Toward Personalize User-Based Recommendation System For Big Data Application

Nasim Kothiwale,Prof. Shrihari Khtawkar


The use of web are increases the option of user choices also increases. Recommendation system is guideline for users in personalized way to choose/choice their option from the large space datasets. Large volume dataset is refers to “Big Data”, Big data is nothing but the data which is beyond the capacity of storing, managing and processing within a short time period. In this paper purposes the personalized user-based recommendation system in which the movies recommendation list is generated as per user interest. In previous service recommendation system collaborative filtering algorithm is adopted but they are faces problem with scalability and inefficiency at the time of data retrieval. The existing service recommendation systems are fails to meet user requirements because without considering users preferences/interest’s it display same ratings and rankings to different users. Also in traditional recommendation system yielding the big data discovery and analysis problem. In purposed system ratings or features are used to filtering the information by applying multi criteria selection policy. Basically to manage and solve scalability and efficiency problem Hadoop is broadly adopted distributed computing platform with MapReduce parallel processing environment. Finally, Experiment is conducted on real-world data set and results demonstrate the accuracy, efficiency and scalability to improve recommendations.

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