Helping university students to choose elective courses by using a hybrid
multi-criteria recommendation system with genetic optimization
- URL: http://arxiv.org/abs/2402.08371v1
- Date: Tue, 13 Feb 2024 11:02:12 GMT
- Title: Helping university students to choose elective courses by using a hybrid
multi-criteria recommendation system with genetic optimization
- Authors: A. Esteban, A. Zafra and C. Romero
- Abstract summary: This paper presents a hybrid RS that combines Collaborative Filtering (CF) and Content-based Filtering (CBF)
A Genetic Algorithm (GA) has been developed to automatically discover the optimal RS configuration.
Experimental results show a study of the most relevant criteria for the course recommendation.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The wide availability of specific courses together with the flexibility of
academic plans in university studies reveal the importance of Recommendation
Systems (RSs) in this area. These systems appear as tools that help students to
choose courses that suit to their personal interests and their academic
performance. This paper presents a hybrid RS that combines Collaborative
Filtering (CF) and Content-based Filtering (CBF) using multiple criteria
related both to student and course information to recommend the most suitable
courses to the students. A Genetic Algorithm (GA) has been developed to
automatically discover the optimal RS configuration which include both the most
relevant criteria and the configuration of the rest of parameters. The
experimental study has used real information of Computer Science Degree of
University of Cordoba (Spain) including information gathered from students
during three academic years, counting on 2500 entries of 95 students and 63
courses. Experimental results show a study of the most relevant criteria for
the course recommendation, the importance of using a hybrid model that combines
both student information and course information to increase the reliability of
the recommendations as well as an excellent performance compared to previous
models.
Related papers
- A Personalized MOOC Learning Group and Course Recommendation Method Based on Graph Neural Network and Social Network Analysis [9.069543885639245]
The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms.
An AI-based assistant has been developed which utilise the collected data to provide personalised recommendations regarding courses and study groups for students.
arXiv Detail & Related papers (2024-10-14T16:06:56Z) - CURE4Rec: A Benchmark for Recommendation Unlearning with Deeper Influence [55.21518669075263]
CURE4Rec is the first comprehensive benchmark for recommendation unlearning evaluation.
We consider the deeper influence of unlearning on recommendation fairness and robustness towards data with varying impact levels.
arXiv Detail & Related papers (2024-08-26T16:21:50Z) - Revolutionizing Undergraduate Learning: CourseGPT and Its Generative AI Advancements [1.949927790632678]
This paper introduces CourseGPT, a generative AI tool designed to support instructors and enhance the educational experiences of undergraduate students.
Built on open-source Large Language Models (LLMs) from Mistral AI, CourseGPT offers continuous instructor support and regular updates to course materials.
The paper demonstrates the application of CourseGPT using the CPR E 431 - Basics of Information System Security course as a pilot.
arXiv Detail & Related papers (2024-07-25T18:02:16Z) - Advancing LLM Reasoning Generalists with Preference Trees [119.57169648859707]
We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning.
Eurus models achieve state-of-the-art results among open-source models on a diverse set of benchmarks.
arXiv Detail & Related papers (2024-04-02T16:25:30Z) - Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation [56.13803674092712]
We propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR)
CaR employs a two-step process: first, it ranks instruction pairs using a high-accuracy (84.25%) scoring model aligned with expert preferences; second, it preserves dataset diversity through clustering.
In our experiment, CaR efficiently selected a mere 1.96% of Alpaca's IT data, yet the resulting AlpaCaR model surpassed Alpaca's performance by an average of 32.1% in GPT-4 evaluations.
arXiv Detail & Related papers (2024-02-28T09:27:29Z) - Heterogeneity-aware Cross-school Electives Recommendation: a Hybrid
Federated Approach [11.838400501725923]
We propose HFRec, a heterogeneous-aware hybrid federateder system for cross-school elective course recommendations.
We train individual school-based models with adaptive learning settings to recommend tailored under a federated scheme.
Our HFRec model demonstrates its effectiveness in providing personalized elective recommendations while maintaining privacy, as it outperforms state-of-the-art models on both open-source and real-world datasets.
arXiv Detail & Related papers (2024-02-19T15:06:04Z) - Choosing the Best of Both Worlds: Diverse and Novel Recommendations
through Multi-Objective Reinforcement Learning [68.45370492516531]
We introduce Scalarized Multi-Objective Reinforcement Learning (SMORL) for the Recommender Systems (RS) setting.
SMORL agent augments standard recommendation models with additional RL layers that enforce it to simultaneously satisfy three principal objectives: accuracy, diversity, and novelty of recommendations.
Our experimental results on two real-world datasets reveal a substantial increase in aggregate diversity, a moderate increase in accuracy, reduced repetitiveness of recommendations, and demonstrate the importance of reinforcing diversity and novelty as complementary objectives.
arXiv Detail & Related papers (2021-10-28T13:22:45Z) - FaiREO: User Group Fairness for Equality of Opportunity in Course
Recommendation [7.5127108629060935]
This paper focuses on identifying and alleviating biases that might be present in a course recommender system.
We formulate our approach as a multi-objective optimization problem and study the trade-offs between equal opportunity and quality.
arXiv Detail & Related papers (2021-09-13T13:00:13Z) - DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation
with Relational GNN [59.160401038969795]
We propose differentiable sampling on Knowledge Graph for Recommendation with GNN (DSKReG)
We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure.
The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems.
arXiv Detail & Related papers (2021-08-26T16:19:59Z) - Information Directed Reward Learning for Reinforcement Learning [64.33774245655401]
We learn a model of the reward function that allows standard RL algorithms to achieve high expected return with as few expert queries as possible.
In contrast to prior active reward learning methods designed for specific types of queries, IDRL naturally accommodates different query types.
We support our findings with extensive evaluations in multiple environments and with different types of queries.
arXiv Detail & Related papers (2021-02-24T18:46:42Z) - UniNet: Next Term Course Recommendation using Deep Learning [0.0]
We propose a deep learning approach to represent how chronological order of course grades affects the probability of success.
We have shown that it is possible to obtain a performance of 81.10% on AUC metric using only grade information.
This is shown to be meaningful across different student GPA levels and course difficulties.
arXiv Detail & Related papers (2020-09-20T00:07:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.