NectaRSS，一个RSS提要排名系统，隐式学习用户偏好
NectaRSS, an RSS feed ranking system that implicitly learns user preferences
摘要：
本文介绍了一种新的RSS提要排名方法NectaRSS。系统根据用户过去的选择向他/她推荐信息。用户偏好是自动获取的，避免了明确的反馈，排名是基于提取到用户简档的那些偏好。NectaRSS对用户简档和新文档使用众所周知的向量空间模型，并使用信息检索技术对它们进行比较，但引入了一种新的方法来创建用户简档，并根据用户过去的选择进行调整。所提出的方法的效率进行了测试，将其嵌入到一个智能聚合器（RSS提要阅读器），这已被不同的和异构的用户使用。此外，本文证明了NectaRSS产生的新闻网站的排名提高其质量与用户的选择，其优越性比其他算法，使用不同的信息表示方法。
英文摘要：
In this paper a new RSS feed ranking method called NectaRSS is introduced. The system recommends information to a user based on his/her past choices. User preferences are automatically acquired, avoiding explicit feedback, and ranking is based on those preferences distilled to a user profile. NectaRSS uses the well-known vector space model for user profiles and new documents, and compares them using information-retrieval techniques, but introduces a novel method for user profile creation and adaptation from users' past choices. The efficiency of the proposed method has been tested by embedding it into an intelligent aggregator (RSS feed reader), which has been used by different and heterogeneous users. Besides, this paper proves that the ranking of newsitems yielded by NectaRSS improves its quality with user's choices, and its superiority over other algorithms that use a different information representation method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
