  Project ATHENA aims to develop an application to address information
overload, primarily focused on Recommendation Systems (RSs) with the
personalization and user experience design of a modern system. Two machine
learning (ML) algorithms were used: (1) TF-IDF for Content-based filtering
(CBF); (2) Classification with Matrix Factorization- Singular Value
Decomposition(SVD) applied with Collaborative filtering (CF) and mean
(normalization) for prediction accuracy of the CF. Data sampling in academic
Research and Development of Philippine Council for Agriculture, Aquatic, and
Natural Resources Research and Development (PCAARRD) e-Library and Project
SARAI publications plus simulated data used as training sets to generate a
recommendation of items that uses the three RS filtering (CF, CBF, and
personalized version of item recommendations). Series of Testing and TAM
performed and discussed. Findings allow users to engage in online information
and quickly evaluate retrieved items produced by the application.
Compatibility-testing (CoT) shows the application is compatible with all major
browsers and mobile-friendly. Performance-testing (PT) recommended v-parameter
specs and TAM evaluations results indicate strongly associated with overall
positive feedback, thoroughly enough to address the information-overload
problem as the core of the paper. A modular architecture presented addressing
the information overload, primarily focused on RSs with the personalization and
design of modern systems. Developers utilized Two ML algorithms and prototyped
a simplified version of the architecture. Series of testing (CoT and PT) and
evaluations with TAM were performed and discussed. Project ATHENA added a UX
feature design of a modern system.
