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

来自Unconventional Wisdom的SBIR第一阶段项目旨在为万维网（WWW）构建一个推荐系统。 在万维网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太多的无关信息，因为他们搜索的句法。 该提案展示了如何通过三个关键创新产生更准确和个人相关的搜索推荐：学习和联合收割机用户信息的方法（例如人口统计和评级）与WWW源信息（例如网页功能）;一种利用万维网上人类分类信息源的力量的方法;以及一种通过在集合上运行机器学习算法来学习大特征（即，大的同类特征集合）的方法。在Unconventional Wisdom的框架中，人们通过其使用模式隐式地将语义分配给网页，并且机器充当将这些语义传输给其他用户的传输器，使得检索可以基于用户兴趣中隐式的语义。 这使得根据用户的兴趣检索复杂的多媒体信息成为可能。Unconventional Wisdom提供了一种解决电子商务中重大机遇的技术。 由于一个网站可以包含诸如文本、音频、视频或目录项等信息，因此Unconventional Wisdom设想的系统在其应用于WWW和军事或公司内部网时是灵活的。本项目所涉及的研究和开发领域对一般的知识管理具有重要意义，例如开发成套的推理机制，以解决多种类型的信息选择问题，并在系统中生成和支持具有相关信息要求和价值观的人之间的关系。

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.