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Knowledge-Base Recommendation (or Recommender) Systems (KBRS) provide the user with advice about a decision to make or an action to take. KBRS rely on knowledge provided by human experts, encoded in the system and applied to input data, in order to generate recommendations. This survey overviews the main ideas characterizing a KBRS. Using a classification(More)
Over the last decade, online social networks (OSNs) have been growing quickly to become some of the largest systems in use. Their users are sharing more and more content, and in turn have access to vast amounts of information from and about each other. This increases the risk of information overload for every user. We define a set of event types, which can(More)
In our prior work, we identified rules for use in recommendation algorithms on Online Social Network (OSN) in order to increase the relevance of content suggested to a user. The resulting recommendation algorithms filter out and prioritize event types for OSN users (such as photo posts by friends, status posts, shared content, etc.), and are thereby(More)
This paper addresses the modelling of requirements for a content Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user switches roles constantly between content generator and content receiver. The goals and softgoals are different when the user is generating a post, as opposed as replying to a post. In other words, the user is(More)
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