# Personalized E-Learning Recommender System Based on Autoencoders

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## Abstract

**:**

## 1. Introduction

## 2. Motivation and Contributions

- A recommendation system based on collaborative filtering was developed to suggest various e-learning courses to learners.
- The system uses a dataset constructed by Kulkarni et al. [23] to analyze the performance of a recommendation model based on an autoencoder to recommend appropriate courses to learners.
- The proposed model was compared to four models: KNN, SVD, SVD++ and NMF.
- MAE and RMSE are the two metrics used to evaluate the performance of these models.

## 3. Related Work

## 4. Methodology and Preliminaries

- Learning each student’s behavior;
- Predicting the probability of consuming the courses provided. Learning is built based on learner–course interactions, which determine how each learner interacts with the courses presented.

#### 4.1. Problem Definition

#### 4.2. Autoencoder

- Configurable Parameters

#### 4.3. Procedure for Study

## 5. Experiments

#### 5.1. The Dataset

- User rating.csv contains user ratings and includes user ID, course ID and rating, as shown in Table 8.
- UserId identifies the user. Each user rated courses.
- CourseId identifies the course.
- Rating is the rating ranging from 1 to 5 on a scale of 5 stars.

- UserId identifies the user.
- Degree 1 is the user’s diploma.
- Degree 1 Specializations is the specialty of the user’s degree.
- Known languages are languages mastered by the user.
- Key Skills are the skills of the user.
- Career Objective is the career objective of the user.

- UserId, which identifies the user.
- Degree 1, which is the user’s diploma.
- Degree 1 Specializations, which is the specialty of the user’s degree.
- Campus, which is the name of the campus where the user is registered.
- Key Skills, which are the skills of the user.

#### 5.2. Compared Methods

- KNN

- $\mathrm{I}$ is the list of items that can be recommended.
- $\mathrm{N}$ refers to the number of items to recommend.
- ${\hat{\mathrm{r}}}_{\mathrm{ui}}$ represents the “prediction of the rating that the recommender system provides to user $\mathrm{u}$ for item $\mathrm{i}$”.

- ${\mathrm{U}}_{\mathrm{ui}}^{\mathrm{K}}$ represents the “K-nearest neighbors” of the user named u who evaluated the item named $\mathrm{i}$.
- ${\mathrm{r}}_{\mathrm{vi}}$ denotes the “actual rating given by the neighbor” user $\mathrm{v}$, which concerns item $\mathrm{i}$.
- ${\overline{r}}_{\mathrm{u}}$ is the average rating relative to user $\mathrm{u}$, which is calculated according to the rating history.
- ${\overline{r}}_{\mathrm{v}}$ represents the average rating relative to user $\mathrm{v}$, which is calculated according to the rating history.
- $\mathrm{sim}\left(\mathrm{u},\mathrm{v}\right)$ is the calculation of the similarity between users $\mathrm{u}$ and $\mathrm{v}$ based on distance metrics, such as cosine and Pearson’s correlation coefficient.

- SVD

- SVD++

- $\mathrm{i}$ is the number of items.
- $\mathrm{u}$ is the number of users.
- $\mathrm{f}$ denotes the dimension obtained after the reduction in the matrix dimension.

- ${\mathrm{b}}_{\mathrm{u}}$ and ${\mathrm{b}}_{\mathrm{i}}$ are the deviations from the average values for user $\mathrm{u}$ and item $\mathrm{i}$, respectively.
- $\mathsf{\mu}$ represents the average value of all data.
- ${|\mathrm{N}}_{\mathrm{u}}|$ denotes the “number of items” that are assessed by user $\mathrm{u}$.
- $\left|{\mathrm{R}}_{\mathrm{u}}\right|$ denotes the “number of users” who have rated a specific item.
- $\left|{\mathrm{R}}_{\mathrm{i}}\right|$ represents the “number of items” that have been evaluated by multiple users;
- ${\mathrm{y}}_{\mathrm{j}}$ designates the left orthogonal of implicit matrix.
- $\mathsf{\lambda}1,\mathsf{\lambda}2$ are additional parameters added to values ${|\mathrm{R}}_{\mathrm{u}}|$ and ${|\mathrm{R}}_{\mathrm{i}}|$ for regularization [48].

