Probabilistic Learning by Uncertainty Sampling with Non-Binary Relevance
Gianni Amati, Fábio Crestani · Studies in fuzziness and soft computing · 2000
We present a learning model for probabilistic learning in information retrieval and information filtering which is based on the concept of “uncertainty sampling”. Uncertainty sampling is a technique that exploits user relevance feed-back both for relevant and non-relevant documents. In particular, relevance sampling uses those documents whose relevance is most uncertain to speed up the learning of the user relevance criteria. We extend the use of uncertainty sampling by considering multiple levels of relevance and we show how this new learning model for information retrieval and filtering could be evaluated using collections with non-binary relevance assessments. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.