Efficient Top-k Retrieval in Real Social Tagging Networks

We consider in this paper top-k query answering in social tagging systems, also known as folksonomies. This problem requires a significant departure from existing, socially agnostic techniques. In a network-aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker and how much weight their tagging actions should have in the result buildup. We propose an algorithm that has the potential to scale to current applications. While the problem has already been considered in previous literature, this was done either under strong simplifying assumptions or under choices that cannot scale to even moderatesize real world applications. We first consider a key aspect of the problem, which is accessing the closest or most relevant users for a given seeker. We describe how this can be done on the fly (without any pre-computations) for several possible choices arguably the most natural ones of proximity computation in a user network. Based on this, our top-k algorithm is sound and complete, and exhibits the same behavior as the one from existing literature, while addressing its scalability issues. To further reduce response times, we also analyze in more depth this behavior a Del.icio.us dataset and identify promising directions for efficiency by approximation.