Recommendation System for E‑Learning and E‑Commerce Using

Machine Learning

Shabana, Sreyalakshmi P., Tharun B., Vyshnavi M. and Sameer

Department of CSE (AI & ML), Srinivasa Ramanujan Institute of Technology, Anantapur, Andhra Pradesh, India

Keywords: Recommender Systems, E‑Learning, E‑Commerce, Machine Learning, Personalization, Hybrid Model, Cold

Start Problem, Deep Learning, User Behavior Analysis.

Abstract: Recommender systems have become an essential component of modern e-learning and e-retail platforms,

providing personalized content recommendations to enhance user engagement and satisfaction. Traditional

recommendation techniques, for example, methods like content-based filtering and collaborative filtering,

suffer from drawbacks like the new user problem, limited data density, and overspecialization. To address

these obstacles, this study proposes a combined recommender structure that integrates multiple techniques,

including content-based and collaborative filtering, along with advanced machine learning algorithms. The

proposed system leverages matrix factorization, TF-IDF vectorization, and deep learning models to refine

recommendations and adapt to dynamic user preferences. Experimental evaluation using key performance

indicators like exactness, retrieval rate, F1-measure, and average prediction error (APE) demonstrates that the

hybrid approach significantly improves recommendation accuracy compared to standalone methods. The

findings highlight the potential of hybrid recommender systems in enhancing personalized learning

experiences, optimizing product recommendations, and improving overall platform efficiency. Future

research directions include exploring real-time adaptability, reinforcement learning, and contextual awareness

to further refine recommendation accuracy and user engagement.

1 INTRODUCTION

In the digital age, e-learning and e-retail platforms

generate vast amounts of content and product listings,

making it increasingly challenging for individuals to

discover relevant information efficiently. The

overwhelming number of choices often leads to

decision fatigue, where users struggle to identify

suitable courses or products. Recommender these

setups are crucial for in addressing this challenge by

evaluating user preferences, past interactions, and

product traits to deliver individualized suggestions.

These systems significantly enhance user experience

by ensuring learners access appropriate educational

materials and shoppers discover products adjusted to

their interests.

Classic suggestion frameworks mainly depend on

two approaches: feature-driven filtering and

community-based filtering. Feature-driven filtering

proposes options by assessing their properties and

linking them to a person’s earlier actions. In contrast,

collaborative filtering generates recommendations

based on behavioral patterns among users with

similar interests. While both approaches have been

widely used, they face several limitations, including

the newcomer obstacle (trouble suggesting items to

beginners due to missing past information), data

sparsity (insufficient user- item interactions),

scalability issues, and overspecialization (limited

recommendation diversity). These challenges often

result in inaccurate or repetitive suggestions, reducing

overall effectiveness.

To overcome these limitations, mixed-method

suggestion structures blend various strategies to boost

recommendation precision and versatility. By

merging feature-driven and community-based

filtering with machine learning algorithms, hybrid

models enhance personalization and optimize

recommendation quality. Sophisticated methods like

grid decomposition, TF-IDF vectorization, and deep

learning models help refine user preferences, even in

cases where historical data is limited. These methods

allow the system to learn from user behavior

dynamically, making recommendations more precise

and relevant.

This research aims to develop a hybrid

100

Shabana, , P., S., B., T., M., V. and Sameer,

Recommendation System for E-Learning and E-Commerce Using Machine Learning.

DOI: 10.5220/0013923400004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 5, pages

100-105

ISBN: 978-989-758-777-1

recommendation system that effectively addresses the

shortcomings of traditional methods in e-learning and

e-retail domains. The proposed system leverages

advanced machine learning techniques to enhance

recommendation accuracy while ensuring scalability

and real-time adaptability. By evaluating

effectiveness assessed with essential markers such as

exactness, retrieval rate, F1- measure, and average

prediction error (APE), this study demonstrates the

effectiveness of hybrid models in delivering highly

relevant and user-centric recommendations.

