An Investigation on Online Versus Batch Learning in Predicting User Behaviour

Nikolay Burlutskiy, Miltiadis Petridis, Andrew Fish, Alexey Chernov, Nour Ali

    Research output: Chapter in Book/Report/Conference proceedingConference contribution with ISSN or ISBN

    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.
    LanguageEnglish
    Title of host publicationInternational Conference on Innovative Techniques and Applications of Artificial Intelligence
    Place of PublicationCambridge, UK
    Pages135-149
    Number of pages15
    ISBN (Electronic)9783319471754
    DOIs
    StatePublished - 5 Nov 2016
    EventInternational Conference on Innovative Techniques and Applications of Artificial Intelligence - Cambridge, 13-15 December 2016
    Duration: 5 Nov 2016 → …

    Conference

    ConferenceInternational Conference on Innovative Techniques and Applications of Artificial Intelligence
    Period5/11/16 → …

    Fingerprint

    Learning algorithms
    Bayesian networks
    Learning systems
    Deep learning

    Bibliographical note

    The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-47175-4_9

    Keywords

    • Online Learning
    • Deep Learning
    • Classification

    Cite this

    Burlutskiy, N., Petridis, M., Fish, A., Chernov, A., & Ali, N. (2016). An Investigation on Online Versus Batch Learning in Predicting User Behaviour. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 135-149). Cambridge, UK. DOI: 10.1007/978-3-319-47175-4_9
    Burlutskiy, Nikolay ; Petridis, Miltiadis ; Fish, Andrew ; Chernov, Alexey ; Ali, Nour. / An Investigation on Online Versus Batch Learning in Predicting User Behaviour. International Conference on Innovative Techniques and Applications of Artificial Intelligence. Cambridge, UK, 2016. pp. 135-149
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    title = "An Investigation on Online Versus Batch Learning in Predicting User Behaviour",
    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.",
    keywords = "Online Learning, Deep Learning, Classification",
    author = "Nikolay Burlutskiy and Miltiadis Petridis and Andrew Fish and Alexey Chernov and Nour Ali",
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    Burlutskiy, N, Petridis, M, Fish, A, Chernov, A & Ali, N 2016, An Investigation on Online Versus Batch Learning in Predicting User Behaviour. in International Conference on Innovative Techniques and Applications of Artificial Intelligence. Cambridge, UK, pp. 135-149, International Conference on Innovative Techniques and Applications of Artificial Intelligence, 5/11/16. DOI: 10.1007/978-3-319-47175-4_9

    An Investigation on Online Versus Batch Learning in Predicting User Behaviour. / Burlutskiy, Nikolay; Petridis, Miltiadis; Fish, Andrew; Chernov, Alexey; Ali, Nour.

    International Conference on Innovative Techniques and Applications of Artificial Intelligence. Cambridge, UK, 2016. p. 135-149.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution with ISSN or ISBN

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    AU - Petridis,Miltiadis

    AU - Fish,Andrew

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    AU - Ali,Nour

    N1 - The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-47175-4_9

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    N2 - 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.

    AB - 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.

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    Burlutskiy N, Petridis M, Fish A, Chernov A, Ali N. An Investigation on Online Versus Batch Learning in Predicting User Behaviour. In International Conference on Innovative Techniques and Applications of Artificial Intelligence. Cambridge, UK. 2016. p. 135-149. Available from, DOI: 10.1007/978-3-319-47175-4_9