Weinzierl, SvenStierle, MatthiasZilker, SandraMatzner, Martin2020-01-042020-01-042020-01-07978-0-9981331-3-3http://hdl.handle.net/10125/63929Software 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.10 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalService Analyticspredictive process monitoringprocess miningrecommender systemweb usage miningConference Paper10.24251/HICSS.2020.190