SBIR Phase I: An Intelligent World-Wide Web Agent that Learns User Profiles to Find Relevant Information

这个来自非常规智慧的SBIR一期项目旨在为万维网（WWW）建立一个推荐系统。在WWW上查找相关信息变得越来越困难。大多数当前的搜索引擎产生了太多不相关的信息，因为他们搜索语法。该提案展示了如何通过三个关键创新来产生更准确和与个人相关的搜索推荐：一种学习和结合用户信息（例如人口统计和评级）与WWW源信息（例如网页特征）的方法；利用WWW上人类分类信息源的力量的方法；还有一种学习大型特征的方法，也就是通过对这些特征集运行机器学习算法来学习大型同构特征集。在非常规智慧的框架中，人们通过他们的使用模式隐式地为网页分配语义，机器作为这些语义的转运者，将这些语义传递给其他用户，这样检索就可以基于用户兴趣隐含的语义。这使得根据用户的兴趣检索复杂的多媒体信息成为可能。非常规智慧提供了一种解决电子商务中重大机遇的技术。由于网站可以包含诸如文本、音频、视频或目录项目等信息，因此非传统智慧设想的系统在WWW和军事或企业内部网的应用上是灵活的。本项目所处理的研究和发展领域对一般的知识管理具有重要的意义，例如在开发处理多种类型的信息选择的打包推理机制以及在具有相关信息需求和价值的人员之间产生和支持关系的系统中。

This SBIR Phase I project from Unconventional Wisdom aims to build a recommendation system for the World Wide Web (WWW). Finding relevant information on the WWW is becoming increasingly difficult. Most current search engines produce too much irrelevant information because they search syntactically. This proposal shows how to produce more accurate and personally-relevant search recommendations through three key innovations: a method to learn and combine user information (e.g. demographics and ratings) with WWW source information (e.g. web page features); a method to leverage the power of human-categorized information sources on the WWW; and a method to learn mega-features, that is, large sets of homogeneous features through running a machine-learning algorithm over the sets. In Unconventional Wisdom's framework, people implicitly assign semantics to web pages by their pattern of usage and machines act as the transporters of those semantics to other users so that retrieval can be based on semantics implicit in the user's interests. This makes it possible to retrieve complex multimedia information based on the user's interests.Unconventional Wisdom proffers a technology addressing a significant opportunity in e-commerce. Since a web site can contain information such as text, audio, video or catalog items, the system envisioned by Unconventional Wisdom is flexible in its application to both the WWW and military or corporate Intranets. The area of research and development that this project treats has important implications for knowledge management in general, such as in developing packaged inference mechanisms to address information selection of many types and in systems to generate and support relationships among persons with related information requirements and values.