User interest learning-based vertical search method

Abstract

The invention discloses a user interest learning-based vertical search method. The method specifically comprises the steps of determining interests and preferences of a user according to attribute contents aiming at personal interest demands of the user and filled by the user during registration, and building an initial user interest model; if the user performs screening query according to attribute types, adding attribute type query records to the user personal interest model to finish model updating; calculating interest degree values of the user to all physical resources, and then performing sorting and storage according to the interest degree values from high to low; for a personalized search, performing screening query according to keywords or attributes selected by the user to generate an initial query result list; and for physical resources in the initial query result list, in combination with the interest degree values of the user to the physical resources, sorting the physical resources in the list according to the interest degree values from high to low to generate a final query result list. Through user interest learning, a result consistent with a current interest is actively provided for the user.

Description

technical field [0001] The invention relates to a vertical search method based on user interest learning, in particular to a vertical search method for storing user browsing information or query behavior in a vertical search website, and belongs to the technical field of vertical search. Background technique [0002] Today is an era of information explosion. Information is no longer a scarce resource. With all kinds of information emerging one after another, users' attention has become scarce instead. The further development of search engine technology has given users great convenience. According to the "39th Statistical Report on Internet Development in China" released by China Internet Network Information Center CNNIC, as of December 2016, the number of search engine users in China reached 602 million, with a utilization rate of 82.4%. Smart, vertical specialization has become a development trend. On the one hand, the types of search information are more abundant; on the...

AI Technical Summary

Problems solved by technology

This implies two major problems. On the one hand, it is often difficult for users to clearly summarize their needs. On the other hand, even if the query words can reflect the current needs of users, most vertical search engine websites do not take into account the interests of users. Extract and perform personalized search, or actively make information recommendation services; secondly, in vertical search engines that provide users with social annotation of resources, in general, they are easily affected by spam annotation: malicious users may use a large number of widely used The emergence of tags to improperly enhance its influence has brought great challenges to the validity of search results

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