A new probabilistic scheme for information retrieval in hypertext

Abstract

Abstract The aim of probabilistic models is to define a retrieval strategy within which documents can be optimally ranked according to their relevance probability, with respect to a given request. In this scheme, the underlying probabilities are estimated according to a history of past queries along with their relevance judgments. Having evolved over the last twenty years, these estimations allow us to take both document frequency and within-document frequency into account. In the current study, we suggest representing documents not only by index term vectors as proposed by previous probabilistic models but also by considering relevance hypertext links. These relationships, which provide additional evidence on document content, are established according to requests and relevance judgments, and may improve the ranking of the retrieved records, in a sequence most likely to fulfill user intent. Thus, to enhance retrieval effectiveness, our learning retrieval scheme should modify: (1) the weight assigned to e...