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We propose an approach that organizes the search-result clusters into a hierarchical structure, called a query taxonomy, from the user's perspective. The proposed approach is based on an unsupervised classification method, which uses the dynamic Web as the training corpus. With query taxonomy, users can browse relevant Web documents more conveniently and comprehensibly. Our experimental results verify the feasibility and the effectiveness of the proposed approach to query taxonomy generation in Web search.
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |