Leveraging Existing Technologies to Improve Large Scale Recommender Systems

access_time 10 de dezembro de 2018 às 14:30 até 10 de dezembro de 2018 às 15:30
place IST Alameda - Sala 0.20, Pavilhão Informática II

Research on Recommendation Systems (RS) demonstrates that, although increasingly complex techniques can improve results, it is not often that the authors show concern on how such techniques can be implemented on a large scale. Within this context, this thesis intends to approach the following hypothesis: on a traditional ratings-based RS implementation, we can replace the rating by a more informative value, such that, when a traditional recommendation algorithm is applied, higher accuracy results can be obtained. Thus, our goal is to achieve the effectiveness levels of current state-of-the-art approaches, while maintaining the efficiency and practical usability of the already existing, highly scalable, software frameworks.

local_offer Prova de CAT
person Candidato: André Filipe Caldaça da Silva Carvalho N.º 76593
supervisor_account Orientador 1: Prof. Pável Pereira Calado
supervisor_account Orientador 2: Prof. João Paulo Baptista de Carvalho