Semantically Rich Recommendations in Social Networks for Sharing, Exchanging and Ranking Semantic Context

Abstract

Abstract. Recommender algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures. This paper explores how semantically rich complex recommendation structures, represented as RDF graphs, can be exchanged and shared in a distributed social network. After presenting a motivating scenario we define several annotation ontologies we use in order to describe context information on the user's desktop and show how our ranking algorithm can exploit this information. We discuss how social distributed networks and interest groups are specified using extended FOAF vocabulary, and how members of these interest groups share semantically rich recommendations in such a network. These recommendations transport shared context as well as ranking information, described in annotation ontologies. We propose an algorithm to compute these rankings which exploits available context information and show how rankings are influenced by the context received from other users as well as by the reputation of the members of the social network with whom the context is exchanged.

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“…The method of [60] works on top of a (process) logs repository and none of the methods presented there [60] can be used in our scenario. For further information on other variants of recommendation mechanisms, we refer to [27,74].…”

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“…For instance, [10] combines ontologybased solutions with information gathered by tagging mechanisms typical of social networks, in order to provide a semantically enabled recommendation system. Instead, [11] supports a distributed social network based on recommendation structures implemented as RDF graphs. In particular, it supports the spread of data and resources based on semantically rich information stored in FOAF documents.…”

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