Beijing Yojaja Software Technology Development Co., Ltd.

Renfei Shang

Tongyu Dai

Si Chen

Chenyu Tian

In some embodiments, a method determines training data from first nearline features for a first sliding time window and a fixed time window. The first sliding time window changes during a first time period and the fixed time window is static during the first time period. A prediction model is trained using the training data. The prediction model is trained for a second time period in the first time period. The method determines values for second nearline features for a second sliding time window and a fixed time window for a request for a current session that is received during the second time period. The second sliding time window is based on a time for the request. The values are input for the second nearline features into the prediction model to generate a prediction. The method performs an action for the current session based on the prediction.

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Beijing, China

Renfei Shang