Web active retrieval system based on reinforcement learning - LI YIQUN; XIAO XIAN; LIU YANQIONG; LIANG YUXUAN; YANG YANWU; ZHANG WENSHENG (2010)
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-adaptabil