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LDA model-based search engine result optimization system

A search engine and model technology, applied in the direction of network data indexing, network data retrieval, other database retrieval, etc., can solve the problem of search engines not being able to find, can't input, etc., and achieve the effect of improving search accuracy and efficiency.

Active Publication Date: 2015-01-21
SUZHOU UNIV
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Problems solved by technology

Moreover, users cannot input the entire document as search content into the search engine. On the one hand, if fuzzy matching is performed, too many search keywords will return a lot of meaningless content; on the other hand, if exact matching is performed, the search The engine will not find suitable results

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  • LDA model-based search engine result optimization system
  • LDA model-based search engine result optimization system

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Embodiment

[0025] Such as figure 1 , figure 2 , image 3 , Figure 4 As shown, the search engine result optimization system based on the LDA model, the optimization method is: the user gives a query, uses the search engine, obtains the search engine result, and then uses the document and the search engine result as the input of the LDA model according to the document provided by the user , where the LDA model uses the topic model algorithm. At this time, the LDA model has been trained according to the training set and can be directly used to predict documents; the predicted results can be changed into two kinds of vectors, which are p(k|d ) and p(w|d), by calculating and sorting the similarity between documents, the final results related to user documents can be output.

[0026] The LDA model assumes that a document is the distribution of some topics, and a topic is the distribution of words on the word list. The generation process of a document is as follows, where Dir represents th...

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Abstract

The invention discloses an LDA model-based search engine result optimization system. The optimization method comprises the following steps: giving query by a user and using a search engine to obtain a search engine result; taking files provided by the user and the search engine result as input of an LDA model, wherein the LDA model uses a topic model algorithm, at the moment, the LDA model is trained according to a training set and can be directly used for predicting the files, and the predicted result can be changed into two vectors: p(k / d) and p(w / d); carrying out calculation and sorting through the similarity between the files to output a final result related to the files of the users. According to the LDA model-based search engine result optimization system, the semantic re-matching is carried out on the basis of the existing search engine results to find the search results in which the users are really interested and which are related to the semantic content, so that the search efficiency and the search precision are improved.

Description

technical field [0001] The invention belongs to the technical field of computers and the Internet, and in particular relates to a search engine result optimization system based on an LDA model. Background technique [0002] A search engine refers to a system that automatically collects information from the Internet, corporate intranet, etc., and provides it to users for query after a certain arrangement. In creative work such as paper writing and document arrangement, search engines are often used to search for interesting information from the Internet as proof materials, references or direct information sources for document materials. According to the different search sources of search engines, search engines can be divided into two categories: Internet search engines and intranet search engines. Common Internet search engines include Google, Bing, Baidu, etc. They are all databases created by extracting the information of various websites from the Internet. At present, t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
CPCG06F16/951G06F16/9535
Inventor 严建峰刘志强高阳杨璐曾嘉
Owner SUZHOU UNIV
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