E-commerce platform flow pushing method and system for directional display of shopping cart commodities

Abstract

The invention discloses an e-commerce platform flow pushing method and system for shopping cart commodity directional display, and the system comprises a basic information acquisition unit, a data analysis unit and a flow pushing information output unit, and relates to the technical field of e-commerce platform flow pushing, and solves the problem that incomplete data analysis cannot fit the actual condition of a user, and the user experience is improved. In order to solve the technical problems of poor user experience due to the fact that a user purchases commodities according to the purchase record of the user, the method performs classification processing on the purchased commodities according to the purchase record of the user, analyzes whether the user purchases the commodities by active search or flow pushing in the classification processing process, and performs different analyses according to different conditions, so that the user experience is improved. According to the method, the corresponding stream pushing information is generated, meanwhile, the commodities collected by the user are analyzed in the analysis process, whether collection records exist or not is judged, and reasonable stream pushing is carried out in combination with the real-time information of similar products, so that the actual situation of the user can be fit, and the experience feeling of the user is improved; and meanwhile, the commodity marketing effect brought by the plug flow can also be improved.

Description

Technical Field

[0001] This invention relates to the field of e-commerce platform promotion technology, specifically to an e-commerce platform promotion method and system for targeted display of goods in shopping carts. Background Technology

[0002] Big data analytics has rapidly become an integral part of data-driven business, playing a vital role in the operations of brands and enterprises. The e-commerce industry, in particular, needs data analytics, especially insights into its audience, its users' online behavior, and their personal preferences.

[0003] According to patent application number CN202111597448.2, this patent includes a popularity calculation module, a popularity penalty weight calculation module, a product similarity calculation module, a user interest decay value calculation module, a product rating prediction value calculation module, and a product recommendation module. The method and system described in this invention solve the problem of high similarity among popular products. Simultaneously, based on each user's behavioral characteristics, a time function is added to the prediction formula to reduce the contribution of historical data to prediction preferences. While improving recommendation accuracy, it effectively mines and recommends less popular products in the dataset, increasing recommendation coverage, alleviating the "long tail effect" problem of recommendation systems, and improving the quality of product recommendations.

[0004] Some existing e-commerce platforms push products to users based on their purchase history. This method has limitations. Pushing products solely based on purchase history cannot be tailored to the user's actual situation. Incomplete data analysis leads to errors in the push information, resulting in a poor user experience and failing to achieve the desired effect of appropriate product promotion. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an e-commerce platform push method and system for targeted display of goods in shopping carts, solving the problem that incomplete data analysis cannot match the actual situation of users, thus causing a poor user experience.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an e-commerce platform push system for targeted display of goods in a shopping cart, comprising: The basic information acquisition unit is used to acquire basic information of the target object and transmit it to the data analysis unit. The target object is: user purchase records, and the basic information includes: product type and number of views. The data analysis unit is used to acquire and analyze the basic information of the transmitted target object. It generates corresponding purchase category information by classifying the target object, including recommended purchase information and search purchase information. The recommended purchase information is transmitted to the automatic streaming analysis unit, and the search purchase information is transmitted to the search streaming analysis unit. The specific method for generating purchase category information is as follows: Once the target object is obtained, the purchase method of its products is determined. If the product is found and purchased by the user through search, the corresponding purchase record is recorded as a search purchase and corresponding search purchase information is generated. If the product is recommended by the system and purchased, the corresponding purchase record is recorded as a recommended purchase and corresponding recommended purchase information is generated. The search streaming analysis unit is used to acquire and analyze the transmitted search and purchase information. By acquiring the basic data corresponding to the search and purchase information and simultaneously acquiring the stored information transmitted by the information storage unit, the unit analyzes the search and purchase information to generate corresponding streaming information. The streaming information is then transmitted to the streaming information output unit. The method for generating streaming information is as follows: S1: Obtain the search and purchase information and the purchased products, and classify the purchased products to generate corresponding purchase product category information denoted as i, where i = 1, 2, ..., n, and i represents the product category number; S2: Next, the stored information is obtained and the purchased product category information is matched with it to generate corresponding matching information. The matching information is then compared with the system's database to generate corresponding similar information. Then, the number of purchases corresponding to the similar information is obtained and sorted from largest to smallest to generate similar information sorting results. S3: Obtain the sorting results of similar information and their corresponding purchase counts, and generate corresponding push information according to the order from largest to smallest; The automatic streaming analysis unit acquires and analyzes the transmitted recommended purchase information. It categorizes the recommended purchase information and analyzes it based on its basic purchase data to obtain corresponding streaming information. Simultaneously, it transmits the streaming information to the streaming information output unit. The method for generating streaming information is as follows: P1: Obtain recommended purchase information and classify it according to product type to generate product category information. At the same time, obtain the number of push notifications corresponding to the product category information and record it as C, where C = 1, 2, ..., m. Then classify the number of push notifications to generate the number of views A and the number of purchases B, where A = 1, 2, ..., o, B = 1, 2, ..., p, o + p = m. P2: Next, we analyze the number of purchases B, obtain the product corresponding to the number of purchases B, and determine whether the product is favorited. If the product is favorited, then the product is recorded as a favorited product; otherwise, it is recorded as a normal product. P3: Retrieve all favorited items and label them. At the same time, retrieve the real-time information of the corresponding favorited items from the system and generate corresponding push notification information. It should be noted that the real-time information of the corresponding favorited items from the system means that it will be pushed to the user in a timely manner when the item or similar items are on promotion.

