Proceedings of the 20th international conference companion on World wide web
Scenarios for collaborative architectures for monetizing broadcast archive clips
Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments
Integrating multi-source user data to enhance privacy in social interaction
Proceedings of the 13th International Conference on Interacción Persona-Ordenador
Recommending social network applications via social filtering mechanisms
Information Sciences: an International Journal
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As the exponential growth of information generated on the World Wide Web, Social Recommendation has emerged as one of the hot research topics recently. Social Recommendation forms a specific type of information filtering technique that attempts to suggest information (blogs, news, music, travel plans, web pages, images, tags, etc.) that are likely to interest the users. Social Recommendation involves the investigation of collective intelligence by using computational techniques such as machine learning, data mining, natural language processing, etc. on social behavior data collected from blogs, wikis, recommender systems, question & answer communities, query logs, tags, etc. from areas such as social networks, social search, social media, social bookmarks, social news, social knowledge sharing, and social games. In this tutorial, we will introduce Social Recommendation and elaborate on how the various characteristics and aspects are involved in the social platforms for collective intelligence. Moreover, we will discuss the challenging issues involved in Social Recommendation in the context of theory and models of social networks, methods to improve recommender systems using social contextual information, ways to deal with partial and incomplete information in the social context, scalability and algorithmic issues with social computational techniques.