A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs

dc.contributor.author Weinzierl, Sven
dc.contributor.author Stierle, Matthias
dc.contributor.author Zilker, Sandra
dc.contributor.author Matzner, Martin
dc.date.accessioned 2020-01-04T07:28:00Z
dc.date.available 2020-01-04T07:28:00Z
dc.date.issued 2020-01-07
dc.description.abstract Software companies that offer web-based services instead of local installations can record the user’s interactions with the system from a distance. This data can be analyzed and subsequently improved or extended. A recommender system that guides users through a business process by suggesting next clicks can help to improve user satisfaction, and hence service quality and can reduce support costs. We present a technique for a next click recommender system. Our approach is adapted from the predictive process monitoring domain that is based on long short-term memory (LSTM) neural networks. We compare three different configurations of the LSTM technique: LSTM without context, LSTM with context, and LSTM with embedded context. The technique was evaluated with a real-life data set from a financial software provider. We used a hidden Markov model (HMM) as the baseline. The configuration LSTM with embedded context achieved a significantly higher accuracy and the lowest standard deviation.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.190
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63929
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject predictive process monitoring
dc.subject process mining
dc.subject recommender system
dc.subject web usage mining
dc.title A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs
dc.type Conference Paper
dc.type.dcmi Text
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