As digital platforms continue to evolve, the

demand for intelligent, adaptive, and context-aware

recommendation systems grows. The proposed

hybrid model not only enhances personalized

learning experiences and targeted product

recommendations but also contributes to higher user

engagement, increased sales conversions, and

improved customer satisfaction. Future

advancements could explore reinforcement learning,

contextual awareness, and real-time adaptability to

further refine recommendation accuracy, making

online experiences more intuitive, efficient, and

enjoyable.

2 RELATED WORKS

In 2005, G. Adomavicius and A. Tuzhilin authored

the paper "Toward the Next Generation of

Recommender Systems: A Survey of the State-of-the-

Art and Possible Extensions," which was published in

within IEEE Journal of Knowledge and Data

Engineering (Volume 17, Issue 6). This paper

provided a comprehensive survey of recommender

systems, discussing existing approaches and

proposing possible extensions for future

advancements.

In 2017, X. He, L. Liao, H. Zhang, L. Nie, X. Hu,

and T. S. Chua presented "Neural collaborative

filtering" at the the 26th Global Web Conference

(pages 173-182). Their work introduced deep neural

networks to collaborative filtering, significantly

improving recommendation accuracy by learning

non-linear user-item interactions.

In 2009, Y. Koren, R. Bell, and C. Volinsky

published "Matrix factorization techniques for

recommender systems" in IEEE Computing

Magazine (volume 42, issue 8, pages 30-37). This

research demonstrated how matrix factorization

techniques enhance recommendation accuracy by

capturing latent user- item relationships.

In 2007, A. Paterek contributed "Improving

regularized singular value decomposition for

collaborative filtering" in the Records from the KDD

Cup and Seminar. This study refined singular value

decomposition (SVD) by incorporating regularization

techniques to improve the precision of group- based

filtering-based recommendations.

In 2015, J. McAuley, C. Targett, Q. Shi, and A.

van den Hengel presented "Image-based

recommendations on styles and substitutes" at the

38th Global ACM SIGIR Symposium on Information

Retrieval Research and Development (pages 43-52).

This research introduced image-based

recommendation models that analyze product visual

features to suggest similar styles and substitutes.

In 2009, S. Rendle, C. Freudenthaler, Z. Gantner,

and L. Schmidt-Thieme presented "BPR: Bayesian

personalized ranking from implicit feedback" at the

25th Symposium on Uncertainty in Artificial

Intelligence (pages 452-461). Their work proposed a

Bayesian ranking model that learns personalized

ranking preferences from implicit user feedback.

In 2015, F. Ricci, L. Rokach, and B. Shapira

published Recommendation Systems Guidebook by

Springer, New York, NY. This book serves as a

comprehensive guide on recommender systems,

covering traditional and modern recommendation

techniques.

In 2001, B. Sarwar, G. Karypis, J. Konstan, and J.

Riedl presented "Item-based collaborative filtering

recommendation algorithms" at the 10th Worldwide

Web International Meeting (pages 285-295). Their

research introduced product-focused group filtering,

enhancing suggestion quality scalability and

efficiency.

In 2016, I. Goodfellow, Y. Bengio, and A.

Courville published Deep Learning in MIT Press,

Cambridge, MA. This book provides foundational

knowledge on advanced neural network methods,

extensively used in suggestion frameworks.

In 2018, H. Fang, Z. Guo, X. Zhang, and J. Zhu

published "A survey on deep learning-based

recommendation systems" within IEEE Open Access

(volume 6, pages 69032-69051). Their work analyzed

various deep learning models used in

recommendation systems, highlighting their strengths

and limitations.

3 EXISTING SYSTEM

Recommendation systems in e-learning and e-

commerce primarily rely on traditional approaches

like attribute-driven filtering and community-based

filtering for provide personalized suggestions.

