Emotion-based Recommender System

TL;DR

This paper introduces a novel framework for incorporating user emotions into recommender systems, including new theories, metrics, visualization techniques, and an experimental demonstration of improved recommendation effectiveness.

Contribution

It presents a new theory and metrics for capturing user emotions, along with visualization methods and a framework for emotion-based recommendations, filling a gap in existing research.

Findings

Effective visualization of user emotions over time.

Demonstrated improvement in recommendation relevance.

Validated the proposed emotion-based framework experimentally.

Abstract

Recommender system is one of the most critical technologies for large internet companies such as Amazon and TikTok. Although millions of users use recommender systems globally everyday, and indeed, much data analysis work has been done to improve the technical accuracy of the system, to our limited knowledge, there has been little attention paid to analysis of users' emotion in recommender systems. In this paper, we create a new theory and metrics that could capture users' emotion when they are interacting with recommender systems. We also provide effective and efficient visualization techniques for visualization of users' emotion and its change in the customers' lifetime cycle. In the end, we design a framework for emotion-based recommendation algorithms, illustrated in a straightforward example with experimental results to demonstrate the effectiveness of our new theory.