A large variety of user interaction with products, informational sources, or more general interactive media happens through mobile devices connected to Internet. Whereas desktop is still important for daytime at work audiences, smart devices (such as Tablets and smartphones) dominate the time people spend connected to the web.

Personalization is a desired functionality for applications within mobile environments. It provides means of fulfilling users' needs more effectively and efficiently and, consequently, increasing users' satisfaction, overtaking the traditional one-size-fits-all paradigm. It is considered a fundamental feature for both users who are receiving services and for service providers wanting to target their services to each individual, but different conceptual and technical challenges must be faced to achieve adequate adaptivity.

For personalization to be successful, information about the specific users is necessary to understand real user needs. Three principal sources of data about the user are usually considered: contextual information (e.g., geolocation, presence of other people), representations of user specific attributes, such as interests and needs, and social signals (e.g., likes or visited places on Facebook and Twitter).

A substantial amount of research has already been performed, especially in the area of recommender systems, which predict the user preferences on specific products and services. Most of the proposed approaches have focused on tapping specific features about the users and their current context and activities. But a very few consider a more holisitic approach to capitalize multiple sources of information and explore the mutual influence between user preferences, current context, and signals extracted from the egocentric social network data in different phases of the personalization process.

This special issue on personalized mobile services is designed to report on recent significant advances, carry out innovative explorations, and establish foundations for further research. It solicits original research contributions from academia and industry in the form of theoretical foundations, experimental and methodological developments, comparative analyses, descriptive, experiments, and clinical studies in the field.

Potential topics include but are not limited to the following:

• Novel context-aware approaches for personalized mobile services
• Social network analysis and mining for personalized mobile services
• Temporal analysis of social networks for mobile search and recommendation
• Opinion mining and sentiment analysis for mobile search and recommendation
• User behaviour modeling
• Mobile user profiling and classification
• Personalization for groups
• Adaptive user interfaces
• Context-aware mobile recommender systems
• Personalized mobile services in health, learning, commerce, tourism, and cultural heritage
• Privacy enhanced personalization
• Security and trust in personalized mobile services
• Measuring personalization effectiveness
• Beyond accuracy: novelty, diversity, and serendipity
• Testing and quality evaluation of mobile personalized services
• Large-scale personalized mobile services and scalability issues