基于网络抓取和自然语言处理的技术推荐系统

Hima Bindu Ankem Venkata, Andrea Calazacon, Taha M. Mahmoud, T. Hanne
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引用次数: 0

摘要

本研究的目标是开发一个推荐系统的原型,该系统可以帮助没有深入了解技术产品和服务的个人和组织选择最适合他们需求的适当工具/服务。众所周知,推荐系统是一种个性化的信息过滤系统,通常集成到各种消费者和商业应用中。这些个性化的系统起着至关重要的作用,特别是当用户不确定要搜索什么时。我们的工作重点是基于web的推荐系统,为学术界和工业界的最终用户提供软件工具和服务的推荐。本文的重点是使用Apache Nutch从网络中提取信息,Apache Nutch是一个开源的网络爬虫,它从广泛用于软件推荐的网站中提取数据。提取的信息在Elasticsearch中建立索引,Elasticsearch应用自然语言处理(NLP)和文本挖掘功能为最终用户提供适当的建议。Kibana仪表板和可视化用于以有利于最终用户的格式将建议可视化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Technology Recommender System Based on Web Crawling and Natural Language Processing
The goal of this study is to develop a prototype for a recommendation system that could assist individuals and organizations without in-depth knowledge of technology products and services to choose an appropriate tool/service that best suits their needs. Recommendation systems, as they are popularly known, are personalized information filtering systems which are usually integrated into various consumer and commercial applications. These personalized systems play a vital role, especially when the user is unsure of what to search for. Our work focuses in particular on web-based recommendation systems for end-users in academia and industries who need recommendations for their software tools and services. This paper focuses on extracting information from the web using Apache Nutch, an open-source web crawler which extracts data from websites widely used for software recommendations. The information extracted is indexed in Elasticsearch, whose Natural Language Processing (NLP) and text mining capabilities are applied to provide appropriate recommendations to the end-users. Kibana dashboards and visualizations are used to visualize the recommendations in a format that is conducive for the end-users.
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