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• W2920121702 abstract "With the development of WeChat, more and more shopping malls choose WeChat official account to issue preferential information, attract consumers' attention and improve the communication effect. As a reflection of the spread of information in shopping malls, the reading volume of WeChat official account's articles is the goal of this study. Reading volume is affected by many factors. The study discusses two aspects: historical data and the statistical characteristics. Considering the time and space dependence in the articles, this paper proposes a hierarchical Long Short-Term Memory(LSTM) model. The low-level network modeling time dimension features, and the high-level network modeling space dimension feature. The prediction only based on statistical features does not include the historical reading data, it has a good fitting result for the short-term model. The prediction only based on historical data can better predict the long-term growth trend, but loses the influence of the title and other features. In order to solve the limitations of the two methods, this study proposes a hybrid prediction model to predict the reading volume based on historical data and statistical characteristics. Experiments show that the method proposed in this paper has better prediction accuracy." @default.
• W2920121702 created "2019-03-11" @default.
• W2920121702 creator A5028862897 @default.
• W2920121702 creator A5034722146 @default.
• W2920121702 creator A5074641183 @default.
• W2920121702 date "2018-12-08" @default.
• W2920121702 modified "2023-10-16" @default.
• W2920121702 title "A Prediction Model of Wechat Official Account's Article Reading Volume" @default.
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• W2920121702 doi "https://doi.org/10.1145/3297156.3297275" @default.
• W2920121702 hasPublicationYear "2018" @default.
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