A Hybrid Serendipity Social Recommender Model

Abstract

Social support is essential, especially in a working environment, because it can reduce psychological strain. The psychological strain associated with mental health in daily lives could have been gained from mismatched staffing and indirect control of the staffs. In order to meditate the problem, in this study, a hybrid serendipity social recommender model is proposed. This model is a combination of several proposed models encountered during the development process. Firstly, Rough Set Theory (RST) has been used in the early development stage to compute an automated attribute selection. RST is a mathematical tool that is widely used for knowledge discovery and feature selection. At the same time, it, minimizes redundancies among variables in classifying objects and extracts rules from the database. In the next stage, the classification model is used to classify the data into subclasses by using a deep learning algorithm. This algorithm aims to define the higher matching suggested attributes and used for processing massive data. Lastly, a reasoning approach is applied by using case-based reasoning from the result produced. The reasoning approach is used to finding the reasons why the attributes are selected. This approach searches the history of the selected attributes or compute a reason by digging back in the database.