Authors
Date (dd-mm-yyyy)
2015
Title
Dynamic Query Modeling for Related Content Finding
Publication Year
2015
Publisher
New York, NYAssociation for Computing Machinery9781450336215
ISBN
9781450336215
Document type
Conference contribution
Faculty
Faculty of Science (FNWI)
Institute
Informatics Institute (IVI)
Abstract
While watching television, people increasingly consume additional content related to what they are watching. We consider the task of finding video content related to a live television broadcast for which we leverage the textual stream of subtitles associated with the broadcast. We model this task as a Markov decision process and propose a method that uses reinforcement learning to directly optimize the retrieval effectiveness of queries generated from the stream of subtitles. Our dynamic query modeling approach significantly outperforms state-of-the-art baselines for stationary query
modeling and for text-based retrieval in a television setting. In particular we find that carefully weighting terms and decaying these weights based on recency significantly improves effectiveness. Moreover, our method is highly efficient and can be used in a live television setting, i.e., in near real time.
URL
go to publisher's site
Permalink
https://hdl.handle.net/11245/1.504812
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