Online shopping mall commodity recommendation system based on cloud computing big data analysis

Abstract

The invention discloses an online shopping mall commodity recommendation system based on cloud computing big data analysis. The online shopping mall commodity recommendation system comprises a user infant basic information acquisition module, a historical shopping order screening module, a historical shopping order classification analysis module, a database, a consumed commodity actual consumptionduration statistics module, an analysis server and a recommendation terminal. According to the invention, the birth date of the baby and all historical shopping orders in the set time period under the user account are obtained by logging in the account by the user, so that the consumption coefficient corresponding to the consumption unit commodity number of the baby age group corresponding to thesame commodity type consumed by each baby is counted; and further, the predicted total consumption duration corresponding to the commodity quantity of the latest shopping order consumed by the same commodity type consumed by each infant is calculated, so that a recommendation time point is obtained, the matching degree with the user demand is improved by the recommendation time point, the defectof low matching degree with the user of the current online shopping mall recommendation time point is overcome, and the user consumption experience is improved.

Description

technical field [0001] The invention belongs to the technical field of commodity recommendation, and relates to an online shopping mall commodity recommendation system based on cloud computing big data analysis. Background technique [0002] With the rapid development of the economy, online shopping is more and more accepted by the public and has become an important channel for people to consume, such as maternal and child products. Mothers often shop online for baby-related products, such as milk powder, diapers, etc. Consumable goods, and in order to improve consumers’ online shopping experience and online shopping willingness, the current online shopping mall will use a personalized recommendation system to recommend products and time-point recommendations to consumers. Based on the number of items purchased in the latest purchase record and the baby’s current age group, the mothers calculate the standard consumption time for the baby’s current age group to consume the nu...

AI Technical Summary

Problems solved by technology

However, this time point calculation method is too idealistic and does not take into account the actual situation of each user's baby. Therefore, the current time point recommended by the online mall does not match the user's high degree of matching, and it is difficult to meet the user's high-matching recommendation needs.

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