Exploration of text mining techniques for automatic acquisition of contextual information for Context-Aware recommendation systems

• Grant number: 13/16039-3
• Support Opportunities: Scholarships in Brazil - Master
• Start date: November 01, 2013
• End date: February 28, 2015
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Solange Oliveira Rezende
• Grantee: Camila Vaccari Sundermann
• Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

Nowadays, users have difficulties in choosing products and services on the Web due to the large variety of options. Recommender systems are designed to assist users to identify items of interest in a set of options. Most approaches for recommendation systems focus on recommending items that are most relevant to individual users, not taking into account the context of the users. However, in many applications, it is also important to consider contextual information to make recommendations. For example, a user may want to watch a movie with his girlfriend on Saturday night or with your friends during a weekday, and a movie rental store on the Web can recommend different types of movies for this user depending on his/her context. A major challenge for the use of context-aware recommendation systems is the lack of methods for automatic acquisition of contextual information for these systems. In this scenario, the goal of this project is to explore some text mining techniques to acquire contextual information for context-aware recommendation systems. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

SINOARA, ROBERTA A.; SUNDERMANN, CAMILA V.; MARCACINI, RICARDO M.; DOMINGUES, MARCOS A.; REZENDE, SOLANGE O.; ALMEIDA, A; BERNARDINO, J; GOMES, EF. Named Entities as Privileged Information for Hierarchical Text Clustering. PROCEEDINGS OF THE 18TH INTERNATIONAL DATABASE ENGINEERING AND APPLICATIONS SYMPOSIUM (IDEAS14), v. N/A, p. 10-pg., 2014-01-01. (13/16039-3, 13/14757-6, 10/20564-8, 12/13830-9)

MANZATO, MARCELO G.; DOMINGUES, MARCOS A.; FORTES, ARTHUR C.; SUNDERMANN, CAMILA V.; D'ADDIO, RAFAEL M.; CONRADO, MERLEY S.; REZENDE, SOLANGE O.; PIMENTEL, MARIA G. C.. Mining unstructured content for recommender systems: an ensemble approach. INFORMATION RETRIEVAL JOURNAL, v. 19, n. 4, p. 378-415, AUG 2016. (13/22547-1, 13/10756-5, 12/13830-9, 14/08996-0, 13/16039-3)

SUNDERMANN, CAMILA VACCARI; DOMINGUES, MARCOS AURELIO; CONRADO, MERLEY DA SILVA; REZENDE, SOLANGE OLIVEIRA. Privileged contextual information for context-aware recommender systems. EXPERT SYSTEMS WITH APPLICATIONS, v. 57, p. 139-158, SEP 15 2016. (13/16039-3)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

SUNDERMANN, Camila Vaccari. Contextual information extraction using text mining for recommendation systems context sensitive. 2015. Master's Dissertation - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.