Banca de DEFESA: AMANDA CHAGAS DE OLIVEIRA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : AMANDA CHAGAS DE OLIVEIRA
DATE: 28/11/2022
TIME: 09:00
LOCAL: meet.google.com/hnh-hipf-dzq
TITLE:

A Recommendation Model for Groups Using Diversification Techniques

KEY WORDS:

Recommendation Model, Groups, Diversification

PAGES: 90
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Metodologia e Técnicas da Computação
SPECIALTY: Sistemas de Informação
SUMMARY:

Recommender Systems traditionally recommend items to individual users. However, there are scenarios where individuals gather in groups, and with that arises the need to recommend to groups. Most of these groups form naturally, for example, to watch a movie, have lunch at a restaurant, or even plan a trip. In all these hypotheses, it is possible to use Recommender Systems to offer personalized information to the group as a whole. To do so, it is necessary to consider the individual preferences of the group members, to satisfy them fully, and, in this sense, to use techniques for aggregating this information. Although there are consensus techniques for aggregating information, the recommendations can be repetitive among themselves, as they will always serve the same group profile. This inconvenience sets a precedent for adopting diversity techniques for recommendations to the group. In this work, we investigate how to apply such diversification techniques in group recommendations, based on the members' preferences, to avoid over-specialization of the system and thus keep the group members satisfied in general. To do so, it is necessary to develop a group formation model and then model how the recommendation will be carried out to be able to apply diversification techniques to the items to be recommended. Two experiments were carried out in this research, considering related work as a baseline. In the offline experiment, the result of the proposed model was slightly higher than the baseline, having been 3.8% more accurate and 1.8% more diverse. In the second experiment, the online experiment, the proposed model completely dominated the baseline in terms of accuracy and was slightly inferior in terms of diversity.

COMMITTEE MEMBERS:
Presidente - 2011187 - FREDERICO ARAUJO DURAO
Interno - 4769482 - IVAN DO CARMO MACHADO
Interno - 2357676 - DANILO BARBOSA COIMBRA
Externa à Instituição - RAMON PEREIRA LOPES - UFRB
Externa à Instituição - NATASHA CORREIA QUEIROZ LINO