[0007] P4: Obtain all normal products and label them as k, where k = 1, 2, ..., h. Then, obtain the purchase time corresponding to normal product k and label it as Tk. At the same time, obtain the number of times the user purchased the normal product and label it as Rk. Then, substitute Tk and Rk into the formula. The flow value Qk corresponding to the normal product k is calculated, where All are preset scaling coefficients, and the obtained push stream values ​​Qk are sorted from largest to smallest to generate corresponding push stream information; The streaming information output unit is used to acquire the transmitted streaming information and display it to the operator through a display device. Beneficial effects

[0008] This invention provides a method and system for targeted display of goods in shopping carts on e-commerce platforms. Compared with existing technologies, it has the following advantages: This invention categorizes purchased goods based on a user's purchase history. During the categorization process, it analyzes whether the purchase was driven by active search or by a push notification, performing different analyses for each case to generate corresponding push notification information. Simultaneously, it analyzes the user's favorite items to determine if there are any existing favorites and combines this with real-time information on similar products to optimize push notifications. This approach better aligns with the user's actual situation, enhancing the user experience and improving the marketing effectiveness of the push notifications. Attached Figure Description

[0009] Figure 1 This is a system flowchart of the present invention. Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] Please see Figure 1 This application provides an e-commerce platform push system for targeted display of products in shopping carts, comprising: The basic information acquisition unit is used to acquire basic information about the target object and transmit it to the data analysis unit. The target object is the user's purchase records, and the basic information includes product type and number of views. It should be noted that the user's purchase records refer to all purchase records made by that user within time T1, and the specific value of time T1 is set by the operator.

[0012] The data analysis unit is used to acquire and analyze the basic information of the transmitted target object. It generates corresponding purchase category information by classifying the target object, including recommended purchase information and search purchase information. The recommended purchase information is transmitted to the automatic push stream analysis unit, and the search purchase information is transmitted to the search push stream analysis unit. The specific method for generating purchase category information is as follows: The system retrieves the target object and determines the purchase method of its products. If the product was found and purchased by the user through search, the corresponding purchase record is recorded as a search purchase, and corresponding search purchase information is generated. If the product was recommended by the system and purchased, the corresponding purchase record is recorded as a recommended purchase, and corresponding recommended purchase information is generated. It should be noted that when a user purchases a product, there are two types: one is a purposeful search purchase, which is marked as a search purchase; the other is a purchase made after being recommended by the system, which is marked as a recommended purchase.

