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Addressing Cold Start Problem in Recommendation System Using Custom Built Hadoop Ecosystem

机译:使用自定义构建的Hadoop生态系统来解决建议系系统中的冷启动问题

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In the modern digital era, the most common problem is being surrounded by the e-Commerce and social networking sites which provides accurate and timely recommendations based on user interests. When we have user-related data, it will be easier to give recommendations to those users based on their previous activity in that particular website. But cold start problem arises when there is no information related to a user or an item, because they are new to that particular website. Hadoop is one of the distributed huge data processing framework that provides an effective solution to overcome these cold start problems in real time scenarios. Here, we have used the tools like Sqoop, Hive and build a model that address and resolve certain cold start issues in recommendation systems.
机译:在现代数字时代,最常见的问题被电子商务和社交网站包围,基于用户兴趣提供准确和及时的建议。当我们有与用户相关的数据时,将根据在该特定网站上以前的活动向这些用户提供建议。但是,当没有与用户或项目相关的信息时,会出现冷启动问题,因为它们是该特定网站的新功能。 Hadoop是分布式巨大的数据处理框架之一,提供了有效解决方案,以克服实时场景中的这些冷启动问题。在这里,我们使用了SQOOP,Hive等工具,并构建了一个模型,该模型解决了推荐系统中的某些冷启动问题。

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