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Competitive product level theme preference mining method

A theme and level technology, applied in the field of theme preference mining at the competitive product level, can solve problems such as low efficiency, slow speed of key themes, and inability to expand to multiple sets of competing products, achieving the effect of efficient scalability and easy expansion.

Active Publication Date: 2021-06-04
HEFEI UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this work only provides a comparison of two competing products in the subject and cannot be extended to multiple groups of competing products
In addition, existing methods learn model parameters through the Gibbs sampling algorithm, but in large-scale online user-generated data, mining key topics is slow and inefficient due to the need for tens of thousands of iterations

Method used

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  • Competitive product level theme preference mining method
  • Competitive product level theme preference mining method
  • Competitive product level theme preference mining method

Examples

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Embodiment Construction

[0058] In the present embodiment, a topic preference mining method of a competition level combines the textual content of the user's comment, the product set of the product is designed, and the subject preference mining method of the competition level, considering the user's limited attention to information and products. Introducing the Bernoulli hybrid mechanism to distinguish between the topic and background topics related to competition, using collapsed Gibs sampling methods to approximate the model, suitable for discovering potentially competitive fine molecular markets and their corresponding themes, thus excavating excavation User focus on competitive products focus on competitive products. Specifically, it is performed as follows:

[0059] Step 1, build the user data collection;

[0060] Step 1.1, build the product set composed of E a different product that all users commented, recorded as e = {e 1 , E 2 , ..., e m , ..., e M }, Where E m Represents the product set of the m...

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Abstract

The invention discloses a competitive product level theme preference mining method. The method comprises the following steps: 1, constructing and representing a user data set; 2, modeling a competitive sub-market, a competition-related theme and a background theme; 3, modeling limited attention of a user, 4, constructing a parametric Bayesian model, and 5, carrying out parameter inference by utilizing a collapse type Gibbs sampling algorithm. When large-scale user generated contents are dealt with, the competitive sub-markets and themes corresponding to the competitive sub-markets can be effectively, quickly and accurately recognized, and enterprises can quickly recognize competitors and insight into focus topics concerned by users on competitive products.

Description

Technical field [0001] The present invention relates to the field of competitive submarine identification and the topic mining technology of the competition submarine, and specific to a topic preference mining method of a competition level. Background technique [0002] In recent years, with the popularity of online networks, forums, blogs, search engines and other social media have become an important way to establish links with the real world, and record the data created by users in many ways, which contain various comparison information. . The company hopes to compete from the contents of users to make competitive intelligence analysis, which may determine potential competitors that may threaten their brands or products, and can also inspect the focus theme of competitive products from fine-grained perspective. [0003] In recent years, there have been more and more research from online users to produce competitive intelligence analysis. For example, literature [USING FAVORITE...

Claims

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Application Information

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IPC IPC(8): G06F16/33G06F16/335G06F16/953G06Q30/02
CPCG06F16/334G06F16/335G06F16/953G06Q30/0201
Inventor 钱洋周凡姜元春刘业政孙见山柴一栋梁瑞成井二康陶守正周永行
Owner HEFEI UNIV OF TECH
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