01617nas a2200289   4500000000100000000000100001008004100002260001200043653002800055653001300083653001900096653002900115653002200144653002200166653003200188653002500220100002000245700001600265700001600281700002000297245006600317856008000383300001000463490000600473520083400479022001401313       2021                            d  c03/202110aArtificial Intelligence10aFeedback10aNeural Network10aSelf-supervised Learning10aTransfer Learning10aLogical Inference10aNatural Language Generation10aStatistical Learning1 aAllmin Susaiyah1 aAki Härmä1 aEhud Reiter1 aMilan Petković00aNeural Scoring of Logical Inferences from Data using Feedback  uhttps://www.ijimai.org/journal/sites/default/files/2021-03/ijimai_6_5_9.pdf  a90-990 v63 aInsights derived from wearable sensors in smartwatches or sleep trackers can help users in approaching their healthy lifestyle goals. These insights should indicate significant inferences from user behaviour and their generation should adapt automatically to the preferences and goals of the user. In this paper, we propose a neural network model that generates personalised lifestyle insights based on a model of their significance, and feedback from the user. Simulated analysis of our model shows its ability to assign high scores to a) insights with statistically significant behaviour patterns and b) topics related to simple or complex user preferences at any given time. We believe that the proposed neural networks model could be adapted for any application that needs user feedback to score logical inferences from data.  a1989-1660