Banca de DEFESA: DIOGO VINÍCIUS DE SOUSA SILVA

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : DIOGO VINÍCIUS DE SOUSA SILVA
DATE: 09/06/2022
TIME: 14:00
LOCAL: https://meet.google.com/prv-pkhk-dwq
TITLE:

Using Clustering Techniques and Markov Chains for Long-Tailed Item Recommender Systems

KEY WORDS:

recommender system, long tail, graphs, markov chain, clustering, evaluation survey

PAGES: 127
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Sistemas de Informação
SUMMARY:

Recommendation Systems focus on recommending the relevant items to end-users. Usually, the most important items are the most popular ones. However, the emergence of new forms of content distribution in the most diverse markets implied the emergence of the long tail phenomenon, i.e., the creation of new niche products market. Considering the growth of groups related to niche markets, the study of recommendations in the long tail has become increasingly demanding. Nevertheless, long-tail items naturally tend to generate fewer inputs to information systems such as purchase logs, user feedback, and ratings. As a result, it becomes more difficult to recommend items from the long tail. Motivated by these premisses, the primary purpose of this thesis is to develop and exploit recommendation models capable of leading users to niche items located in the long tail, but at the same time highly relevant ones. For this, two major techniques of clustering and representation of matrices through graphs are explored. The first technique adopts Markov chains to calculate similarities of the nodes of a user-item graph. The second technique applies clustering to the set of items in a dataset. Such a combination aims to give more visibility to the long tail items. Experiments were carried out to evaluate the approach and measure the effectiveness of the recommendations considering the long tail context. Metrics such as recall, diversity, and popularity of the generated recommendations were calculated and compared with techniques whose objectives do not directly cover long-tail recommendations. In addition, a questionnaire applied to experts in the business domain also complemented the evaluation of the proposed approaches. By comparing our proposals against three state-of-the-art baselines, the results show that it is possible to improve the accuracy of the recommendations even by focusing on less popular items, in this case, niche products that form the long tail. The recall in some cases improved by about 27.9%, while the popularity of recommended items has declined. In addition, the recommendations show to contain more diversified items indicating better exploitation of the long tail.

BANKING MEMBERS:
Presidente - 2011187 - FREDERICO ARAUJO DURAO
Interno - 1814369 - CASSIO VINICIUS SERAFIM PRAZERES
Interno - 1850683 - MAYCON LEONE MACIEL PEIXOTO
Interno - 2115562 - RAFAEL AUGUSTO DE MELO
Interna - 1232218 - DANIELA BARREIRO CLARO
Externo à Instituição - RENATO DE FREITAS BULCÃO NETO - UFG
Externo à Instituição - MARCELO GARCIA MANZATO
Externo à Instituição - JOÃO BATISTA DA ROCHA JÚNIOR
Externo à Instituição - PEDRO DE ALCÂNTARA DOS SANTOS NETO