Mobile Multimedia Recommendation in Smart Communities: A Survey

TL;DR

This survey reviews mobile multimedia recommendation systems within smart communities, highlighting techniques, challenges, and open issues to improve personalized content delivery on resource-limited mobile devices.

Contribution

It provides a comprehensive analysis of existing mobile multimedia recommendation approaches across three smart community contexts, identifying key techniques and challenges.

Findings

Proactive, sensor-based, and hybrid recommenders enhance personalization.

Challenges include integrating context and social properties for better recommendations.

Open issues involve improving accuracy and trustworthiness of recommendations.

Abstract

Due to the rapid growth of internet broadband access and proliferation of modern mobile devices, various types of multimedia (e.g. text, images, audios and videos) have become ubiquitously available anytime. Mobile device users usually store and use multimedia contents based on their personal interests and preferences. Mobile device challenges such as storage limitation have however introduced the problem of mobile multimedia overload to users. In order to tackle this problem, researchers have developed various techniques that recommend multimedia for mobile users. In this survey paper, we examine the importance of mobile multimedia recommendation systems from the perspective of three smart communities, namely, mobile social learning, mobile event guide and context-aware services. A cautious analysis of existing research reveals that the implementation of proactive, sensor-based and hybrid recommender systems can improve mobile multimedia recommendations. Nevertheless, there are still challenges and open issues such as the incorporation of context and social properties, which need to be tackled in order to generate accurate and trustworthy mobile multimedia recommendations.