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• W4386487254 abstract "With the advent of the era of big data, the information data is exploding. How to dig the hidden value and information matching from the massive information data is a far-reaching research topic. In China's 13th Five-Year Plan, the state has included the analysis and application of big data in the national strategic level, and data is value. Under the background of big data, e-commerce has entered a more diversified information development era, and people's recognition of e-commerce is increasing day by day. Online shopping has gradually become an indispensable part of life. Taobao, JD.COM and other platforms are deeply involved in the C2C field, and their own growth has also changed the traditional commercial marketing mode, thus promoting the sustained and vigorous development of domestic e-commerce business. The progress of the industry has brought great profits and convenience, but the challenges it faces are becoming increasingly severe. The key problems are as follows: First, the problem of data overload. The booming business of e-commerce makes the data of related users and commodities explode, and it is increasingly difficult for e-commerce users to find the required information in time and accurately from the complicated data. Second, the problem of cold start. The new user has no corresponding historical behavior data for the time being, and the platform can't recommend suitable products to him through prediction, that is, the user starts cold. Cold start of goods is the same. The background of e-commerce website can mine users' consumption or browsing records, establish users' consumption preference model, and recommend excellent products to targeted customers with appropriate recommendation technology, thus reducing consumers' time and energy in searching for their real favorite products. Multi-dimensional dynamic recommendation system of mobile e-commerce platform using an improved collaborative filtering algorithm updates the weights of user feature vectors and commodity feature vectors according to the real-time changes of user behavior, and improves the dynamic updating ability of the recommendation system, so as to improve the accuracy of recommendation and the satisfaction of users." @default.
• W4386487254 created "2023-09-07" @default.
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• W4386487254 date "2023-05-01" @default.
• W4386487254 modified "2023-09-27" @default.
• W4386487254 title "Research on Multi-Dimensional Dynamic Recommendation Technology of Mobile E-Commerce Platform Based on Collaborative Filtering Algorithm" @default.
• W4386487254 doi "https://doi.org/10.1109/icnetic59568.2023.00116" @default.
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