Query click graph-based search recommendation model optimization

Abstract

The invention discloses query click graph-based search recommendation model optimization. Compared with the prior art, the query click graph-based search recommendation model optimization is realized through the following steps of: firstly, analyzing a search behavior and an intention of a user, researching a data extraction method and an expression of the search behavior, and proposing a user query log-based query term correlation method through deeply mining a query session; and secondly, analyzing a theory and a calculation method of a traditional query click bipartite graph recommendation model. A query click bipartite graph is simple in structure, strong in practicability and independent of search term and webpage similarity calculation in the implementation process, so that the query click bipartite graph can be widely applied to search engines. According to the query click graph-based search recommendation model optimization, a clicking frequency is utilized to replace the number of clicks to construct a weight of a middle edge of the bipartite graph, so that the weight is prevented from being offset due to overmuch invalid clicks and the recommendation system can achieve a stable state as far as possible. Finally, the superiority of an improved model is proved in three aspects through experiments and data analysis.

Description

technical field [0001] The invention relates to an image model optimization scheme, in particular to a search recommendation model optimization based on a query click graph. Background technique [0002] Many scholars have conducted research and analysis on user search logs, mainly establishing query recommendation models from the aspects of query word association and query click bipartite graph. Due to the different levels of knowledge of users, as well as random submission of non-standard query words and clicks on irrelevant query results during search operations, there are a large number of inaccurate, non-standard and non-representative query information in the query log. Over time, these inaccurate information will gradually accumulate. If traditional recommendation methods are used to mine and understand these inaccurate information, inaccurate or unacceptable queries will be recommended. Therefore, in the era of big data, mining accurate, representative and high-qual...

AI Technical Summary

Problems solved by technology

Due to the different levels of knowledge of users, as well as random submission of non-standard query words and clicks on irrelevant query results during search operations, there are a large number of inaccurate, non-standard and non-representative query information in the query log. Over time, these inaccurate information will gradually accumulate. If traditional recommendation methods are used to mine and understand these inaccurate information, inaccurate or unacceptable queries will be recommended

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