Exploiting implicit user feedback in interactive video retrieval

This paper describes a video retrieval search engine that exploits both video analysis and user implicit feedback. Video analysis (i.e. automatic speech recognition, shot segmentation and keyframe processing) is performed by employing state of the art techniques, while for implicit feedback analysis we propose a novel methodology, which takes into account the patterns of user-interaction with the search engine. In order to do so, we introduce new video implicit interest indicators and we define search subsessions based on query categorization. The main idea is to employ implicit user feedback in terms of user navigation patterns in order to construct a weighted graph that expresses the semantic similarity between the video shots that are associated with the graph nodes. This graph is subsequently used to generate recommendations. The system and the approach are evaluated with real user experiments and significant improvements in terms of precision and recall are reported after the exploitation of implicit user feedback.