Evaluating the implicit feedback models for adaptive video retrieval

@inproceedings{Hopfgartner2007EvaluatingTI,
  title={Evaluating the implicit feedback models for adaptive video retrieval},
  author={Frank Hopfgartner and Joemon M. Jose},
  booktitle={Multimedia Information Retrieval},
  year={2007}
}
Interactive video retrieval systems are becoming popular. On the one hand, these systems try to reduce the effect of the semantic gap, an issue currently being addressed by the multimedia retrieval community. On the other hand, such systems enhance the quality of information seeking for the user by supporting query formulation and reformulation. Interactive systems are very popular in the textual retrieval domain. However, they are relatively unexplored in the case of multimedia retrieval. The… 
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Utilizing Implicit User Feedback to Improve Interactive Video Retrieval

A framework, where the video is first indexed according to temporal, textual, and visual features and then implicit user feedback analysis is realized using a graph-based methodology, which encodes the semantic relations between video segments based on past user interaction and is subsequently used to generate recommendations.

Facet-Based Browsing in Video Retrieval: A Simulation-Based Evaluation

A novel interactive video retrieval approach which uses sub-needs of an information need for querying and organising the search process and demonstrates that the faceted browser can potentially improve the search effectiveness.

Effects of Usage-Based Feedback on Video Retrieval: A Simulation-Based Study

A graph-based model for all types of implicit and explicit feedback, in which the relevant usage information is represented, is proposed, designed to capture the complex interactions of a user with an interactive video retrieval system.

Optimizing visual search with implicit user feedback in interactive video retrieval

A framework is proposed, in which video processing is performed by employing well established techniques, while implicit user feedback analysis is realized with a graph based approach that processes the user actions and navigation patterns during a search session, in order to initiate semantic relations between the video segments.

Studying interaction methodologies in video retrieval

This paper proposes to study the importance of various implicit indicators of relevance and investigates how this implicit feedback can be combined with static user profiles towards an adaptive video retrieval model.

Simulated evaluation of faceted browsing based on feature selection

The experimental results of this study demonstrate that the faceted browser can potentially improve the search effectiveness and present a methodology to reduce the dimensionality of features by selecting the most important ones.

Improving retrieval relevance using users' explicit feedback

CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral, is proposed in this study, and is found to have the highest retrieval precisions at all the three levels compared to the other feedback models.

Moving beyond text highlights: inferring users' interests to improve the relevance of retrieval

Users’ post-click behaviour may serve as a significant indicator of their interests, and thus can be used to improve the relevance of the retrieved results, according to a study that examined users’ text highlight frequency, length and users' copy-paste actions.

Personalised video retrieval: application of implicit feedback and semantic user profiles

A semantic user profiling approach for news video retrieval is introduced, which exploits a generic ontology to put news stories into its context and can lead to a better understanding of the personal interests.

A News Video Retrieval Framework for the Study of Implicit Relevance Feedback

  • F. HopfgartnerJ. Jose
  • Computer Science
    Second International Workshop on Semantic Media Adaptation and Personalization (SMAP 2007)
  • 2007
A framework for recording, analysing, indexing and retrieving news videos such as the BBC one o'clock news is proposed, believing that such a framework will be useful to identify implicit indicators of relevance, a nearly untouched area in adaptive multimedia retrieval.

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