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An Investigation on Online Versus Batch Learning in Predicting User Behaviourстатья
- Авторы: Burlutskiy Nikolay, Petridis Miltos, Fish Andrew, Chernov Alexey, Ali Nour
- Сборник: Research and Development in Intelligent Systems XXXIII
- Год издания: 2016
- Место издания: Springer, Cham
- Первая страница: 135
- Последняя страница: 149
- DOI: 10.1007/978-3-319-47175-4_9
- Аннотация: 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.
- Добавил в систему: Коновалов Александр Юрьевич