Recommendation system for big data applications based on set similarity of user preferences

Recommender system techniques are software techniques to provide users with tips on the object they need to devour or the item they want to apply. The conventional approach is to consider this as a decision problem and to solve it using rule based techniques, or cluster analysis. But recommendation systems are mainly employed in applications such as online market, which works with big data. Since, performing data mining on big data is a tedious task due to its distributed nature and enormity, instead of data mining, another method known as set-similarity join can be utilized. This paper proposes a solution for item recommendation for big data applications. The proposed work presents customized and personalized item recommendations and prescribes the most suitable items to the users successfully. In particular, key terms are used to indicate users preferences, and a user-based collaborative filtering algorithm is embraced to create suitable suggestions. Proposed work is designed to work with Hadoop, a broadly chosen distributed computing platform using the MapReduce framework

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