Reaction type search method and contents correlation technique based on contents relativity

Abstract

The invention discloses a reaction type search method and a context correlation method based on context correlation. The method comprises the following steps: when a enquiry request is received, a original enquiry result set is generated by using a main search engine; after current user sees a enquiry result and point-hits a target web, the target web's ID is obtained, and relativity of all web in the original enquiry result set and the target web is queried from a web relative matrix K; and a web which has greatest relativity with the target web is used as a new enquiry result to submit the user. Comparing with prior art, the invention avoids to learn complex ranking function of query-sensitive, cancels search class concept, and replaces with web grade relative analysis to a solve grain size-ascription problem of category classification; the method does not need an action of tracking a particular user in long term comparing with a configured file tracking method based on user selfhood; comparing with a direct optimization search result's method based on point-hitting data, the method can effectively solve problems such as one meaning with two or more words and one word having two or more meanings.

Description

technical field [0001] The present invention relates to a content management system using computer technology and its realization method, in particular to a method for realizing correlation mining of webpage content under the framework of a feedback search engine. Background technique [0002] With the rapid development of the Internet, search engines have become the most important way for WEB users to obtain network resources. At present, mainstream search engines mainly generate relevant query results based on the occurrence frequency of query words input by users in web pages, supplemented by information such as the authority of web pages. However, because the keywords submitted by WEB users are generally very short and may have ambiguity, the search engine cannot determine the webpage required by the user, which reduces the accuracy of the search results and affects the pertinence of the retrieved information (including search and search). A comprehensive evaluation of ...

AI Technical Summary

Problems solved by technology

However, it has the following problems: on the one hand, in order to submit a set of most pertinent search results to users, it is often necessary to use a very fine classification granularity for pre-classification processing, but the fine classification granularity often leads to a large number of classification crossovers.

The disadvantage of this method is: for general search engines, when encountering ambiguous query keywords; this method ignores the problem of "polysemous words", making the relevance of links and query sentences more dependent on attention The number of people with this link, so the search results will be missing

Due to the potentially infinite number of possible queries, it is difficult to effectively learn the ranking function in the large-scale open environment of actual search engines.

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