Pre-processing approaches for collaborative filtering based on hierarchical clustering

• Ano de defesa: 2018
• Autor(a) principal: Aguiar Neto, Fernando Soares de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Agrupamento de Dados; Clustering; Dissertação; Dissertation; Optimization; Otimização; Recommender systems; Sistemas de recomendação
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-25032019-102215/

Recommender Systems (RS) support users to find relevant content, 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 these problems effectively and efficiently by segmenting the data into a number of similar groups based on predefined characteristics. Although these 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 work, 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 can be applied prior to any recommendation system. Our experiments have shown promising recommendation results in the context of nine well-known public datasets from different domains.