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Our work proposes a novel fast clustering-based Recommendation method (denoted as FCR) designed on top of Apache Spark.
This greatly inspired researchers to improve the performance of recommender systems or adapt them in the context of big data for handling large-scale datasets.
This paper proposes a novel recommender system based on a cluster ensemble technique for big data. The proposed system incorporates the ... [Show full abstract] ...
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Oct 18, 2021 · We have proposed two correlation clustering algorithms (RBACC and LGBACC), that are based on finding Spearman's rank correlation coefficient among data points.
Dec 1, 2021 · we propose two correlation clustering algorithms based on entirely different techniques. The first one is based on calculating mathematical ...
This article provides a general overview of modern approaches to recommender system design using clustering as a preliminary step to improve overall performance ...
Oct 31, 2023 · Clustering is a machine learning technique that aims to find patterns and similarities among data points and group them into clusters based on some criteria.
A distributed group recommendation system based on extreme gradient boosting and big data technologies · Computer Science. Applied Intelligence · 2019.
Feb 5, 2023 · Clustering algorithms are an important part of recommendation systems and play a significant role in personalizing recommendations for users.
Missing: Fast | Show results with:Fast
Jun 19, 2024 · Clustering-based recommendation systems offer a scalable, efficient, and personalized approach to making recommendations. By grouping users or ...
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