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

Weinzierl, Sven; Stierle, Matthias; Zilker, Sandra; Matzner, Martin

doi:10.24251/hicss.2020.190

Cited by 13 publications

(27 citation statements)

References 20 publications

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“…To understand customer experience, the customer journey can be analyzed using customer data [67,68]. With process discovery, the executed activities, the duration spent per activity, and the process variants can be analyzed to better understand customer behavior [62,69].…”

Section: Customer Datamentioning

confidence: 99%

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

Zilker¹,

Marx²,

Stierle³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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With the ongoing trend of servitization nurtured through digital technologies, the analysis of services as a starting point for improvement is gaining more and more importance. Service analytics has been defined as a concept to analyze the data generated during service execution to create value for providers and customers. To create more useful insights from the data, there is a continuous need for more advanced solutions for service analytics. One promising technology is process mining which has its origins in business process management. Our work provides insights into how process mining is currently used to analyze service processes and how it could be used along the service process. We find that process mining is increasingly applied for the analysis of the providers' internal operations, but more emphasis should be put on analyzing the customer interaction and experience.

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Section: Customer Datamentioning

confidence: 99%

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

Zilker¹,

Marx²,

Stierle³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Otherwise, valuable information about the context remains hidden in the data attributes, and a DNN learns models with a limited predictive quality since the loss of the potential context information is high. For the next activity prediction, Weinzierl et al [11] confirm a significant relationship between the representation of an event log's context attributes and a DNN's predictive quality. Further, we assume an impact of the representation of an event log's control-flow attributes, especially the activity attributes, on the DNN's predictive quality.…”

Section: Introductionmentioning

confidence: 96%

“…It aims to anticipate future process steps in the course of a running process instance. The task's importance is emphasised, among others, by four analytical uses: (1) Early warning -Monitoring the next most likely activities to be proactively aware of inefficiencies, risks, mistakes [10] or complexities [11]. (2) Best action recommendation -Determining the next best actions based on correctly predicted (next most likely) activities and depending on desired key performance indicators [12].…”

Section: Introductionmentioning

confidence: 99%

“…These architectural types are: (1) multi-layer perceptron (MLP) [17,18], (2) recurrent neural network (RNN) (e.g. [15,19,11]) and (3) convolutional neural network (CNN) [20,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…[26,15]). A standard DNN requires an appropriate encoding of the context attributes to benefit from context information [11,15,27]. Otherwise, valuable information about the context remains hidden in the data attributes, and a DNN learns models with a limited predictive quality since the loss of the potential context information is high.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs

Weinzierl¹,

Zilker²,

Brunk³

et al. 2020

Preprint

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Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially, techniques for the next activity prediction anticipate great potential in improving operational business processes. To gain more accurate predictions, a plethora of these techniques rely on deep neural networks (DNNs) and consider information about the context, in which the process is running. However, an in-depth comparison of such techniques is missing in the PBPM literature, which prevents researchers and practitioners from selecting the best solution for a given event log. To remedy this problem, we empirically evaluate the predictive quality of three promising DNN architectures, combined with five proven encoding techniques and based on five context-enriched real-life event logs. We provide four findings that can support researchers and practitioners in designing novel PBPM techniques for predicting the next activities.

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A survey on recommendation in process mining

Eili

Rezaeenour

2022

Concurrency and Computation

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Recommender systems have been widely applied in several domains to make informed decisions by recommending items that might be of interest. Considering recommendation during business process execution is also highly advantageous as the efficient suggestions about possible activities or resources can impact process performance. However, the deployment of the recommendation frameworks in process mining still needs more investigations to identify the current challenges to enable the practical application of research findings and ensure a large-scale adoption of this technique. Accordingly, a systematic review is conducted to provide a taxonomy of the published studies on process-aware recommender systems based on specified criteria, including the type and perspective of recommendation, a list of datasets and evaluation metrics used in the setting of PARS, implementation environments, and different algorithms used in PARS. In this regard, there are various insights extracted from this study: (i) Most studies in the business process analysis domain are of descriptive and predictive nature, (ii) recommendation in process mining is an emerging research area that is being evolving; the majority of proposals relate to 2015 and after that, and (iii) due to the lack of common evaluation protocol, datasets, and metrics, most studies are validated through experiments and prototyping, with less tendency to the practical implementation of a solution regarding real scenarios.

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

Cited by 13 publications

References 20 publications

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs

A survey on recommendation in process mining

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