Consumer Behavior Based on Big Data and Cloud Computing and Construction of Customer Perception Mobile Terminal System

Abstract

The arrival of the era of big data has realized the transformation of people’s production and lifestyle. At the same time, it also increases people’s desire to consume, and the feedback behavior of consumers’ comments and ratings is the feedback of users’ experience in merchants’ products, that is, the matching of products to consumer needs and preferences. When the product can reach the user’s satisfaction level, the customer-aware mobile terminal system is constructed and optimized by using the advanced methods and technologies of big data information display and the principles and laws of the collaborative filtering algorithm in cloud computing. It ensures the ecological development of the consumer industry. Among them, in the experimental evaluation of the collaborative filtering recommendation algorithm, the mean absolute error (MAE) and root mean square error (RMSE) values of the SVD++ algorithm are higher than those of the other three algorithm models, indicating that other algorithm models can effectively improve the accuracy of the recommendation algorithm. A cross-sectional comparative analysis of experimental results has shown that, as the number of neighbors increased, the MAE and RMSE values first decreased and then increased. When the number of neighbors N is 25, the MAE and RMSE reach the minimum value, so the optimal number of neighbors is 25. Therefore, it is very important to use the collaborative filtering algorithm to analyze and construct the consumer behavior and customer perception mobile terminal system.