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

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dc.contributor.authorWeinzierl, Sven
dc.contributor.authorStierle, Matthias
dc.contributor.authorZilker, Sandra
dc.contributor.authorMatzner, Martin
dc.date.accessioned2020-01-04T07:28:00Z
dc.date.available2020-01-04T07:28:00Z
dc.date.issued2020-01-07
dc.description.abstractSoftware 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.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.190
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63929
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectService Analytics
dc.subjectpredictive process monitoring
dc.subjectprocess mining
dc.subjectrecommender system
dc.subjectweb usage mining
dc.titleA Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs
dc.typeConference Paper
dc.type.dcmiText

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