Content-aware Recommender Systems using Semantic Analysis

每天，数以百万计的新闻故事被在线阅读，越来越多的读者倾向于只在内容提供商的页面上阅读一篇新闻。这对网络媒体的收入产生了重大影响。Digital Cavalier Technology Services Inc.（该公司）提供个性化企业解决方案，生成内容推荐，鼓励用户阅读更多故事。与维多利亚大学合作开发了一个语义推荐系统。该公司希望通过更准确地处理单词歧义来增强这个系统，包括一个主题模型来更有效地搜索高维空间，并引入一个学习排序算法。通过这些改进，公司将产生可申请专利的知识产权，并创造额外的融资/创收机会。该公司的研发团队将利用通过此次合作获得的专家知识，以该研究项目的成果为基础，开发衍生技术，如自动关键字标签，以及需求驱动的出版。

Every day millions of news stories are read online with an increasing tendency for readers to stay on thecontent provider's page for only one story. This has a significant impact on the revenue of online media outlets.Digital Cavalier Technology Services Inc. (the company) provides personalization enterprise solutions thatgenerate content recommendations encouraging users to read additional stories. In collaboration with theUniversity of Victoria a semantic recommender system has been developed. The company would like toenhance this system by more accurately handling word ambiguity, including a topic model to more efficientlysearch high dimensional spaces, and by introducing a learning-to-rank algorithm. From these enhancements thecompany will generate patentable IP and create additional funding/revenue generation opportunities.The company's R&D team will build upon the deliverables of this research project using the expert knowledgegained through this engagement to develop spin-off technologies such as automatic keyword tagging, as well asdemand-driven publishing.