基于机器学习的核电文档个性化推荐系统研究

Research on nuclear power document personalized recommendation system based on machine learning

本文对采用个性化推荐的方式来辅助用户开展文件检索进行研究,根据用户历史搜索记录以及用户网站行为日志进行分析来推荐用户想要的搜索结果,变被动搜索为主动推荐。文章从推荐系统的建设思路、总体架构设计、数据采集来源分析、数据处理策略、推荐引擎的模型设计、机器学习计算框架选择几个部分来开展研究。重点阐述了基于文件的协同过滤算法叠加基于图的推荐模型的算法核心。通过计算文件之间的相似度,并根据文件的相似度以及用户的历史行为生成推荐列表,再根据岗位、知识点等实体关联所建立的关系图来对推荐结果进行过滤、排序。通过开展基于机器学习的文档个性化推荐研究,为基于大数据及人工智能技术的文档及信息资源开发利用做了有益的探索。

This paper applies the personalized recommendation method to assist users to carry out file retrieval research,and based on user history search records and user website behavior log to analyze the search results that users want,and passive search is active recommendation. The article selects several parts from the construction of the recommendation system,the overall architecture design,the data collection source analysis,the data processing strategy,the recommendation engine model design,and the machine learning computing framework. The algorithm core of the file-based collaborative filtering algorithm superimposed graph-based recommendation model is emphasized. By calculating the similarity between the files,and generating a recommendation list according to the similarity of the files and the historical behavior of the user,the recommendation results are filtered and sorted according to the relationship diagrams established by the entity associations such as posts and knowledge points. Through the research on document-based personalized recommendation of machine learning,it has made useful exploration for the development and utilization of documents and information resources based on big data and artificial intelligence technology.