A survey for user behavior analysis based on machine learning techniques: current models and applications

Significant research has been carried out in the field of User Behavior Analysis, focused on understanding, modeling and predicting past, present and future behaviors of users. However, the heterogeneity of the approaches makes their comprehension very complicated. Thus, domain and Machine Learning experts have to work together to achieve their objectives. The main motivation for this work is to obtain an understanding of this field by providing a categorization of state-of-the-art works grouping them based on specific features. This paper presents a comprehensive survey of the existing literature in the areas of Cybersecurity, Networks, Safety and Health, and Service Delivery Improvement. The survey is organized based on four different topic-based features which categorize existing works: keywords, application domain, Machine Learning algorithm, and data type. This paper aims to thoroughly analyze the existing references, to promote the dissemination of state-of-the-art approaches discussing their strong and weak points, and to identify open challenges and prospective future research directions. In addition, 127 discussed papers have been scored and ranked according to relevance-based features: paper reputation, maximum author reputation, novelty, innovation and data quality. Both types of features, topic-based and relevance-based have been combined to build a similarity metric enabling a rich visualization of all considered publications. The obtained graphic representation provides a guide of recent advancements in User Behavior Analysis by topic, highlighting the most relevant ones.