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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minaret

Augmenting Collaborative Recommenders by Fusing Social Relationships: Membership and Friendshipand dense datasets as obtained from Last.fm. Our experiments have not only revealed the significant effects of the two relationships, especially the membership, in augmenting recommendation accuracy in the sparse data condition, but also identified the outperforming ability of the graph modeling in terms of realizing the optimal fusion mechanism.

engrossed

SPER

Social Recommender Systemsems in the basic landscape of recommender systems in general via a short review and comparison, we present related work in this more specialized area. After having laid out the basic conceptual grounds, we then contrast an earlier study with a recent study in order to investigate the limits of appli

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大看臺

Challenges in Tag Recommendations for Collaborative Tagging Systemswikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called .. Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system..The process of tagging reso

fiction

A Multi-criteria Approach for Automatic Ontology Recommendation Using Collective Knowledged processing of information are critical issues (e.g. biomedicine). In these domains, the number of available ontologies has grown rapidly during the last years. This is very positive because it enables a more effective (or more intelligent) knowledge management. However, it raises a new problem: wh

glans-penis

Implicit Trust Networks: A Semantic Approach to Improve Collaborative Recommendationse main concern in a collaborative recommendation is to identify the most suitable set of users to drive the selection of the items to be offered in each case. To distinguish relevant and reliable users from unreliable ones, trust and reputation models are being increasingly incorporated in these sys

CHOKE

Social Recommendation Based on a Rich Aggregation Modelmodel. The underlying infrastructure is based on a complex relationship model among three core entities: people, items, and tags. We describe the general model and the different recommender systems that were built on top, including the main results and the implications from one system to another. We

Boycott

Group Recommender Systems: New Perspectives in the Social Webr, we revise state of the art approaches on group formation, modelling and recommendation, and present challenging problems to be included in the group recommender system research agenda in the context of the Social Web.