## 5 Conclusions, Discussion, and Future Directions  
In this work, a study about the cold-start problem in mobile advertisement systems has been conducted and the issue of the 'frozen user problem' is discussed. A novel prediction approach to design personalized advertisement systems that can deal with this problem is proposed. The main steps are as follows:  
1. Use device and application-related variables that could be gathered from a new user. 2. Train and test state-of-the-art machine learning models in existing datasets. 3. Implement a client-server architecture that gets user data and predicts his/her interest for the advertisement using a deep learning ensembler. Figure 5: Implementation–development step. A validation and an implementation of this approach has been done. Beginning with the validation, experiments have been conducted in real world datasets with 1 million users to test the performance of deep learning models for a frozen user (when limited attributes are available). The results showed that deep learning techniques (and the ensembler model) are really close to the performance that the models achieve with all the attributes available and, therefore, they can work very well. State-of-the-art deep learning models achieve high performance even with limited input variables. About the implementation, a mobile application that retrieves advertisements from Google Ads has been implemented (based on the developers' expertise there could different implementation approaches). The implementation that has been performed clearly demonstrated that the approach is viable and can be easily incorporated into applications. Although the cold-start problem is a popular domain for research, in this work the special case of 'frozen user' is pinpointed. To the best of our knowledge, there is a gap in the literature and this manuscript opens paths for future research (about the frozen user). Furthermore, the approach that is proposed will help developers to implement efficient personalized advertisement systems when there is not enough data available. By following the above steps they will be able to implement systems that can deal with the frozen user problem. An implementation example has been made in this work, but based on the developers' expertise (programming languages, software available, etc.) they can build their own implementations. This is very important because users often arise privacy concerns, do not connect their social media accounts, insert fake data, etc. [43; 44]. Information overload and irritating advertisements can be avoided [45]. Our future plans include further exploring the frozen user problem and implementing new approaches by taking into consideration new technologies such as IOT (Internet of Things), big data, augmented reality, etc. [46]. Furthermore, an advertisement personalization system (not only for cold start users but also general) that combines knowledge from various domains (artificial intelligence, IoT, big data, knowledge-based, marketing, psychology, and augmented reality) will be implemented [47; 48]. Conceptualization, I.V. and A.T.; Approach, I.V. and A.T.; Implementation, I.V. ; Resources, I.V. ; Writing--original draft preparation, I.V. ; Writing--review and editing, I.V. and A.T.; Supervision, A.T.; Project administration, I.V. and A.T.; Funding acquisition, I.V. All authors have read and agreed to the published version of the manuscript. This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme \"Human Resources Development, Education and Lifelong Learning\" in the context of the project \"Reinforcement of Postdoctoral Researchers-2 nd Cycle\" (MIS-5033021), implemented by the State Scholarships Foundation (IKY). 2019-050-0503-18772. Not applicable.