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• W2946474052 abstract "Recommender Systems (RS) support users to find relevant contents, such as movies, books, songs, and other products based on their preferences. Such preferences are gathered by analyzing past users’ interactions, however, data collected for this purpose are typically prone to sparsity and high dimensionality. Clustering-based techniques have been proposed to handle those problems effectively and efficiently by segmenting the data into a number of similar groups based on predefined characteristics. Although such techniques have gained increasing attention in the recommender systems community, they are usually bound to a particular recommender system and/or require critical parameters, such as the number of clusters. In this paper, we present three variants of a general-purpose method to optimally extract users’ groups from a hierarchical clustering algorithm, specifically targeting RS problems. The proposed extraction methods do not require critical parameters and enable any recommender algorithm to be used at the recommendation step. Our experiments have shown promising recommendation results in the context of nine well-known public datasets from different domains." @default.
• W2946474052 created "2019-05-29" @default.
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• W2946474052 date "2020-09-01" @default.
• W2946474052 modified "2023-10-15" @default.
• W2946474052 title "Pre-processing approaches for collaborative filtering based on hierarchical clustering" @default.
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• W2946474052 doi "https://doi.org/10.1016/j.ins.2020.05.021" @default.
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