Multimedia Recommendation System Using Adaptive Resonance Theory Neural Model for Digital Storytelling

Abstract

Multimedia recommendation technology has been developed in various fields these days. In order to provide multimedia in addition to dialog, it is essential to select appropriate multimedia associated with certain situation for more delivery effects of digital storytelling, which enables story telling agents to share their stories with users using digital multimedia in an effective way. For this purpose, we propose a multimedia recommendation system for software agents of smart devices to select multimedia that is appropriate to the given situation in storytelling to users or interacting with users. The fusion ART network is employed for the multimedia recommendation system that selects an appropriate digital media file for individual multimedia features. The system is learned incrementally based on feedback from users. The proposed system is purposed to select multimedia to be conveyed in addition to the dialog between the user and the digital creature on a smartphone. The applicability is verified through experiments with a smartphone application implemented for demonstration.