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Online book recommendation system by using collaborative filtering and association mining

机译:协同过滤和关联挖掘的在线图书推荐系统

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Recommendation systems is used for the purpose of suggesting items to purchase or to see. They direct users towards those items which can meet their needs through cutting down large database of Information. A various techniques have been introduced for recommending items i.e. content, collaborative and association mining techniques are used. This paper solves the problem of data sparsity problem by combining the collaborative-based filtering and association rule mining to achieve better performance. The results obtained are demonstrated and the proposed recommendation algorithms perform better and solve the challenges such as data sparsity and scalability.
机译:推荐系统用于建议要购买或查看的物品。他们通过减少大型信息数据库将用户引导到那些可以满足其需求的项目上。已经引入了用于推荐项目的各种技术,即,使用了内容,协作和关联挖掘技术。本文通过将基于协作的过滤和关联规则挖掘相结合来解决数据稀疏性问题,以获得更好的性能。证明了获得的结果,所提出的推荐算法性能更好,并解决了诸如数据稀疏性和可伸缩性之类的挑战。

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