Titlebook: Recommender Systems for the Social Web; José J. Pazos Arias,Ana Fernández Vilas,Rebeca P. Book 2012 Springer-Verlag GmbH Berlin Heidelber

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Recommendations on the Movein. Taking a step further, these suggestions could be based not only on the user’s current location, but also on the places where the user is supposed to be in the near future, so the recommended locations would be on the path the user is going to follow. In order to do that we need some location pr

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Conclusiones and Open Trends, their legal effects, the problem of interoperability and the social influence for recommendation (trust and groups). Finally, two differentes applications of social recommendation are also shown. This chapter include the authors’ view about the open trends and the future of recommendation. Specifi

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Social Recommendation Based on a Rich Aggregation Modelral model and the different recommender systems that were built on top, including the main results and the implications from one system to another. We conclude by highlighting the main findings and suggesting next steps and future directions.

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1868-4394 e Web 2.0 hype which have to be incorporated in traditional .The recommendation of products, content and services cannot be considered newly born, although its widespread application is still in full swing. While its growing success in numerous sectors, the progress of the  Social Web has revolution

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Implicit Trust Networks: A Semantic Approach to Improve Collaborative Recommendationso provide explicit data (about which other users they trust or not) to form such networks. In this chapter, we apply a semantic approach to automatically build implicit trust networks and, thereby, improve the recommendation results transparently to the users.

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SCORM and Social Recommendation: A Web 2.0 Approach to E-learningeducative content that are not able to filter, asses and/or consume. Along this line, we introduce in this paper a new recommendation mechanism supported by the tag clouds which label both users and content.

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