STRATEGIES FOR FINDING RELEVANT DOCUMENTS AMONG NEIGHBOR LISTS

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One method for gaining access to a database is for a searcher to choose some key terms or write a short description of the topic of his interest. The resultant key terms which are given directly or processed out of the written request are then used to make a vector for searching the database for related documents. It is well known that the specificity of such a search is not as good as that which would be obtained from a more definitive description of the searchers interest, a description which he in many cases can only give after he has found an article of interest. This is the basis for so-called relevance feedback procedures. Relevance feedback makes use of relevance judgments made on already retrieved material to focus a search. In this study we are concerned with a special kind of relevance feedback. We deal with databases in which for each document the list of the top documents in relation to the given document have already been computed. Our purpose is to discover the most efficient use of these precomputed neighbor lists to facilitate a search which may begin by a search based on several key terms as described in the previous paragraph. When the first relevant document is found it comes with a precomputed list of neighbors. The question is whether to look back for the next relevant document on the initial list or to look on the list of precomputed neighbors associated with a relevant document. After the second relevant document has been identified then one again has the question of how to use its neighbor list optimally and so on. We have tried a number of different strategies and have found that there is definite improvement in using the neighbor lists and have quantified the performance obtained on the CISI, CRAN, CACM, and MED test sets of documents. A paper describing these results is in press.