Pre-processing approaches for collaborative filtering based on hierarchical clustering

Author(s)

Fernando Soares de Aguiar Neto

• Document type: Master's Dissertation
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2018-10-19

Examining board members

André Carlos Ponce de Leon Ferreira de Carvalho; Gustavo Enrique de Almeida Prado Alves Batista; Marcos Aurelio Domingues; Marcos André Gonçalves

• Advisor: Marcelo Garcia Manzato; Ricardo José Gabrielli Barreto Campello

Abstract

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. (AU)

• FAPESP's process: 16/04798-5 - Pre-Processing Approaches Applied to Data Clustering Based Recommender Systems.
• Grantee: Fernando Soares de Aguiar Neto
• Support Opportunities: Scholarships in Brazil - Master