Commodity recommendation method for cross-border e-commerce platform based on data processing technology

Abstract

The invention discloses a commodity recommendation method for a cross-border e-commerce platform based on a data processing technology. The commodity recommendation method specifically comprises the following steps: step 1, collecting and preprocessing data; step 2, establishing a user portrait; step 3, establishing a commodity portrait; 4, designing a recommendation algorithm; 5, constructing a real-time recommendation system; step 6, evaluating and optimizing a recommendation effect; and step 7, front-end display and interactive design. According to the method, the most suitable commodity is pushed by analyzing the user data, and meanwhile, the key attributes of the commodity are extracted according to the recommended commodity, and association rule mining is performed, so that the requirements of customers are met to the greatest extent, and the sales of the commodity is improved.

Description

Technical Field

[0001] The present invention relates to the field of commodity recommendation methods, and in particular to a commodity recommendation method for a cross-border e-commerce platform based on data processing technology. Background Art

[0002] Cross-border e-commerce refers to trading entities in different customs territories concluding transactions and conducting electronic payment settlements through e-commerce platforms, and delivering goods through cross-border e-commerce logistics and remote warehousing, thus completing the entire process of international business activities; the main difference between this form of e-commerce and traditional e-commerce is that it involves international logistics, entry and exit customs clearance, and international settlement; cross-border e-commerce has developed rapidly in recent years and has become an important force in promoting global trade growth.

[0003] Cross-border e-commerce needs to use online platforms when promoting products, so as to display the goods to be sold to overseas customers and attract customers. Therefore, diversified products, accurate product recommendations, and personalized attraction pages are particularly important. They can ensure that customers can find the products they need in the shortest time, thereby promoting the rapid completion of transactions, increasing sales, and promoting economic development. Therefore, a recommendation method is urgently needed to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a product recommendation method for a cross-border e-commerce platform based on data processing technology to solve the problems raised in the above background technology.

[0005] To achieve the above purpose, the present invention provides the following technical solution: a product recommendation method for a cross-border e-commerce platform based on data processing technology, specifically comprising the following steps:

[0006] Step 1: Data collection and preprocessing;

[0007] Use the Internet to collect data, clean and pre-process the data to remove outliers and handle missing data; ensure data standardization and ensure that information from different sources is consistent, making it suitable for subsequent analysis;

[0008] Step 2: Create a user portrait;

[0009] Building user portraits involves statistical analysis of user behavior data to identify characteristics such as user activity, purchasing power level, and category preferences; grouping users to facilitate subsequent targeted recommendations;

[0010] Step 3: Create product portrait;

[0011] The creation of product portraits is used to extract key attributes of products and conduct association rule mining to discover frequently purchased product combinations, which is convenient for increasing the sales of related products and making it easier for users to quickly find the related products they need.

[0012] Step 4: Recommendation algorithm design;

[0013] The design of recommendation algorithms includes collaborative filtering, content recommendation, and hybrid recommendation. Collaborative filtering relies on the similarity of users or products to make recommendations, while content recommendation is based on the matching degree between user profiles and product profiles. Hybrid recommendation attempts to combine these two methods to balance the accuracy and coverage of recommendations.

[0014] Step 5: Real-time recommendation system construction;

[0015] For the construction of real-time recommendation systems, real-time user behavior data is processed; online learning allows the model to be updated instantly, and the elastic scaling of cloud computing resources is used to ensure the stable operation of the system during traffic peaks;

[0016] Step 6: Recommendation effect evaluation and optimization;

[0017] The effectiveness evaluation of the recommendation system includes two aspects: offline evaluation and online ab testing. Offline evaluation analyzes the various indicators of the recommendation algorithm through historical data, while ab testing uses comparative experiments to intuitively observe the actual impact of different recommendation strategies. The feedback loop ensures the continuous improvement of the recommendation system.

[0018] Step 7: Front-end display and interaction design;

[0019] The recommended content is effectively presented to users through front-end display and interactive design; the personalized interface design reflects the characteristics of the user portrait, and the clear interactive design improves user engagement; at the same time, the diversity and novelty of the recommendation system maintains user interest and loyalty.

[0020] Preferably, the data collection in step 1 involves aggregating multi-source information, including user basic information, product details and user behavior data.

[0021] Preferably, step 2 extracts key attributes such as age and gender from user information.

[0022] Preferably, the method of grouping users in step 2 is to use a clustering algorithm k-means.

[0023] Preferably, in step 3, cosine similarity or Jaccard coefficient is used to measure the similarity between products.

[0024] Preferably, in step 5, the stream processing technology Apache Kafka is used to process real-time user data.

[0025] Preferably, the online ab test process in step 6 is to determine the test target, create an experimental version, allocate traffic, collect and analyze data, and apply the optimal version.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] 1. The present invention pushes the most suitable products by analyzing user data. At the same time, it extracts key attributes of the products based on the recommended products and conducts association rule mining, so as to meet customer needs to the greatest extent and thus increase product sales;

[0028] 2. The present invention also uses front-end display and interactive design to effectively present recommended content to users; personalized interface design reflects the characteristics of user portraits, and clear interactive design improves user participation; at the same time, the diversity and novelty of the recommendation system maintains user interest and loyalty. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The present invention is a flowchart of a method for recommending products for a cross-border e-commerce platform based on data processing technology. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] See also Figure 1 The present invention provides a technical solution: a product recommendation method for a cross-border e-commerce platform based on data processing technology, which specifically includes the following steps:

[0032] Step 1: Data collection and preprocessing;

