  We derive a method to enhance the evaluation for a text-based Emotion Aware
Recommender that we have developed. However, we did not implement a suitable
way to assess the top-N recommendations subjectively. In this study, we
introduce an emotion-aware Pseudo Association Method to interconnect disjointed
users across different datasets so data files can be combined to form a more
extensive data file. Users with the same user IDs found in separate data files
in the same dataset are often the same users. However, users with the same user
ID may not be the same user across different datasets. We advocate an emotion
aware Pseudo Association Method to associate users across different datasets.
The approach interconnects users with different user IDs across different
datasets through the most similar users' emotion vectors (UVECs). We found the
method improved the evaluation process of assessing the top-N recommendations
objectively.
