This paper recapitulate the trend that the perfect interaction between smart phone and human is user-centered, which make the mobile not only a tool, but a emotional partner.From the investigation, we learn what the users regard android mobile as, and understand that the user care only about the functionality to do their work rather than the characteristics of the android. We advance some principles which were based on the discussion to help designer to do user-centered interaction design, and will lead to the user-centered, reassuring, comfortable and consumer-satisfied human-machine interface.Combining with traditional interaction design and the characteristics of interactive manner, the paper put forward some Prediction Technology, by which can be used to design a user-centered system.
Based on real sales data, this article constructed LGBM and LSTM sales prediction models to compare and verify the performance of the proposed models. In this article, we forecast the product sales of stores in the future T + 3 days and use MAPE as the evaluation index. The experiment shows that the product sales prediction model based on the convolutional LSTM (ConvLSTM) network has better prediction accuracy. From a store point of view, ConvLSTM prediction model MAPE was 0.42 lower than the long short-term memory (LSTM) network and 0.68 lower than LGBM. From the perspective of commodity categories, different commodity categories are suitable for different forecasting methods. Some categories are suitable for regression forecasting, while others are suitable for time-series forecasting. Among the categories suitable for time-series forecasting, the ConvLSTM model performs the best.
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