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IADIS International Conference WWW/Internet 2010Figure 1. General structure of the systemiii) Recommending friends via tagsThis approach uses the cosine similarity measure (Ji, et al., 2007) to find the similarity between two users.The approach detects similarity between two users by taking into consideration the number of tags commonbetween them, and how often they have used them. Clearly it has to be run for each pair of users present inthe topology of a social network, in order to be comprehensive with regard to it. The equation used forcollaborative tagging (Ji, et al., 2007) is:Equation 1. Similarity between users via Collaborative Taggingiv) Recommending friends via ontology and ratingsThis approach builds on the first. It finds all the users which have tagged recommended resources that thefirst approach recommends for a target user, in order to recommend friends to the target user. It takes intoconsideration the number of tags that each user has attributed to each particular resource. This is used to ratethe friendship of the recommended friend.v) Recommending experts via technical terms & tagsThis approach attempts to find a measure for the expertise of each user, if any. It compares the tags that auser has used for different resources against a set of technical terms. The more tags a user has which fallwithin the set, the more expert he is classified to be.vi) Finding expertise distribution of experts via ontologyThis approach is similar to the former approach in that it deals with the expertise of a user. However,whereas the former approach measures the expertise of each user and put a figure on it, this approachattempts to put a figure of how much the expertise of a user is spread. To do this, this approach makes use ofthe ontology. It counts, for each user, the number of technical tags used by him pertaining to concept fallingunder different branches. Therefore, each user would have scores as much as there are branches in theontology. A branch is defined as a concept and its children, which is a child of the concept of “Collection”,which is the parent of all branch parents. From these, scores, then, for each user, one can calculate thestandard deviation. This would go to indicate how much the expertise of a user is spread.Different algorithms are put forward in different approaches. The approach, which traverses an ontologytree in order to recommend similar resources and similar friends employs the concept of recursion:getScoresUp(concepts, position, currentScore){for (int i = 0; i < concepts.size(); i++){altagsCurrConc = concepts(i).getTags();/* loop on altagsCurrConc, finding which resources are taggedby the tags and adding [currentScore] to their scoresarrayList which is the value of hTresScores, keys beingresourcesNames*/ (Process A)379