THE EFFECTIVENESS OF DOCUMENT NEIGHBORING IN SEARCH ENHANCEMENT

We consider two kinds of queries that may be applied to a database. The first is a query written by a searcher to express an information need. The second is a request for documents most similar to a document already judged relevant by the searcher. We examine the effectiveness of these two procedures and show that in important cases the latter query type is more effective than the former. This provides a new view of the cluster hypothesis and a justification for document neighboring procedures (precomputation of closely related documents). If all the documents in a database have readily available precomputed nearest neighbors, a new search algorithm, which we call parallel neighborhood searching, is conveniently used. We show that this feedback-based method provides significant improvement in recall over traditional linear searching methods, and even appears superior to traditional feedback methods in overall performance.