Rae, Adam; Sigurbjörnsson, Börkur and van Zwol, Roelof
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In this paper we address the task of recommending additional tags to partially annotated media objects, in our case images. We propose an extendable framework that can recommend tags using a combination of different personalised and collective contexts. We combine information from four contexts: (1) all the photos in the system, (2) a user's own photos, (3) the photos of a user's social contacts, and (4) the photos posted in the groups of which a user is a member. Variants of methods (1) and (2) have been proposed in previous work, but the use of (3) and (4) is novel.
For each of the contexts we use the same probabilistic model and Borda Count based aggregation approach to generate recommendations from different contexts into a unified ranking of recommended tags. We evaluate our system using a large set of real-world data from Flickr. We show that by using personalised contexts we can significantly improve tag recommendation compared to using collective knowledge alone. We also analyse our experimental results to explore the capabilities of our system with respect to a user's social behaviour.
|Item Type:||Conference Item|
|Copyright Holders:||2010 Centre de Hautes Etudes Internationales dï¿½Informatique Documentaire, Paris, France|
|Project Funding Details:||
|Keywords:||Flickr; tag recommendation; social networks; personalisation|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Adam Rae|
|Date Deposited:||05 May 2010 11:12|
|Last Modified:||04 Oct 2016 17:36|
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