Web active retrieval system based on reinforcement learning

A technology of reinforcement learning and web pages, applied in special data processing applications, instruments, electronic digital data processing, etc., can solve problems such as poor adaptive ability and lack of learning ability, and achieve the effect of high accuracy and convenient use.

Inactive Publication Date: 2010-06-23
INST OF AUTOMATION CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology improves on existing systems used for recommending content based on their relevance or importance towards certain types of people (users). It allows individuals who have similar interests to recommend relevant documents quickly through an algorithm called Q-Learning. Additionally, it suggests customizable filters tailored specifically to each individual personality profile, allowing them to receive suggestions about specific topics related to those profiles without being overwhelmed up too much. Overall, this technology helps improve efficiency and effectiveness across various domains such as social media networks.

Problems solved by technology

This patents describes various technical means involved with improving the performance of artificial agents or systems like reactive training machines during their environments. These improvements include optimizing actions over time, exploring alternative strategies, developing models capable of better understanding complex situations, enhancing learning capabilities, and providing feedback signals to optimize future behaviors.

Method used

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  • Web active retrieval system based on reinforcement learning
  • Web active retrieval system based on reinforcement learning
  • Web active retrieval system based on reinforcement learning

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Embodiment Construction

[0022] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0023] Such as figure 1 as shown, figure 1 It is an overall logical block diagram of the active Web retrieval system provided by the present invention, including an original input terminal and a page recommendation output terminal. Wherein, the original input terminal is used to send the original user request to the web page (Web) search Agent module, and the Web search Agent module uses the search engine to search after receiving the user request, and after the search is completed, the Web filter Agent module learns the pages therein and sorting, extracting the page and recommending it to the user through the Web interface Agent module; after the user browses, use the Web interface Agent module to record, obtain the use...

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Abstract

The invention discloses a Web active retrieval system based on reinforcement learning; the system comprises a Web search Agent module, a Web filter Agent module, a Web interface Agent module and a user information learning Agent module; wherein, the Web search Agent module is used for searching subjects based on user interests, analyzing Web content and realizing Web download function; the Web filter Agent module is used for finishing web content analysis, page filtering and classified index; the Web interface Agent module is used for recommending webs on behalf of user interests after learning, receiving the user feedbacks and recording user browsing behaviors and having statistical analysis function; and the user information learning Agent module is used for realizing the interest updates based on reinforced learning, updating the user information continuously and finishing the optimum model on behalf of user interest. The Web active retrieval system based on reinforcement learning has strong self-adaptability, high accuracy and convenient use.

Description

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Claims

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Application Information

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Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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