New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to pre- dict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a signi¯cant improvement over our baseline.
University of Massachusetts Amherst, "A Framework to Predict the Quality of Answers with NonTextual" (2006). Computer Science Department Faculty Publication Series. 137.
Retrieved from https://scholarworks.umass.edu/cs_faculty_pubs/137