%0 Conference Paper %1 10032390 %A Schleicher, Miro %A Pryss, Ruediger %A Schobel, Johannes %A Schlee, Winfried %A Spiliopoulou, Myra %B 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) %D 2022 %K Adherence Recommender Time_series_with_large_gaps mHealth %P 1-10 %R 10.1109/DSAA54385.2022.10032390 %T Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users %U https://ieeexplore.ieee.org/document/10032390/ %X Adherence and phases of non-adherence in the usage of mHealth apps for self-monitoring generate time series characterized by gaps of varying duration. These reduce the knowledge that can be gained from the data because important observations are not captured. In this paper, an approach is presented that allows experts to estimate the possible duration of a user’s absence in order to build on it and take appropriate action.For this purpose, the users’ time series (with gaps and of different lengths) are decomposed into sequences that have no gaps anymore. Each sequence is associated with the duration of the subsequent gap. Using unsupervised binning, these gap values are sorted into a predefined number of categories. Sequences with similar gap durations are thus assigned the same category labels, grouped using unsupervised time series clustering and are assigned with cluster labels. Thus triplets are derived consisting of user ID, cluster label and gap category label. These triplets can then be used for collaborative filtering with matrix factorization. It is now possible to estimate the gap duration even if the same sequence has not yet been observed for the app user.The results show that the quality of binning depends on the appropriate choice of the number of categories, on the technique used, and on the maximum length of the gaps. In this example, a 5-star rating, the Fischer-Jenks algorithm and a maximum gap length of 30 days. We demonstrate how clustering can be used to gradually adapt the time series to the conditions of collaborative filtering and how complex matrix factorization models respond better to the complex structures of the data and lead to better results. In this work, K-Medoids and Agglomerative Clustering obtained applicable results and for matrix factorization SVD++.We believe that our approach provides a valuable back-end tool for experts to better assess the adherence of users to a selfmonitoring app.

@inproceedings{10032390, abstract = {Adherence and phases of non-adherence in the usage of mHealth apps for self-monitoring generate time series characterized by gaps of varying duration. These reduce the knowledge that can be gained from the data because important observations are not captured. In this paper, an approach is presented that allows experts to estimate the possible duration of a user’s absence in order to build on it and take appropriate action.For this purpose, the users’ time series (with gaps and of different lengths) are decomposed into sequences that have no gaps anymore. Each sequence is associated with the duration of the subsequent gap. Using unsupervised binning, these gap values are sorted into a predefined number of categories. Sequences with similar gap durations are thus assigned the same category labels, grouped using unsupervised time series clustering and are assigned with cluster labels. Thus triplets are derived consisting of user ID, cluster label and gap category label. These triplets can then be used for collaborative filtering with matrix factorization. It is now possible to estimate the gap duration even if the same sequence has not yet been observed for the app user.The results show that the quality of binning depends on the appropriate choice of the number of categories, on the technique used, and on the maximum length of the gaps. In this example, a 5-star rating, the Fischer-Jenks algorithm and a maximum gap length of 30 days. We demonstrate how clustering can be used to gradually adapt the time series to the conditions of collaborative filtering and how complex matrix factorization models respond better to the complex structures of the data and lead to better results. In this work, K-Medoids and Agglomerative Clustering obtained applicable results and for matrix factorization SVD++.We believe that our approach provides a valuable back-end tool for experts to better assess the adherence of users to a selfmonitoring app.}, added-at = {2023-02-20T01:52:09.000+0100}, author = {Schleicher, Miro and Pryss, Ruediger and Schobel, Johannes and Schlee, Winfried and Spiliopoulou, Myra}, biburl = {https://www.bibsonomy.org/bibtex/2f7bb364c1e22e446c85157d52d7c6a79/kmd-ovgu}, booktitle = {2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)}, doi = {10.1109/DSAA54385.2022.10032390}, interhash = {3bf81cffb38a41c8c8199458e546521e}, intrahash = {f7bb364c1e22e446c85157d52d7c6a79}, keywords = {Adherence Recommender Time_series_with_large_gaps mHealth}, month = oct, pages = {1-10}, timestamp = {2023-02-20T01:52:09.000+0100}, title = {Expect the gap: A recommender approach to estimate the absenteeism of self-monitoring mHealth app users}, url = {https://ieeexplore.ieee.org/document/10032390/}, year = 2022 }