{"id":351263,"date":"2019-01-17T09:48:51","date_gmt":"2019-01-17T17:48:51","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=351263"},"modified":"2019-01-17T09:48:51","modified_gmt":"2019-01-17T17:48:51","slug":"implicit-contextual-modelling-information-seeking","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/implicit-contextual-modelling-information-seeking\/","title":{"rendered":"Implicit Contextual Modelling for Information Seeking"},"content":{"rendered":"<p>In this position paper we present an overview of our current work in utilising contextual models to help web searchers find relevant web documents. The traditional approach to such information retrieval (IR) assumes that a user\u2019s information need is static and does not change as they peruse the documents the search system presents to them. However, this approach is simplistic, and does not consider the dynamic nature of an information need, something that has been well documented. In our approach we use implicit evidence, captured unobtrusively from searcher interaction to model such change. We are particularly interested in the <em>intentionality <\/em>behind this interaction, and which document representations (i.e. summaries, sentences) users view. This evidence is firstly used to enhance the user\u2019s query, then automatically update the display and if required, re-search the web.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this position paper we present an overview of our current work in utilising contextual models to help web searchers find relevant web documents. The traditional approach to such information retrieval (IR) assumes that a user\u2019s information need is static and does not change as they peruse the documents the search system presents to them. 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