Publication

Abstract : Personality computing has grown significantly in use, with useful applications emerging in fields of study like human-robot interaction and recommendation systems. Traditional recommendation systems frequently run into issues with free riders, data scarcity, and a lack of understanding of user preferences. By strengthening the grasp of user preferences and increasing the precision of recommendations, the addition of well-established user personality features aids in resolving these problems. Therefore, the goal of this research is to take advantage of personality computing's numerous advantages while overcoming the drawbacks of current recommendation systems. This model achieved an accuracy of 97% on the test data because it was trained on pre-processed and padded sequences. By utilising LSTM layers, the model is better able to understand contextual information included in the text input and efficiently capture sequential dependencies.