A Method of Search Engine Log Data Mining Oriented to User Information Requirements

Abstract

The invention relates to the field of internet search engine log division, in particular to a method of search engine log data mining facing user information requirements. The method of the search engine log data mining facing the user information requirements comprises the steps of inquiring log block classification, inquiring similarity calculation and user information requirement provision. Search term similarity and search result similarity are calculated comprehensively to be used as query similarity, two queries are judged whether to have the same information requirements or not according to the query similarity, and division of search logs can be carried out effectively and quickly. The method of the search engine log data mining facing the user information requirements has the advantages that aiming at the defect that a traditional search engine quality evaluation method cannot describe complex and vague information requirements of users completely, a search engine user information requirement satisfaction evaluation method based on behavior logs is provided. User information requirements are used as a unit, user satisfaction is evaluated by analyzing search behaviors of users in search engine logs, personal requirements of the users are analyzed, the development of a search engine technology is promoted, and service quality of a search engine is improved.

Description

technical field [0001] The invention relates to the field of Internet search engine log division, in particular to a search engine log data mining method oriented to user information requirements. Background technique [0002] Search engine log research is an indispensable part of the Internet, especially for website optimization, SEO business needs to be done well, and scientific log analysis must be carried out. The user activity information contained in the search engine logs, such as the user's use time, the location of the clicked document, the number of searches, etc., can provide a basis for user behavior analysis and guide the technical improvement of the search engine. Search engine log division is the basis of search engine log research. At present, there are mainly two methods for dividing search engine logs: manual division and automatic division. The manual division method can be divided into user self-report and evaluator manual annotation. [0003] User self...

AI Technical Summary

Problems solved by technology

Compared with manual division, automatic division is simple and fast, but the disadvantage is that the accuracy is not high

[0006] However, the methods mentioned above ignore the fact that when users use search engines, they often perform multiple search activities with information requirements at the same time, which is shown in the search logs as simultaneous search behaviors with multiple search purposes. A complete query activity will be divided into several small pieces and recorded in the search engine log

Traditional methods often divide several small blocks of the same information requirement into multiple search records with different information requirements, which cannot effectively identify such user activity search engine logs with multiple information requirements

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