A search architecture enabling efficient diversification of search results

In this paper, we deal with efficiency of the diversification of results returned by Web Search Engines (WSEs). We extend a search architecture based on additive Machine Learned Ranking (MLR) systems with a new module computing the diversity score of each retrieved document. Our proposed solution is designed to be used with other techniques, (e.g. early termination of rank computation, etc.). Furthermore, we use an efficient state-of-the-art diversification approach based on knowledge extracted from query logs, and prove that it can efficiently works in a additive machine learned ranking system, and we study its feasibility.