[0013] The search streaming analysis unit is used to acquire and analyze the transmitted search and purchase information. By acquiring the basic data corresponding to the search and purchase information and simultaneously acquiring the stored information transmitted by the information storage unit, the unit analyzes the search and purchase information to generate corresponding streaming information. The streaming information is then transmitted to the streaming information output unit. The specific method for generating the streaming information is as follows: S1: Obtain the search and purchase information and the purchased products, and classify the purchased products to generate corresponding purchase product category information denoted as i, where i = 1, 2, ..., n, and i represents the product category number; S2: Next, the stored information is obtained and the purchased product category information is matched with it to generate corresponding matching information. The matching information is then compared with the system's database to generate corresponding similar information. Then, the number of purchases corresponding to the similar information is obtained and sorted from largest to smallest to generate similar information sorting results. S3: Obtain the sorting results of similar information and their corresponding purchase counts, and generate corresponding push notifications based on these counts from largest to smallest. It should be noted that matching the purchased product category information means filtering products of the same type, integrating the filtered product results to generate corresponding matching information, further filtering the corresponding complementary product information based on the filtered products, and finally generating push notifications.

[0014] Based on the analysis of actual situations, if a user purchases car interior air fresheners, the push notifications will integrate related car interior products. Finally, the push notifications will be generated based on the total number of purchases. In real life, when an applicant purchases car-related products, such as car cleaning towels on an e-commerce platform (the cleaning towels were found through the applicant's own search), the system will automatically categorize the purchased products and subsequently push related car products, such as license plate numbers, antifreeze, and so on.

[0015] The automatic streaming analysis unit is used to acquire and analyze the transmitted recommended purchase information. It categorizes the recommended purchase information and analyzes it based on its basic purchase data to obtain corresponding streaming information. Simultaneously, it transmits the streaming information to the streaming information output unit. The specific method for generating streaming information is as follows: P1: Obtain recommended purchase information and classify it according to product type to generate product category information. At the same time, obtain the number of push notifications corresponding to the product category information and record it as C, where C = 1, 2, ..., m. Then classify the number of push notifications to generate the number of views A and the number of purchases B, where A = 1, 2, ..., o, B = 1, 2, ..., p, o + p = m. P2: Next, we analyze the number of purchases B, obtain the product corresponding to the number of purchases B, and determine whether the product is favorited. If the product is favorited, then the product is recorded as a favorited product; otherwise, it is recorded as a normal product. P3: Retrieve all favorited items and label them. At the same time, retrieve the real-time information of the corresponding favorited items from the system and generate corresponding push notification information. It should be noted that the real-time information of the corresponding favorited items from the system means that it will be pushed to the user in a timely manner when the item or similar items are on promotion.

[0016] P4: Obtain all normal products and label them as k, where k = 1, 2, ..., h. Then, obtain the purchase time corresponding to normal product k and label it as Tk. At the same time, obtain the number of times the user purchased the normal product and label it as Rk. Then, substitute Tk and Rk into the formula. The flow value Qk corresponding to the normal product k is calculated, where All are preset scaling factors, and the obtained push stream values ​​Qk are sorted from largest to smallest to generate corresponding push stream information.

[0017] Based on the analysis of practical application scenarios, the purchase time value corresponding to a normal product k is 20 days. Here, 20 days represents the length of time from the time the order is placed to the current time. The number of purchases is 3. Then, substituting the values ​​20 and 3 into the formula above, where... After substituting into the formula, the calculated flow rate is... The push stream value Qk = 0.1343 is calculated in this way to obtain different push stream values, and then the push stream is generated in descending order.

[0018] The streaming information output unit is used to acquire the transmitted streaming information and display it to the operator through a display device.

[0019] A method for targeted display of products in shopping carts on e-commerce platforms, the method specifically includes the following steps: Step 1: Obtain the user's purchase records and classify the corresponding products based on the purchase records to obtain the classification results, which include recommended purchase information and search purchase information; Step 2: Next, the search and purchase information is analyzed. This involves classifying the purchased products corresponding to the search and purchase information to obtain category information, and then matching the category information with the system's database to obtain matching information. Step 3: Then, the matching information is compared with the system's database to generate corresponding similar information. At the same time, the similar information is sorted from largest to smallest according to the number of purchases to generate push information. Step 4: Next, obtain recommended purchase information and classify the products according to product type to obtain category information. Then, classify the obtained push traffic to obtain the number of views and the number of purchases. Step 5: Finally, analyze the products corresponding to the number of purchases. This involves determining whether the products are favorited. For products that are favorited, obtain real-time information and generate corresponding push notification information. Step 6: For products that do not have any favorites, calculate their promotion value based on the number of purchases and the purchase time, and generate promotion information by sorting them from largest to smallest promotion value.

