MIRA: A Computational Neuro-Based Cognitive Architecture Applied to Movie Recommender Systems
This work applies a neuroscience-inspired cognitive model to movie recommendation, showing incremental improvements in precision for users in recommendation systems.
The authors tackled the problem of movie recommendation by developing MIRA, a cognitive architecture inspired by the LIDA model, which achieved precision percentages similar to traditional models under the same conditions and improved precision when tested with volunteers using small data volumes.
The human mind is still an unknown process of neuroscience in many aspects. Nevertheless, for decades the scientific community has proposed computational models that try to simulate their parts, specific applications, or their behavior in different situations. The most complete model in this line is undoubtedly the LIDA model, proposed by Stan Franklin with the aim of serving as a generic computational architecture for several applications. The present project is inspired by the LIDA model to apply it to the process of movie recommendation, the model called MIRA (Movie Intelligent Recommender Agent) presented percentages of precision similar to a traditional model when submitted to the same assay conditions. Moreover, the proposed model reinforced the precision indexes when submitted to tests with volunteers, proving once again its performance as a cognitive model, when executed with small data volumes. Considering that the proposed model achieved a similar behavior to the traditional models under conditions expected to be similar for natural systems, it can be said that MIRA reinforces the applicability of LIDA as a path to be followed for the study and generation of computational agents inspired by neural behaviors.