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Hybrid Recommendation System with Collaborative Filtering and Association Rule Mining Using Big Data

机译:大数据协同过滤和关联规则挖掘的混合推荐系统

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The large amount of increase in information available over the Internet has created a greatest challenge in searching useful information. As a result an intelligent approach such as recommendation system is used that can recommend everything from movies, books, music, restaurant, news and jokes that can efficiently retrieve useful information from web. Collaborative filtering is primary approach of any RS. But only CF cannot provide enough scalability and accuracy. This paper presents a model that combines RS method such as CF with big data technique such as association rule mining. The main focus of this paper is to provide a scalable and robust recommendation system that can provide good accuracy. In our work, we have proposed conduction of a personalized movie recommendation by considering user's past behavior.
机译:Internet上可用信息的大量增加给搜索有用信息带来了最大的挑战。结果,使用了诸如推荐系统之类的智能方法,该方法可以推荐电影,书籍,音乐,餐厅,新闻和笑话中的所有内容,从而可以从网络中有效检索有用的信息。协作过滤是任何RS的主要方法。但是只有CF无法提供足够的可伸缩性和准确性。本文提出了一种模型,该模型将诸如CF之类的RS方法与诸如关联规则挖掘之类的大数据技术相结合。本文的主要重点是提供一种可提供良好准确性的可伸缩且强大的推荐系统。在我们的工作中,我们建议通过考虑用户的过去行为来进行个性化电影推荐。

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