Abstract
A new Relevance Feedback (RF) technique called Weight Propagation has been developed which provides greater retrieval effectiveness and computational efficiency than previously described techniques. Documents judged relevant by the user propagate positive weights to documents close by in vector similarity space, while documents judged not relevant propagate negative weights to such neighbouring documents. Retrieval effectiveness is improved since the documents are treated as independent vectors rather than being merged into a single vector as is the case with traditional vector model RF techniques, or by determining the documents relevancy based in part on the lengths of all the documents as with traditional probabilistic RF techniques. Improving the computational efficiency of Relevance Feedback by considering only documents in a given neighbourhood means that the Weight Propagation technique can be used with large collections.
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Yamout, F., Oakes, M., Tait, J. (2007). Relevance Feedback Using Weight Propagation Compared with Information-Theoretic Query Expansion. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_25
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DOI: https://doi.org/10.1007/978-3-540-71496-5_25
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