Content-based filtering analyzes item attributes and

Recommendation System for E-Learning and E-Commerce Using Machine Learning

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compares them with user preferences, whereas

collaborative filtering generates recommendations by

identifying patterns in user interactions and

behaviors. Despite their widespread use, these

approaches face significant challenges, including the

cold start problem, data sparsity, and limited diversity

in recommendations.

In e-learning, recommendation systems typically

utilize course metadata, student performance, and

engagement metrics to suggest learning materials.

While these systems improve access to relevant

content, they often fail to adapt to individual learning

styles and real-time user engagement. Many

traditional models struggle to provide dynamic

recommendations, leading to repetitive or less

relevant course suggestions. As a result, students may

not receive truly personalized learning experiences,

limiting the effectiveness of the system.

In e-commerce, product recommendation systems

analyze purchase history, browsing behavior, and

customer demographics to suggest relevant products.

While these techniques enhance user experience and

boost sales, they suffer from overspecialization and

inability to capture evolving user interests. The

recommendations often fail to reflect changing

customer preferences, leading to lower engagement

rates. Additionally, traditional models rely on limited

data sources, making it difficult to provide accurate

and adaptable recommendations.

Moreover, standalone recommendation

techniques in both e- learning and e-commerce lack

the ability to integrate multiple sources of user data,

restricting their accuracy and adaptability. This

limitation results in static and less effective

recommendations, making it difficult to cater to

diverse user needs. Consequently, there is a growing

demand for hybrid recommendation systems that can

overcome these challenges by combining multiple

techniques, improving personalization, and ensuring

real-time adaptability in both domains.

4 PROPOSED SYSTEM

The proposed suggestion framework combines

attribute- focused filtering, group-based filtering, and

cutting-edge machine learning methods to improve

recommendation accuracy and user satisfaction.

Traditional recommendation models suffer from

constraints like the initial engagement barrier, limited

data density, and overspecialization, which restrict

their ability to generate diverse and personalized

recommendations. To tackle these issues, the

suggested setup uses machine learning strategies,

such as array decomposition, neural networks, and

adaptive learning, enabling it to dynamically adapt to

user preferences. Unlike conventional models, this

system considers real-time user interactions,

contextual factors like time of access, device type,

and session duration to refine recommendations and

enhance user engagement.

For e-learning applications, the system analyzes

multiple factors, including course completion rates,

time spent on learning modules, assessment scores,

learning pace, and individual engagement patterns.

By leveraging behavioral analytics and real-time

tracking, it provides highly personalized course

recommendations tailored to the learner’s skill level

and interests. Unlike traditional models that primarily

depend on course metadata and predefined tags, this

approach ensures that recommendations evolve

dynamically based on user progress and interaction

patterns. The system incorporates a real-time

feedback mechanism, allowing students to provide

input on recommended materials, which helps refine

the learning pathway. Additionally, the system

supports adaptive learning by identifying weak areas

and suggesting resources to strengthen them, making

education more engaging and effective. It also

considers learning styles, ensuring that

recommendations cater to visual, auditory, or

kinesthetic learners, thus maximizing knowledge

retention.

In the e-commerce domain, the proposed system

enhances item suggestions by evaluating buying

patterns, navigation habits, and user demographics

data, and seasonal trends. Unlike conventional

recommendation models that simply suggest similar

items, this system introduces diverse and trending

products to expand user choices and improve

engagement. By leveraging real-time data processing,

the recommendations remain relevant and up-to-date,

adapting as user preferences shift over time.

Additionally, the system integrates user reviews,

product ratings, and popularity trends to refine

recommendations, increasing customer satisfaction.

To further enhance accuracy, it incorporates

contextual factors, such as purchase frequency, recent

searches, and external influences like ongoing sales

or discounts. Reinforcement learning is used to

continuously improve recommendation precision, as

the system learns from user interactions and fine-

tunes its suggestions accordingly.

