decision-making, including: A step of setting a classification label: after a classification label module is driven by a central processing module, one or more classification labels associated with multiple product information are set and stored in a product database; An analysis label selection instruction step: the classification label module uses a communication device to select one of the classification labels to generate a label selection command as an index to read the product database, from other classification labels that have not yet been selected , Extract other classification tags that have an association relationship with the tag selection instruction, so as to restrict the communication device to only further select from other extracted classification tags; A step of capturing operation history: The central processing module drives an information capturing module to select one or more of the classification labels in the analysis label selection instruction step before the completion of the consumption of the communication device. One or more of the tag selection commands sent are retrieved, and an operation history information is generated, and stored in the operation history database; and A step of performing potential preference analysis: the central processing module drives a recommendation module to perform a potential preference analysis based on the operation history information, and calculates consumption recommendation information for the communication device, and at the same time based on the result of the potential preference analysis , And a purchasing decision parameter stored in the commodity database to generate a purchasing decision suggestion. For example, the implementation method of the optimization system for consumer recommendation information and purchase decision in the scope of patent application, wherein when the step of extracting the operation history is executed, the operation history information is a priority for the communication device to select each sub-category label One or a combination of sequence, a number of selections, a percentage of the number of selections, a browsing time, and the operation history information further includes a browsing count of the product information. For example, the implementation method of the optimization system for consumer recommendation information and purchase decision in the scope of patent application, wherein, after the execution of the potential preference analysis step is executed, the step of establishing dynamic advertisement parameters is successively executed: a communication module and an advertisement server After the communication device is established, a product catalog, one or more product identification codes and a target audience parameter corresponding to the product information are created on the advertising server, and a target audience based on the operation history information of multiple communication devices is created Grouping. For example, the implementation method of the optimization system for consumer recommendation information and purchasing decision of item 8 of the scope of patent application, wherein after the step of establishing dynamic advertisement parameters is executed, a step of placing personalized advertisements is continued: the advertisement server is based on the target audience Parameter, for the communication device that has browsed an e-commerce platform, a social platform or a content aggregation platform and is classified into the target audience grouping, a personalized advertisement that meets the consumption recommendation information is placed on a third-party platform. For example, the implementation method of the optimization system for consumer recommendation information and purchase decision in the 6th or 9th item of the patent application, wherein, after the analysis tag selection instruction step is executed, the information acquisition module first obtains a consumption history database The consumption history information of the plurality of communication devices that have completed the consumption is retrieved, and used as a calculation basis for the recommendation module to generate the consumption recommendation information and the purchase decision recommendation when the execution potential preference analysis step is executed.

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