RECOMMENDATIONS SYSTEM BASED ON MATRIXFACTORIZATION FOR DIGITAL EDUCATION PLATFORM
The utilization of matrix factorization methods in an effort to create a recommendation system is explored in this final project. The challenge of creating a recommendation system arose with the advent of the World Wide Web or WWW, which later became accessible to the public. Since then, all kind...
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| 格式: | Final Project |
| 語言: | Indonesia |
| 在線閱讀: | https://digilib.itb.ac.id/gdl/view/79770 |
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| 總結: | The utilization of matrix factorization methods in an effort to create a recommendation
system is explored in this final project. The challenge of creating a
recommendation system arose with the advent of the World Wide Web or WWW,
which later became accessible to the public. Since then, all kinds of information
have become highly accessible on the internet. This is believed to have positive
impacts such as easy access to information and the acceleration of the escalation
of knowledge. However, there are also negative impacts from this. The ease and
openness of access are believed to be one of the causes of Information Overload
and Decision Fatigue.
In this final project, we will attempt to address the mentioned problems by creating
a recommendation system. Specifically, this recommendation system will be
designed for users of the Dicoding Indonesia service in an effort to enhance their
experience. The improvement in experience is based on the approach of Personalized
Learning Experience in the form of class recommendations from a recommendation
system that we create. The algorithm we use in building the recommendation
system is based on matrix factorization methods. Of course, we will create
algorithms ranging from simple or traditional ones to those utilizing artificial neural
networks. Not forgetting, after creating a recommendation system, we undergo an
evaluation process. Various evaluation processes are used, both in terms of model
building and user aspects.
From various experiments we conducted by testing the recommendation system
with various metrics such as regression and classification, the SVD model has the
most balanced results in both regression and classification metrics, namely 0.3136
for RMSE, 0.8758 for Precision@K, and 0.9037 for Recall@K. However, if we only
prioritize regression metrics, the model that utilizes low-high feature interactions
and artificial neural networks has the best result, which is RMSE 0.2493. |
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