Mobile Multimedia Recommendation in Smart Communities: A Survey
Feng Xia, Nana Yaw Asabere, Ahmedin Mohammed Ahmed, Jing Li, Xiangjie, Kong

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…
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Taxonomy
TopicsRecommender Systems and Techniques · Caching and Content Delivery · Image and Video Quality Assessment
