# Mobile Multimedia Recommendation in Smart Communities: A Survey

arXiv: https://arxiv.org/abs/1312.6565 · HTML: https://mlsanity.com/paper/1312.6565/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2013.

## Built on by (most cited first, 5 of 5)

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- [Graph2text or Graph2token: A Perspective of Large Language Models for Graph Learning](https://mlsanity.com/paper/2501.01124/) (2025)
- [Towards Multimodal Metaphor Understanding: A Chinese Dataset and Model for Metaphor Mapping Identification](https://mlsanity.com/paper/2501.02434/) (2025)
- [Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation](https://mlsanity.com/paper/2202.10236/) (2022)
- [ED-Filter: Dynamic Feature Filtering for Eating Disorder Classification](https://mlsanity.com/paper/2501.14785/) (2025)

## Abstract (arXiv)

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.