RSED: a Novel Recommendation Based on Emotion Recognition Methods
Tiechen Kong, Qing-qiang Liu, Jingjie Zhu · 2009
With the growth of e-commerce, the development of recommendation systems is helpful for users to select desirable products from all kinds of them. The existing e-commerce recommendation approaches are based on a user's preference on music. However, sometimes, it might better meet users' requirement to recommend products according to emotions. In this paper, we propose a novel framework model for emotion based e-commerce recommendation systems. The core of the recommendation framework is the construction of the product vs customer emotion model by two-dimensional overlap spaces, which plays an important role in conveying emotions in products. We investigate the product feature extraction and propose some related matching algorithms for the construction of product vs customer emotion model. Then the system model, data structures and so on are given in our paper. At last, experimental and analytical result shows the proposed emotion-based music recommendation achieves higher accuracy and faster retrieval speed.