A Recommendation Engine based on Adaptive Automata
Paulo Roberto Massa Cereda, João José Neto · 2015
The amount of information available nowadays is huge and in raw state; systems have to act proactively on selecting and presenting context-relevant information, but such feature is time-consuming an exhaustive. This paper presents a recommendation engine based on an adaptive rule-driven device – namely, an adaptive automata – as a lightweight scalable alternative to usual approaches on resource recommendation. The technique employed here is based on frequency analysis instead of relying on usual machine learning.