- NMF

#### 5.3. Evaluation Metrics

- $\hat{\mathrm{Y}}$ represents the rating predicted for the user, and $\mathrm{Y}$ denotes the original rating of the user;
- $\mathrm{N}$ indicates the total number of predicted ratings. Lower values of RMSE and MAE show better prediction accuracy.

#### 5.4. Implementation Details

## 6. Results

## 7. Discussion

- The autoencoder learns latent representations of user–element interactions, enabling them to capture more complex patterns.
- The autoencoder can handle both dense and sparse data.
- The autoencoder can be more scalable.
- The autoencoder can handle different types of data.

## 8. Conclusions and Future Work

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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Item 1 | Item 2 | Item 3 | Item 4 | Item 5 | |
---|---|---|---|---|---|

User1 | 3 | 2 | 3 | ||

User2 | 4 | 3 | 4 | 3 | 5 |

User3 | 3 | 3 | 5 | 4 | |

User4 | 1 | 5 | 5 | 1 |

Article | Machine Learning Method | Approach | Metric | Dataset | Item Types Recommended |
---|---|---|---|---|---|

[27] | SVD, SVD++, Co-clustering and K-NN | CF | -MAE -RMSE | MovieLens-100 K | Movie |

[29] | SVD, SVD++, NMF | CF | -MAE -RMSE | Yelp dataset | Restaurant |

[30] | Denoising autoencoders, deep autoencoders for collaborative filtering, deep autoencoders for collaborative filtering using content information | CF | -“Mean Average Precision” (MAP) -“Normalized Discounted Cumulative Gain” (NDCG) -“Personalization” (P) -“Coverage” -“Serendipity” (SAUC) | Interactions between students and learning objects from a “Massive Open Online Course” (MOOC) | Learning objects |

[31] | Neural Collaborative Filtering (NCF) | -Average precision correlation (AP) | Algebra1 dataset | Question sequencing | |

[34] | Deep belief networks (DBNs) | CF | RMSE | StarC MOOC platform of Central China Normal University | Course |

[35] | Stacked denoising autoencoder (SDAE) with wide linear component | hybrid | Receiver operating characteristic (ROC) curve the area under ROC (AUC-ROC) | Dataset from an online education company | Exercises |

Present Approach | Autoencoder | CF | MAERMSE | Dataset created by Kulkarni et al. [23] | Course |

Hyperparameter | Meaning | Autoencoder |
---|---|---|

Activation | Function utilized by the neuron’s activation | SELU |

Batch Size | The size of the sampler that the network is using | 64 |

Epoch | The total number of iterations required for training the network | 40 |

Loss Function | Compares the distance between the prediction output and the target values to determine the model’s performance | Mean square error (MSE) |

Learning Rate | The rate at which synapse weights are updated | 0.0001 |

Optimizer | “adaptive moment estimation” is an optimization algorithm | Adam |

Activation Function | Advantages | Drawbacks |
---|---|---|

Sigmoid | -Simple to understand -Commonly utilized in shallow networks [42] | -Gradient saturation [42] -Slow convergence-Output is nonzero-centered |

Tanh | -Output is zero-centered | -Vanishing gradient problem could not be solved using this function [42] |

ReLU | -Faster learning | -Fragile during training, resulting in the death of some gradients [42] |

SELU | -Not affected by vanishing gradient problems-Works well in standard feed-forward neural networks (FNNs) [43] | -“Internal covariate shift” problem |

