A reinforcement learning agent for personalized information filtering
A reinforcement learning agent for personalized information filtering
复制标题
用于个性化信息过滤的强化学习代理
DOI：
10.1145/325737.325859
复制
发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Byoung
中科院分区：
文献类型：
--
作者：
Young;Byoung
本文介绍了一种基于Web的个性化信息过滤系统WAIR中用户兴趣的学习方法。所提出的方法分析用户的反应所呈现的文件，并从中学习个人用户的配置文件。强化学习用于调整用户配置文件中的术语权重，以便最好地表示用户的偏好。传统的相关反馈方法需要明确的用户反馈，相比之下，我们的方法学习用户的偏好隐式从直接观察用户的行为在交互过程中。已经进行了现场测试，其中涉及7个用户在4周内阅读总共7，700个HTML文档。与现有的相关反馈方法相比，该方法在个性化信息过滤方面表现出了上级的性能。
This paper describes a method for learning user's interests in the Web-based personalized information filtering system called WAIR. The proposed method analyzes user's reactions to the presented documents and learns from them the profiles for the individual users. Reinforcement learning is used to adapt the term weights in the user profile so that user's preferences are best represented. In contrast to conventional relevance feedback methods which require explicit user feedbacks, our approach learns user preferences implicitly from direct observations of user behaviors during interaction. Field tests have been made which involved 7 users reading a total of 7,700 HTML documents during 4 weeks. The proposed method showed superior performance in personalized information filtering compared to the existing relevance feedback methods.