Effects of usage-based feedback on video retrieval: A simulation-based study
Abstract
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Information Systems, http://dx.doi.org/10.1145/1961209.1961214 ; We present a model for exploiting community-based usage information for video retrieval, where implicit usage information from past users is exploited in order to provide enhanced assistance in video retrieval tasks, and alleviate the effects of the semantic gap problem. We propose a graph-based model for all types of implicit and explicit feedback, in which the relevant usage information is represented. Our model is designed to capture the complex interactions of a user with an interactive video retrieval system, including the representation of sequences of user-system interaction during a search session. Building upon this model, four recommendation strategies are defined and evaluated. An evaluation strategy is proposed based on simulated user actions, which enables the evaluation of our recommendation strategies over a usage information pool obtained from 24 users performing four different TRECVid tasks. Furthermore, the proposed simulation approach is used to simulate usage information pools with different characteristics, with which the recommendation approaches are further evaluated on a larger set of tasks, and their performance is studied with respect to the scalability and quality of the available implicit information. ; This work was partially supported by the Spanish Ministry of Science and Education (TIN2008-06566-C04-02) and the Regional Government of Madrid (S2009TIC-1542). F. Hopfgartner was supported by a postdoctoral fellowship of the German Academic Exchange Service (DAAD)
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