An Experimental Analysis of Suggestions in Collaborative Tagging

Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource, like a web-page. In this paper we investigate the influence of tag suggestions on the emergence of power-law distributions as a result of collaborative tag behavior. Although previous research has already shown that power-laws emerge in tagging systems, the cause of why power-law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power-laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. This imitation behavior leads to a feedback loop in which some tags are reinforced and get more popular which is also known as the `rich get richer´ or a preferential attachment model. We present experimental results that show that the power-law distribution forms when tag suggestions are not presented to the users, and the power-law distribution does not hold when there are tag suggestions presented to the user. Furthermore, we show that the real effect of tag suggestions is rather subtle; the power-law distribution that would naturally occur without tag suggestions is `compressed´ if tag suggestions are given to the user, resulting in a shorter long tail and a `compressed´ top of the power-law distribution. The consequences of this experiment show that tag suggestions by themselves do not account for the formation of power-law distributions in tagging systems.