Furthermore, the proposed system provides a

hybrid approach that combines multiple

recommendation techniques, ensuring that users

receive more accurate, diverse, and personalized

suggestions. By integrating machine learning-driven

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adaptability, the system reduces bias, mitigates cold

start issues, and effectively handles sparse data,

making it highly efficient and scalable. The inclusion

of context-aware recommendations ensures that users

receive content or product suggestions that are not

only based on historical data but also align with their

current needs and behaviors.

By combining personalization, real-time

adaptability, and contextual awareness, the proposed

system significantly enhances user experience in both

e-learning and e-commerce, making it more intuitive,

effective, and user-centric.

4.1 Architecture

Figure 1: Architecture of the project.

Figure 1 show the given architecture represents a

hybrid recommendation system that utilizes NLP and

machine learning for accurate product

recommendations. It starts by loading datasets and

performing text preprocessing to clean product

information. The system then converts product titles

into embeddings using TF-IDF and NLP methods,

ensuring meaningful feature extraction.

Next, K-Means Clustering groups similar

products into 40 clusters based on textual similarities.

When a user provides input, it is processed using

BERT embeddings, allowing the system to

understand contextual meaning effectively. The

system then finds best matches, retrieves the top 5

recommendations, and displays relevant product

details like title, image, and rating.

This approach enhances recommendation

accuracy, personalization, and user engagement,

making it highly effective for e-commerce and digital

platforms.

5 IMPLEMENTATIONS

The implementation of the recommendation system

follows a structured approach, beginning with data

collection to analyze user behavior and preferences.

In the e-learning domain, data is gathered by tracking

course completion rates, time spent on different

modules, quiz performance, and engagement with

learning materials. Additionally, metadata related to

course content, instructor details, and subject

relevance is considered to refine recommendations.

For e- commerce, the system collects purchase

history, browsing patterns, customer reviews, and

demographic details to understand consumer

preferences. Abandoned carts, product ratings,

frequently viewed items, seasonal trends, and

promotional campaigns further enhance the

recommendation accuracy.

Figure 2: User input and recommendation process flow.

Once data is collected, preprocessing techniques are

applied to clean and transform raw data for effective

model training. Natural language processing methods

like TF-IDF and word embeddings are used for

textual data, while missing values are handled, and

numerical data is normalized. Collaborative filtering

techniques generate user-item interaction matrices,

while content-based filtering analyzes product and

course attributes to build a feature-rich dataset for

training. Figure 2 show the User Input and

Recommendation Process Flow.

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Figure 3: User input and recommendation process flow.

The system then employs machine learning models

such as grid decomposition, advanced neural

networks, and adaptive learning to develop the

suggestion engine. Collaborative filtering models

identify patterns based on user behavior, while

content-based filtering focuses on product attributes.

A hybrid model combining both approaches ensures

personalized and diverse suggestions. Figure 3 show

the User Input and Recommendation Process Flow.

The framework steadily adapts through user

engagement, enhancing its suggestions progressively

to boost accuracy and relevance. Following model

training, the system is deployed using cloud-based

APIs, making it scalable and accessible across

multiple platforms. It seamlessly integrates with e-

learning and e-commerce applications to provide real-

time personalized recommendations. Feedback

mechanisms capture user interactions to further

enhance future recommendations. Additionally,

explainability features are incorporated to help users

understand why specific courses or products are

suggested, increasing trust and engagement. This

structured implementation ensures that the

recommendation system is adaptive, accurate, and

user- centric, enhancing user experience across

educational and commercial domains.

6 RESULT AND DISCUSSION

The recommendation system for e-learning and retail

platforms is designed to provide personalized

suggestions based on user input and preferences. The

first screen serves as the landing page, where users

can sign up or sign in to access recommendations. It

presents a clean and intuitive interface with a clear

call to action, prompting users to choose a category.

The "Get Recommendations" button directs users to

the recommendation engine, while the "About"

button provides insights into the system’s

functionality. Figure 4 show the Home Page of the

Recommendation System.