Activation Function | MAE | RMSE |
---|---|---|

SELU | 0.6042 | 0.8756 |

Sigmoid | 1.9906 | 2.4077 |

Relu | 0.7281 | 0.9987 |

Tanh | 1.9624 | 2.3953 |

Optimizer Algorithm | MAE | RMSE |
---|---|---|

Adam | 0.6042 | 0.8756 |

SGD | 1.3769 | 1.7637 |

Dataset | Users | Items | Ratings |

424 | 20 | 8480 |

UserId | CourseId | |||||

1001 | 1002 | … | 1019 | 1020 | ||

2001 | 5 | 3 | … | 1 | 3 | |

2002 | 3 | 5 | … | 0 | 0 | |

… | … | … | … | … | … | |

2423 | 2 | 5 | … | 5 | 5 | |

2424 | 0 | 0 | … | 2 | 3 |

UserId | Degree 1 | Degree 1 Specializations | Known Languages | Key Skills | Career Objective |
---|---|---|---|---|---|

1001 | B.E. | Computer Science & Engineering | “English, Marathi, Hindi” | C, Java, Keras, Flask, DeepLearning, Selenium, cpp, TensorFlow, Machine Learning, Web Development Areas of interest Django, Python, Computer Vision, HTML, MySQL | “Computer Engineering student with good technical skills and problem solving abilities. include Computer Vision, Deep Learning, Machine Learning, and Research.” |

1002 | B.E. | Computer Science & Engineering | Hindi English | Java, Neural Networks, AI, Python, Html5, CPP | Interested in working under company offering AI/Neural Networking outlooks |

… | … | … | … | … | … |

2045 | B.E. | Computer Science & Engineering | Html, Wordpress, Css, C, Drupal-(CMS) Adobe-Illustrator, HTML, Adobe-Photoshop, MYSQL, Bootstrap, Wordpress-(CMS), JavaScript-(Beginner) Python-(Beginner), CSS | To prove myself dedicated worthful and energetic support in an organization that gives me a scope to apply my knowledge and seeking a challenging position and providing benefits to the company with my performance | |

2046 | B.E. | Computer Science & Engineering | “Python, Robotics”, Win32-Sdk, JAVA, Operating-System | “To secure a challenging position where I can effectively contribute my skills as Software Professional, possessing competent Technical Skills.” |

Sr | Degree 1 | Degree 1 Specializations | Campus | Key Skills |
---|---|---|---|---|

1001 | B E | Mechanical, | MITCOE | CATIA |

1002 | B E | Mechanical, | MITCOE | CATIA |

… | … | … | … | … |

10,999 | B E | Electronics Telecommunication Engineering | MITAOE | “AmazonWebServiCes, C CPP, Arduino, MongoDB, Linux, Golang, Microcontrollers, Gobot, InternetofThings, MATLAB, SQL, PHP” |

11,000 | B E | Electronics Telecommunication Engineering | MITAOE | “AmazonWebServiCes, C CPP, Arduino, MongoDB, Linux, Golang, Microcontrollers, Gobot, InternetofThings, MATLAB, SQL, PHP” |

Model | MAE | RMSE |
---|---|---|

KNN | 0.7259 | 1.0895 |

SVD | 0.9922 | 1.2772 |

SVD++ | 0.9796 | 1.2742 |

NMF | 0.9781 | 1.2851 |

Proposed model (autoencoder) | 0.6042 | 0.8756 |

UserId | CourseId |
---|---|

2012 | [1003, 1006, 1004] |

2027 | [1016, 1015, 1001] |

2141 | [1004, 1003, 1005] |

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## Share and Cite

**MDPI and ACS Style**

El Youbi El Idrissi, L.; Akharraz, I.; Ahaitouf, A.
Personalized E-Learning Recommender System Based on Autoencoders. *Appl. Syst. Innov.* **2023**, *6*, 102.
https://doi.org/10.3390/asi6060102

**AMA Style**

El Youbi El Idrissi L, Akharraz I, Ahaitouf A.
Personalized E-Learning Recommender System Based on Autoencoders. *Applied System Innovation*. 2023; 6(6):102.
https://doi.org/10.3390/asi6060102

**Chicago/Turabian Style**

El Youbi El Idrissi, Lamyae, Ismail Akharraz, and Abdelaziz Ahaitouf.
2023. "Personalized E-Learning Recommender System Based on Autoencoders" *Applied System Innovation* 6, no. 6: 102.
https://doi.org/10.3390/asi6060102