To seek, perchance to fail: Expressions of user needs in internet video search

Abstract

This work investigates user expressions of content needs in Internet video search, focusing on cases in which users have failed to meet their search goals, although relevant content is reasonably certain to exist. We study expressions of user needs in the form of requests (i.e., questions) formulated in natural language and published to Yahoo! Answers. Experiments show that classifiers can distinguish requests associated with search-goal failure. We identify a group of ‘easy-to-predict’ requests (cases for which the classifier predicts search-goal failure well) and compile an inventory of strategies used by users to express search goals in these cases. In a final set of experiments, we demonstrate the feasibility of predicting search-goal failure based on query-like representations of the original natural-language requests. The results of our study are intended to inform the future development of indexing and retrieval techniques for Internet video that target difficult queries.