STRATEGIES FOR FINDING RELEVANT DOCUMENTS AMONG NEIGHBOR LISTS

获得对数据库的访问的一种方法是由搜索者选择 一些关键术语或写下他的主题的简短描述 利息。直接给出或处理的结果关键字 然后使用写入请求中的数据来生成用于搜索的向量 相关文档的数据库。众所周知， 这种搜索的特殊性不如预期的那样好。 从搜索者兴趣的更明确的描述中获得，一个 在许多情况下，他只能在找到一个 感兴趣的物品。这就是所谓的相关性反馈的基础 程序。相关性反馈利用了对 已经检索到要集中搜索的材料。在这项研究中，我们 关注一种特殊的相关性反馈。 我们处理的数据库中，每个文档的顶端列表 已经计算了与给定文档相关的文档。 我们的目的是发现如何最有效地利用这些预先计算的资源 邻居列表以便于搜索，该搜索可以通过基于 关于上一段所述的几个关键术语。当 第一个相关文档被发现，它带有一个预先计算的列表 邻里。问题是，是否应该回顾一下下一个相关的 文件上的初始列表或查看预计算列表 与相关文档关联的邻居。在第二个相关的 文档已确定，然后又有一个问题，即如何 以最佳方式使用其邻居列表，依此类推。我们已经尝试了一些 不同的策略，并发现有一定的改善 在使用邻居列表时，并量化了所获得的性能 在文档的CISI、CRAN、CACM和MED测试集上。一篇论文 对这些结果的描述在媒体上。

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.