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The recent increase in the volume and variety of video content available online presents growing challenges for video search. Users face increased difficulty in formulating effective queries and search engines must deploy highly effective algorithms to provide relevant results. This talk addresses these challenges by introducing two novel frameworks and approaches. First, we discuss a principled framework for multimedia retrieval that moves beyond 'what' users are searching for also to encompass 'why' they search. This 'why' is understood as the reason, purpose or immediate goal behind a user information need, which is identified as the underlying 'user intent'. We identify useful intent categories for online video search, present validation experiments showing that these categories display enough invariance to be successfully modeled by a video search engine and demonstrate the potential for these categories to improve video retrieval with a large crowdsourcing user study. Second, we present a novel approach able to predict for which queries results optimization is most useful, i.e., predicting which queries will fail in the search session of a user on a video search engine. Being able to predict when a video search query would fail is likely to make the video search result optimization more efficient and deploy optimization techniques more effectively. This approach uses a combination of features derived from the search log of a video search engine (capturing users' behavior) and features derived from the video search results list (capturing the visual variance of search results), with the objective to predict whether a particular query is likely to fail in the context of a particular search session.