MIRA: A Computational Neuro-Based Cognitive Architecture Applied to Movie Recommender Systems

TL;DR

This paper introduces MIRA, a neuro-inspired cognitive architecture based on the LIDA model, adapted for movie recommendation, demonstrating comparable precision to traditional models with small data and validating its cognitive plausibility.

Contribution

It adapts the LIDA model into MIRA for movie recommendations, showing its effectiveness and reinforcing LIDA's applicability in neural-inspired computational agents.

Findings

MIRA achieved similar precision to traditional models.

MIRA performed well with small data volumes.

The model was validated with volunteer tests.

Abstract

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.