Low precision searches:
The vast majority of publicly available search engines adopt a so-called query-list paradigm , whereby in response to a user's query the search engine returns a linear ranking of documents matching that query. The higher on the list, the more relevant to the query the document is supposed to be. While this approach works efficiently for well-defined narrow queries, when the query is too general, the users may have to sift through a large number of irrelevant documents in order to identify the ones they were interested in. This kind of situation is commonly referred to as a low precision search .
As more than 60% of web queries consist of one or two words, which inevitably leads to a large number of low precision searches. Several methods of dealing with the results of such searches have been proposed. One method is pruning of the result list, ranging from simple duplicate removal to advanced Artificial Intelligence algorithms. The most common approach, however, is relevance feedback , whereby the search engine assists the user in finding additional key words that would make the query more precise and reduce the number of returned documents. An alternative and increasingly popular method is also search results clustering
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