[0033] Use the Internet to collect data, which involves aggregating multi-source information, including basic user information, product details, and user behavior data; clean and pre-process the acquired data to remove outliers and handle missing data; ensure data standardization to ensure that information from different sources is consistent and suitable for subsequent analysis;

[0034] Step 2: Create a user portrait;

[0035] Building user portraits involves statistical analysis of user behavior data to identify characteristics such as user activity, purchasing power level, and category preferences; extracting key attributes from user information, such as age and gender; using clustering algorithms, such as k-means, to group users for subsequent targeted recommendations;

[0036] Step 3: Create product portrait;

[0037] The creation of product portraits is used to extract key attributes of products and conduct association rule mining to find combinations of products that are frequently purchased together, which is convenient for increasing the sales of related products and making it easier for users to quickly find the related products they need. The similarity between products can be measured using methods such as cosine similarity or jaccard coefficient.

[0038] Step 4: Recommendation algorithm design;

[0039] The design of recommendation algorithms includes collaborative filtering, content recommendation, and hybrid recommendation. Collaborative filtering relies on the similarity of users or products to make recommendations, while content recommendation is based on the matching degree between user profiles and product profiles. Hybrid recommendation attempts to combine these two methods to balance the accuracy and coverage of recommendations.

[0040] Step 5: Real-time recommendation system construction;

[0041] For the construction of real-time recommendation systems, stream processing technology (such as Apache Kafka) is used to process real-time user behavior data; online learning allows the model to be updated instantly, and the elastic scaling of cloud computing resources is used to ensure the stable operation of the system during traffic peaks;

[0042] Step 6: Recommendation effect evaluation and optimization;

[0043] The effectiveness evaluation of the recommendation system includes two aspects: offline evaluation and online ab testing. Offline evaluation analyzes the various indicators of the recommendation algorithm through historical data, while ab testing intuitively observes the actual impact of different recommendation strategies through comparative experiments. The feedback loop ensures the continuous improvement of the recommendation system. Among them, the online ab testing process is to determine the test objectives, create experimental versions, allocate traffic, collect and analyze data, and apply the optimal version.

[0044] Step 7: Front-end display and interaction design;

[0045] The recommended content is effectively presented to users through front-end display and interactive design; the personalized interface design reflects the characteristics of the user portrait, and the clear interactive design improves user engagement; at the same time, the diversity and novelty of the recommendation system maintains user interest and loyalty.

[0046] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0047] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A product recommendation method for a cross-border e-commerce platform based on data processing technology, characterized in that: The specific steps include: Step 1: Data collection and preprocessing; Use the Internet to collect data, clean and pre-process the data to remove outliers and handle missing data; ensure data standardization and ensure that information from different sources is consistent, making it suitable for subsequent analysis; Step 2: Create a user portrait; Building user portraits involves statistical analysis of user behavior data to identify characteristics such as user activity, purchasing power level, and category preferences; grouping users to facilitate subsequent targeted recommendations; Step 3: Create product portrait; The creation of product portraits is used to extract key attributes of products and conduct association rule mining to discover frequently purchased product combinations, which is convenient for increasing the sales of related products and making it easier for users to quickly find the related products they need. Step 4: Recommendation algorithm design; The design of recommendation algorithms includes collaborative filtering, content recommendation, and hybrid recommendation. Collaborative filtering relies on the similarity of users or products to make recommendations, while content recommendation is based on the matching degree between user profiles and product profiles. Hybrid recommendation attempts to combine these two methods to balance the accuracy and coverage of recommendations. Step 5: Real-time recommendation system construction; For the construction of real-time recommendation system, process real-time user behavior data; Online learning allows the model to be updated instantly, and the elastic scaling of cloud computing resources is used to ensure the stable operation of the system during traffic peaks; Step 6: Recommendation effect evaluation and optimization; The effectiveness evaluation of the recommendation system includes two aspects: offline evaluation and online ab testing. Offline evaluation analyzes the indicators of the recommendation algorithm through historical data, while ab testing uses comparative experiments to intuitively observe the actual impact of different recommendation strategies. The feedback loop ensures continuous improvement of the recommendation system. Step 7: Front-end display and interaction design; The recommended content is effectively presented to users through front-end display and interactive design; the personalized interface design reflects the characteristics of the user portrait, and the clear interactive design improves user engagement; at the same time, the diversity and novelty of the recommendation system maintains user interest and loyalty.

2. The method for recommending products for a cross-border e-commerce platform based on data processing technology according to claim 1, characterized in that: The data collection in step 1 involves aggregating multi-source information, including user basic information, product details, and user behavior data.

3. The method for recommending products for a cross-border e-commerce platform based on data processing technology according to claim 2, characterized in that: The step 2 extracts key attributes such as age and gender from the user information.

4. The method for recommending products for a cross-border e-commerce platform based on data processing technology according to claim 3, characterized in that: The method for grouping users in step 2 is to use the k-means clustering algorithm.

5. The method for recommending products for a cross-border e-commerce platform based on data processing technology according to claim 4, characterized in that: In step 3, cosine similarity or Jaccard coefficient is used to measure the similarity between products.

6. A method for recommending products for a cross-border e-commerce platform based on data processing technology according to claim 5, characterized in that: In step 5, Apache Kafka, a stream processing technology, is used to process real-time user data.

7. A method for recommending products for a cross-border e-commerce platform based on data processing technology according to claim 6, characterized in that: The online ab testing process in step 6 is to determine the test objectives, create experimental versions, allocate traffic, collect and analyze data, and apply the optimal version.

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