大数据时代，从海量的数据中发现对用户有用的知识成为研究领域重要的问题.通过集成多个搜索引擎的查询结果，实现食品安全领域中搜索信息的集成和个性化自适应排序.本文设计基于元搜索技术、知识本体和自适应的排序学习技术，实现多个搜索引擎相关查询结果集成，在对用户点击的标注和知识本体的基础上，利用基于监督学习的排序技术，实现对食品安全领域信息的个性化自适应排序.系统实现了集成多个搜索引擎的食品安全相关知识的提取和相关结果的重新排序.本研究不仅实现了多个搜索引擎食品安全信息查询相关的结果集成，而且能够根据用户的偏好实现结果的自适应排序.
In the era of big data, knowledge discovery from the mass of data is an important research problem, especially for the user's customized knowledge. In this paper, an integrated search system aiming at personalized re-ranking of food safety knowledge system, PROSK for short, is designed and implemented. Firstly, using the existing search engines, the meta-search engine technique is employed for integrating the results of multiple search engines; then according to the results of the users' click through and the ontology of food safety domain, ranking-based learning algorithm is applied to sort search results adaptively according to the preference profiles. The system integrates the agricultural information from multi-engineers and ranks the query results adaptively and intelligently. This study proposes a feasible solution for ranking of information and knowledge of food safety from multi-engineers adaptively.
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中国综合性科技类核心期刊(北大核心)