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Title: An Investigation on Online Versus Batch Learning in Predicting User Behaviour
Authors: Burlutskiy, N
Petridis, M
Fish, A
Chernov, A
Ali, N
Issue Date: 2017
Citation: International Conference on Innovative Techniques and Applications of Artificial Intelligence,pp. 135 - 149, (2017)
Abstract: An investigation on how to produce a fast and accurate prediction of user behaviour on the Web is conducted. First, the problem of predicting user behaviour as a classification task is formulated and then the main problems of such real-time predictions are specified: the accuracy and time complexity of the prediction. Second, a method for comparison of online and batch (offline) algorithms used for user behaviour prediction is proposed. Last, the performance of these algorithms using the data from a popular question and answer platform, Stack Overflow, is empirically explored. It is demonstrated that a simple online learning algorithm outperforms state-of-the-art batch algorithms and performs as well as a deep learning algorithm, Deep Belief Networks. The proposed method for comparison of online and offline algorithms as well as the provided experimental evidence can be used for choosing a machine learning set-up for predicting user behaviour on the Web in scenarios where the accuracy and the time performance are of main concern.
Appears in Collections:Dept of Computer Science Research Papers

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