Multimedia Recommendation System for Video Game Based on High-Level Visual Semantic Features

Abstract

The increase of multimedia content in e-commerce and entertainment services creates a new research gap in the field of recommendation systems. The main emphasis of the presented work is on increasing the accuracy of multimedia recommendations using visual semantic content. Recent approaches have shown that the inclusion of visual information is helpful to understand the semantic features for a recommendation model. The researchers have contributed to the field of multimedia item recommendations using low-level visual semantic features. Here, we seek to extend this contribution by exploring the high-level visual semantic content using constant visual attributes for video game recommendation systems. With the exponential growth of multimedia content in the video game industry in the last decade, researchers investigate the importance of personalized video game recommendation techniques. Previous methods have not investigated the importance of visual semantic content for video game recommendations. A practical recommendation system for video games is challenging due to the data diversity, level of user interest, and semantic complexity of features involved. This study proposed a novel method named Deep Visual Semantic Multimedia Recommendation Systems (D_VSMR) to deal with high-level visual features for multimedia recommendation systems. A visual semantic-based video game recommendation system utilizing deep learning methods for visual content learning and user profile learning is introduced. The proposed approach employs content-based techniques to expand users’ profiles. The user profile expansion is based on the visual content of games. The required datasets have been obtained from video game e-commerce platforms like Google Play Store and Amazon for evaluation purposes. The evaluation results have shown that the proposed approach’s accuracy and effectiveness have been improved up to 95.87% compared to the other state-of-the-art methods.