Dear Colleagues,

Nowadays, Online Social Networks (OSNs) are becoming the most important medium in which to exchange information, ideas, opinions, and different kinds of content among people: they generate a huge amount of data showing Big Data features, mainly due to their high change rate, large volume, and intrinsic heterogeneity. On the other hand, in the last decade Recommender Systems have been introduced to support the browsing of very large data collections for various applications (e.g., e-commerce, multimedia sharing, cultural heritage, tourism, etc.), assisting users to find “what they really need”. The coupling of OSNs with recommender systems offers new opportunities for researchers. In particular, social network users’ relationships, interactions (with other users or generated content) and properties—through Social Network Analysis (SNA)—can surely improve recommender performances. In such a context, there are still many challenges that have to be faced to realize a new generation of large scale recommender systems that leverage complex information coming from different OSNs to efficiently provide users with better personalized recommendations. This Special Issue on “Social Networks and Recommender Systems” aims to promote new theories, techniques, and methods with which to exploit social data within a recommendation framework. Potential topics include, but not limited to, the following:

• Social media recommender systems,
• Large-scale parallel and distributed implementations of social media recommender systems,
• Applications of social media recommender systems (e.g., cultural heritage, tourism, etc.)
• Context-aware recommender systems incorporating social information,
• Novel recommendation techniques for social network applications,
• Enhancing recommender performances using social big data,
• Multimedia recommender systems for social networks,
• Privacy preserving in social recommender systems.

Prof. Vincenzo Moscato
Prof. Antonio Picariello
Guest Editors

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• online social networks
• recommender systems
• social network analysis
• big data