Abstract. Diversifying query expansion that leverages multiple resources has demonstrated promising results in the task of search result diversification (SRD) on several benchmark datasets. In existing studies, however, the weight of a resource, or the degree of the contribution of that resource to SRD, is largely ignored. In this work, we present a query level resource weighting method based on a set of features which are integrated into a regression model. Accordingly, we develop an SRD system which generates for a resource a number of expansion candidates that is proportional to the weight of that resource. We thoroughly evaluate our approach on TREC 2009TREC , 2010TREC and 2011 Web tracks, and show that: 1) our system outperforms the existing methods without resource weighting; and 2) query level resource weighting is superior to the non-query level resource weighting.
Diversified query expansion (DQE) based approaches aim to select a set of expansion terms with less redundancy among them while covering as many query aspects as possible. Recently they have experimentally demonstrate their effectiveness for the task of search result diversification. One challenge faced by existing DQE approaches is how to ensure the aspect coverage. In this paper, we propose a novel method for DQE, called compact aspect embedding, which exploits trace norm regularization to learn a low rank vector space for the query, with each eigenvector of the learnt vector space representing an aspect, and the absolute value of its corresponding eigenvalue representing the association strength of that aspect to the query. Meanwhile, each expansion term is mapped into the vector space as well. Based on this novel representation of the query aspects and expansion terms, we design a greedy selection strategy to choose a set of expansion terms to explicitly cover all possible aspects of the query.We test our method on several TREC diversification data sets, and show that our method significantly outperforms the state-of-the-art search result diversification approaches.
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