document: Joint relevance and freshness learning from clickthroughs for news search In contrast to traditional Web search , where topical relevance is often the main selection criterion , news search is characterized by the increased importance of freshness . However , the estimation of relevance and freshness , and especially the relative importance of these two aspects , are highly specific to the query and the time when the query was issued . In this work , we propose a unified framework for modeling the topical relevance and freshness , as well as their relative importance , based on click logs . We use click statistics and content analysis techniques to define a set of temporal features , which predict the right mix of freshness and relevance for a given query . Experimental results on both historical click data and editorial judgments demonstrate the effectiveness of the proposed approach .
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