Iterative method and system for optimizing search and satisfaction improvement based on user clicks

Abstract

The invention provides an iterative search optimization and satisfaction degree promotion method and system based on user clicking and belongs to the searching optimization field. The iterative search optimization and satisfaction degree promotion method based on the user clicking comprises step A, extracting clicked pages of a keyword from a query log according to a search keyword, extracting and clicking keywords of the pages from the query log, repeating the above process until convergence, building the keywords and corresponding clicked pages into a bipartite graph, performing the bipartite graph as input and performing a similarity iterative calculation according to an IA-CLICK (Iterative Algorithm) method; step B, reordering search results according to an obtained relevance of query keywords provided by a user and the pages through a relevance R (q,d) measurement formula based on a page similarity assemble obtained from the step A; and step C, performing a search evaluation according to a clicking situation of reordered search results of users and a user satisfaction US measurement formula. The iterative web search optimization and satisfaction degree promotion method and system based on the user clicking has the advantages of enabling user burden to be small, being strong in relevance and simple and efficient in evaluation frame and enabling reality and flexibility of method effects to be improved.

Description

technical field [0001] The present invention relates to the field of optimized search, in particular to an iterative user click-based optimized search and satisfaction improvement method and system. Background technique [0002] At present, in the field of optimized search, there are many methods to achieve optimized search, but there is no authoritative method for user satisfaction; there is a method for optimized search that uses user query logs, which is closer to the real choice of users, but directly uses Query logs are weakly correlated with search results. In the document [Optimizing Web Search Using Web Click-through Data], an Iterative Algorithm (referred to as IA) algorithm for search optimization is proposed. IA algorithm is a classic search optimization algorithm. The IA algorithm considers both query similarity and page similarity, which can introduce related pages to a certain extent and filter noise information; while iteratively enhances the similarity betw...

AI Technical Summary

Problems solved by technology

Its disadvantage is that it does not fully consider the influence of the weight between the page and the query, resulting in an excessively enhanced similarity in the iterative process, and the introduction of too much noise data, which is not conducive to algorithm convergence and subsequent calculations

However, most existing user satisfaction evaluation methods have complex analysis frameworks, which are difficult to meet the performance requirements of search engines.

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