[0020] Furthermore, any content not described in detail in this specification is existing technology known to those skilled in the art.

[0021] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An e-commerce platform push system for targeted display of goods in shopping carts, characterized in that, include: The basic information acquisition unit is used to acquire basic information of the target object and transmit it to the data analysis unit. The target object is: user purchase records, and the basic information includes: product type and number of views. The data analysis unit is used to acquire and analyze the basic information of the target object being transmitted. It generates corresponding purchase category information by classifying the target object, including recommended purchase information and search purchase information. The recommended purchase information is then transmitted to the automatic push stream analysis unit, and the search purchase information is transmitted to the search push stream analysis unit. The search and push stream analysis unit is used to acquire and analyze the transmitted search and purchase information. By acquiring the basic data corresponding to the search and purchase information and the storage information transmitted by the information storage unit, the unit analyzes the search and purchase information to generate corresponding push stream information and transmits the push stream information to the push stream information output unit. The automatic push stream analysis unit is used to acquire and analyze the transmitted recommended purchase information. By classifying the recommended purchase information and analyzing it according to its purchase basic data, the corresponding push stream information is obtained, and the push stream information is transmitted to the push stream information output unit. The streaming information output unit is used to acquire the transmitted streaming information and display it to the operator through a display device.

2. The e-commerce platform push system for targeted display of goods in a shopping cart according to claim 1, characterized in that, The specific method by which the data analysis unit generates purchase category information is as follows: Once the target object is obtained, the purchase method of its products is determined. If the product was found and purchased by the user through search, the corresponding purchase record is recorded as a search purchase and corresponding search purchase information is generated. If the product was recommended by the system and purchased, the corresponding purchase record is recorded as a recommended purchase and corresponding recommended purchase information is generated.

3. The e-commerce platform push system for targeted display of goods in a shopping cart according to claim 1, characterized in that, The search and push analysis unit generates push information in the following way: S1: Obtain the search and purchase information and the purchased products, and classify the purchased products to generate corresponding purchase product category information denoted as i, where i = 1, 2, ..., n, and i represents the product category number; S2: Next, the stored information is obtained and the purchased product category information is matched with it to generate corresponding matching information. The matching information is then compared with the system's database to generate corresponding similar information. Then, the number of purchases corresponding to the similar information is obtained and sorted from largest to smallest to generate similar information sorting results. S3: Obtain the sorting results of similar information and their corresponding purchase counts, and generate corresponding push information according to the order from largest to smallest.

4. The e-commerce platform push system for targeted display of goods in a shopping cart according to claim 1, characterized in that, The automatic streaming analysis unit generates streaming information in the following way: P1: Obtain recommended purchase information and classify it according to product type to generate product category information. At the same time, obtain the number of push notifications corresponding to the product category information and record it as C, where C = 1, 2, ..., m. Then classify the number of push notifications to generate the number of views A and the number of purchases B, where A = 1, 2, ..., o, B = 1, 2, ..., p, o + p = m. P2: Next, we analyze the number of purchases B, obtain the product corresponding to the number of purchases B, and determine whether the product is favorited. If the product is favorited, then the product is recorded as a favorited product; otherwise, it is recorded as a normal product. P3: Obtain all the collected items and label them. At the same time, obtain the real-time information of the corresponding collected items from the system and generate the corresponding push information. P4: Obtain all normal products and label them as k, where k = 1, 2, ..., h. Then, obtain the purchase time corresponding to normal product k and label it as Tk. At the same time, obtain the number of times the user purchased the normal product and label it as Rk. Then, substitute Tk and Rk into the formula. The flow value Qk corresponding to the normal product k is calculated, where All are preset scaling factors, and the obtained push stream values ​​Qk are sorted from largest to smallest to generate corresponding push stream information.

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