An Affective Aware Pseudo Association Method to Connect Disjoint Users Across Multiple Datasets -- An Enhanced Validation Method for Text-based Emotion Aware Recommender

TL;DR

This paper presents an emotion-aware pseudo association method to connect disjoint users across multiple datasets, enhancing the evaluation of text-based emotion-aware recommenders by enabling more comprehensive and subjective assessment of top-N recommendations.

Contribution

The study introduces a novel emotion-aware pseudo association technique that links users across datasets via similar emotion vectors, improving recommendation evaluation.

Findings

Enhanced evaluation accuracy for emotion-aware recommenders

Improved subjective assessment of top-N recommendations

Better integration of disjoint user data across datasets

Abstract

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.