An Affective Aware Pseudo Association Method to Connect Disjoint Users Across Multiple Datasets -- An Enhanced Validation Method for Text-based Emotion Aware Recommender
This is an incremental improvement for researchers working on emotion-aware recommender systems, addressing a specific validation bottleneck.
The paper tackles the problem of evaluating text-based emotion-aware recommender systems by introducing an emotion-aware pseudo association method to connect disjoint users across different datasets, which improved the objective evaluation of top-N recommendations.
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.