Score and Rank Aggregation Methods for Explicit Search Result Diversification

Search result diversification is one of the key techniques to cope with the ambiguous and/or underspecified information needs of the web users. In the last few years, strategies that are based on the explicit knowledge of query aspects emerged as highly effective ways of diversifying the search results. Our contributions in this work are two-fold. First, we extensively evaluate the performance of a state-of-the-art explicit diversification strategy and pin-point its weaknesses. We propose basic yet novel optimizations to remedy these weaknesses and boost the performance of this algorithm. As a second contribution, inspired from the success of the current diversification strategies that exploit the relevance of the candidate documents to individual query aspects, we cast the diversification problem to the problem of ranking aggregation. To this end, we propose to materialize the re-rankings of the candidate documents for each query aspect and then merge these rankings by adapting the score(-based) and rank(-based) aggregation methods. Our extensive experimental evaluations show that certain ranking aggregation methods are superior to the existing explicit diversification strategies in terms of the diversification effectiveness. Furthermore, these ranking aggregation methods have lower computational complexity than the state-of-the-art diversification strategies.