Analysis of the Impact of a Tag Recommendation System in a Real-World Folksonomy

Abstract

Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organising their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improve annotation quality as well as to reduce the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyse the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. Also, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems, and points to future directions for the further development of those systems and their analysis.

Social Image Tag Relevance Learning based on Pixel Voting

“…Each pixel can vote to determine the similarity score of query image and seed image by (6), is the threshold. If the similarity score of pixel t is greater than α, the two value function ( ( ) ≥ ) = 1, otherwise ( ( ) ≥ ) = 0.…”

“…Eliminating the irrelevant tags from the potential tags for each image is the goal of tag de-noising. Take the 'baby' tag for example, the seed image's visual neighbors are selected according to (6). And the visual neighbors vote to calculate the relevance score of the tag 'baby' and the seed image.…”

“…According to [1,2], there are only 10% images having their most relevant tags at the first place. Therefore, more and more meaningful and challenge work based tag relevance learning algorithms is attracting people's attention, such as tag de-noising [3][4][5], tag recommendation [6][7][8] and tag ranking [9][10][11],which are studied to improve social images retrieval efficiency.…”

Balancing the Fluency-Consistency Tradeoff in Collaborative Information Search with a Recommender Approach

“…Probably because the lack of central control (e.g., standardized vocabularies) can lead to the "vocabulary problem" (divergent wording when tagging the very same object; Furnas, Landauer, Gomez, & Dumais, 1987), an endeavor of investigating and supporting consistency has come to the fore, for example by studying "semantic stabilization" (Wagner, Singer, Strohmaier, & Huberman, 2014). This focus on the convergent pole is also reflected by a large body of literature on the development of automatic tag recommendation mechanisms (TRM) (Dellschaft & Staab, 2012;Font, Serrà, & Serra, 2015;Jäschke, Marinho, Hotho, Schmidt-Thieme, & Stumme, 2007). TRM are services that encourage a convergent tag use and hence, alleviate the vocabulary problem by suggesting tags already applied in the past by other users.…”

“…Especially, from an applied perspective, such validation appears important to us because it would imply that designing social systems for information search can be directed either toward convergence (i.e., tagging consistency), or toward divergence (i.e., ideational fluency). The former would aim for aligning the vocabulary (e.g., Font et al, 2015), while the latter for stimulating creation (e.g., Candy & Hewett, 2008), and it would seem difficult to balance the two complementary processes. Considering recent discussions around filter bubbles in Web environments (e.g., Pariser, 2011;Sunstein, 2001), we consider such research to contribute to a more balanced view on interaction in the social web.…”