In this paper, we present the motivation, process, results and analysis of results that we have worked on as part of our participation in the 2015 MediaEval Retrieving Diverse Social Images Task. This year, we adapted a recently-published technique for result diversification (``Relational Learning-to-Rank'' ), borrowed from the world of standard document retrieval. As compared to the original work, our version makes certain changes to the ranking and comparison algorithm, and explores a variety of feature combinations specific to an image retrieval context. The key idea behind our technique was a greedy iterative approach to ranking search results, which attempted to balance relevance with redundancy by comparing candidate results to those already selected by the algorithm. Our approach worked tolerably well on many queries, but there is clearly room for improvement.
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