Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Final Assignment Title Recommendation System Using Collaborative Filtering Method for Undergraduate Students of Informatics Engineering

View through CrossRef
The final project title selection process is a crucial stage for undergraduate students of Informatics Engineering. The difficulty in determining a title that is relevant to their interests and abilities is often an obstacle. This research aims to develop a final project title recommendation system that can assist students in finding suitable research topics. This system utilizes a collaborative filtering approach to analyze historical data on course grades and student preferences. The results show that the item-based collaborative filtering method produces more accurate and relevant recommendations compared to the user-based collaborative filtering method. The RMSE value of the item-based collaborative filtering method (0.4985) is lower than the RMSE value of the user-based collaborative filtering method (0.9759). While the accuracy value of the item-based collaborative filtering method (0.5903) is higher than the user-based collaborative filtering method (0.0153).This system has the potential to optimize the final project title selection process, accelerate study time, and improve the quality of student research.
Title: Final Assignment Title Recommendation System Using Collaborative Filtering Method for Undergraduate Students of Informatics Engineering
Description:
The final project title selection process is a crucial stage for undergraduate students of Informatics Engineering.
The difficulty in determining a title that is relevant to their interests and abilities is often an obstacle.
This research aims to develop a final project title recommendation system that can assist students in finding suitable research topics.
This system utilizes a collaborative filtering approach to analyze historical data on course grades and student preferences.
The results show that the item-based collaborative filtering method produces more accurate and relevant recommendations compared to the user-based collaborative filtering method.
The RMSE value of the item-based collaborative filtering method (0.
4985) is lower than the RMSE value of the user-based collaborative filtering method (0.
9759).
While the accuracy value of the item-based collaborative filtering method (0.
5903) is higher than the user-based collaborative filtering method (0.
0153).
This system has the potential to optimize the final project title selection process, accelerate study time, and improve the quality of student research.

Related Results

Enhanced Product Review Recommendations Using Collaborative Filtering and Singular Value Decomposition
Enhanced Product Review Recommendations Using Collaborative Filtering and Singular Value Decomposition
Recommender systems have become indispensable tools for enhancing user satisfaction and engagement across diverse business sectors, including online marketplaces, streaming service...
EVALUATION OF HYBRID MOVIE RECOMMENDATION SYSTEM BASED ON NEURAL NETWORKS
EVALUATION OF HYBRID MOVIE RECOMMENDATION SYSTEM BASED ON NEURAL NETWORKS
Abstract: Recommendation systems are becoming increasingly important with the growth of streaming platforms. The purpose of this study is to compare the performance of Content-Base...
Nursing Informatics Certification Worldwide: History, Pathway, Roles, and Motivation
Nursing Informatics Certification Worldwide: History, Pathway, Roles, and Motivation
SummaryIntroduction: Official recognition and certification for informatics professionals are essential aspects of workforce development. Objective: To describe the history, pathwa...
An Algorithm for Solving Three-dimensional Assignment Problem
An Algorithm for Solving Three-dimensional Assignment Problem
This article presents a algorithm for solving Three-dimensional assignment problem. Firstly, decompose the three-dimensional cubic matrix corresponding to the three-dimensional ass...
Improvised Collaborative Filtering for Recommendation System
Improvised Collaborative Filtering for Recommendation System
Collaborative filtering (CF) is one of the most important techniques of recommendation system and has been utilized by many e-commerce businesses to provide recommendation to its u...
A Collaborative Filtering Recommendation Model Based on Fusion of Correlation-Weighted and Item Optimal-Weighted
A Collaborative Filtering Recommendation Model Based on Fusion of Correlation-Weighted and Item Optimal-Weighted
Traditional collaborative filtering algorithm has a shortcoming—it assigns all items with equal importance, which can result in excessive frequency in recommending hot it...
Imaging Informatics Education in Clinical Informatics Programs: Perspective from Imaging and Clinical Informatics Professionals
Imaging Informatics Education in Clinical Informatics Programs: Perspective from Imaging and Clinical Informatics Professionals
Abstract Backgroundā€ƒImaging and Clinical Informatics are domains of biomedical informatics. Imaging Informatics topics are often not covered in depth in most Clinical Inf...
Personalized Recommendation Algorithm of Tourist Attractions Based on Transfer Learning
Personalized Recommendation Algorithm of Tourist Attractions Based on Transfer Learning
With the development of information technology and the popularity of the Internet, the data on the network is growing exponentially. Information overload has become a significant i...

Back to Top