Dynamic ranked retrieval

@inproceedings{Brandt2011DynamicRR,
  title={Dynamic ranked retrieval},
  author={Christopher Brandt and Thorsten Joachims and Yisong Yue and Jacob Bank},
  booktitle={WSDM},
  year={2011}
}
We present a theoretically well-founded retrieval model for dynamically generating rankings based on interactive user feedback. Unlike conventional rankings that remain static after the query was issued, dynamic rankings allow and anticipate user activity, thus providing a way to combine the otherwise contradictory goals of result diversification and high recall. We develop a decision-theoretic framework to guide the design and evaluation of algorithms for this interactive retrieval setting… CONTINUE READING

Topics from this paper.

Citations

Publications citing this paper.
SHOWING 1-10 OF 33 CITATIONS

Machine learning from human preferences and choices

VIEW 8 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Structured learning of two-level dynamic rankings

VIEW 8 EXCERPTS
CITES BACKGROUND, RESULTS & METHODS
HIGHLY INFLUENCED

Desire: A Dynamic Approach for Exploratory Search Results Recommendation

  • 2015 Brazilian Conference on Intelligent Systems (BRACIS)
  • 2015
VIEW 2 EXCERPTS
CITES BACKGROUND & METHODS

References

Publications referenced by this paper.

Incremental Relevance Feedback

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Similar Papers