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.
Original language | English |
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Title of host publication | International Conference on Innovative Techniques and Applications of Artificial Intelligence |
Place of Publication | Cambridge, UK |
Publisher | Springer International Publishing |
Pages | 135-149 |
Number of pages | 15 |
ISBN (Electronic) | 9783319471754 |
ISBN (Print) | 9783319471747 |
DOIs | |
Publication status | Published - 5 Nov 2016 |
Event | International Conference on Innovative Techniques and Applications of Artificial Intelligence - Cambridge, 13-15 December 2016 Duration: 5 Nov 2016 → … |
Conference
Conference | International Conference on Innovative Techniques and Applications of Artificial Intelligence |
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Period | 5/11/16 → … |
Bibliographical note
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-47175-4_9Keywords
- Online Learning
- Deep Learning
- Classification
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Alexey Chernov
- School of Arch, Tech and Eng - Senior Lecturer
- Computing and Mathematical Sciences Research Excellence Group
Person: Academic