Collaborative tagging has emerged as an efficient way to/nsemantically describe online resources shared by a community/nof users. However, tag descriptions present some/ndrawbacks such as tag scarcity or concept inconsistencies./nIn these situations, tag recommendation strategies can help/nusers in adding meaningful tags to the resources being described./nFreesound is an online audio clip sharing site that/nuses collaborative tagging to describe a collection of more/nthan 140,000 sound samples. In ...
Collaborative tagging has emerged as an efficient way to/nsemantically describe online resources shared by a community/nof users. However, tag descriptions present some/ndrawbacks such as tag scarcity or concept inconsistencies./nIn these situations, tag recommendation strategies can help/nusers in adding meaningful tags to the resources being described./nFreesound is an online audio clip sharing site that/nuses collaborative tagging to describe a collection of more/nthan 140,000 sound samples. In this paper we propose four/nalgorithm variants for tag recommendation based on tag/nco-occurrence in the Freesound folksonomy. On the basis/nof removing a number of tags that have to be later predicted/nby the algorithms, we find that using ranks instead of raw/ntag similarities produces statistically significant improvements./nMoreover, we show how specific strategies for selecting/nthe appropriate number of tags to be recommended/ncan significantly improve algorithms’ performance. These/ntwo aspects provide insight into some of the most basic/ncomponents of tag recommendation systems, and we plan/nto exploit them in future real-world deployments.
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