Figure 4: Home page of the recommendation system.

The second screen is the product recommendation

interface, allowing users to choose a dataset, enter a

product name, and apply optional filters such as

minimum rating, ASIN, and the number of

recommendations. The interface ensures flexibility,

enabling users to refine their search based on specific

criteria. Once the details are entered, the system

processes the input and provides personalized product

suggestions. The “Home” button allows users to

navigate back to the main page seamlessly, ensuring

a smooth user experience. The visually distinct colors

and structured layout enhance usability, making it

easier for users to interact with the system efficiently.

Figure 5 show the Home Page of the

Recommendation System.

Figure 5: Home page of the recommendation system.

7 CONCLUSIONS

The proposed recommendation system significantly

enhances personalization and user engagement in both

e-learning and e- commerce by leveraging hybrid

techniques and advanced machine learning

algorithms. By addressing challenges like cold start

issues and limited data availability, the setup ensures

accurate and diverse recommendations tailored to

user preferences. Its adaptability allows continuous

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refinement based on user interactions, improving

relevance over time. The cloud- based deployment and

scalable infrastructure enable efficient handling of

large datasets, making the system robust for dynamic

digital platforms. Future advancements, such as

reinforcement learning, real-time analytics, and

context-aware recommendations, will further enhance

its performance, ensuring a more intuitive and

responsive user experience.

8 FUTURE SCOPE

The future of recommendation systems will be driven

by advancements in artificial intelligence,

particularly deep learning, reinforcement learning,

and graph neural networks. These techniques will

improve real-time adaptability, ensuring more accurate

and personalized recommendations. Context- aware

systems that factor in elements such as location, time,

device usage, and sentiment analysis will further

enhance user experience. Additionally, the integration

of multi-modal data, including text, images, videos, and

voice commands, will make recommendations more

interactive and accessible across different platforms.

Security and transparency will also be key

considerations. Explainable AI (XAI) will help users

understand why specific recommendations are made,

increasing trust and engagement. Blockchain

technology can be leveraged to enhance data privacy

and ensure secure transactions, particularly in e-

commerce platforms. Scalability will remain a

priority, with cloud-based and distributed computing

solutions enabling the system to handle vast datasets

efficiently. Future systems will also integrate with

cross-platform services, including IoT devices and

social media, refining recommendations through

broader user interactions, making them more accurate,

dynamic, and user-centric.

REFERENCES

A. Paterek's study, "Improving Regularized Singular Value

Decomposition for Collaborative Filtering," was

presented at the KDD Cup and Workshop in 2007.

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl co-authored

"Item-Based Collaborative Filtering Recommendation

Algorithms," which was published in the Proceedings

of the 10th International Conference on World Wide

Web in 2001 (pp. 285-295).

F. Ricci, L. Rokach, and B. Shapira, Recommender

Systems Handbook. New York, NY: Springer, 2015.

G. Adomavicius and A. Tuzhilin's work, "Toward the Next

Generation of Recommender Systems: A Survey of the

State-of-the-Art and Possible Extensions," was featured

in IEEE Transactions on Knowledge and Data

Engineering (Vol. 17, No. 6), pp. 734-749, 2005.

H. Fang, Z. Guo, X. Zhang, and J. Zhu, “A survey on deep

learning-based recommendation systems,” IEEE

Access, vol. 6, pp. 69032-69051, 201

I. Goodfellow, Y. Bengio, and A. Courville, Deep

Learning. Cambridge, MA: MIT Press, 2016.

J. McAuley, C. Targett, Q. Shi, and A. van den Hengel,

“Image-based recommendations on styles and

substitutes,” Proceedings of the 38th International

ACM SIGIR Conference on Research and

Development in Information Retrieval, 2015, pp. 43-

52.

S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-

Thieme presented "BPR: Bayesian Personalized

Ranking from Implicit Feedback" at the 25th

Conference on Uncertainty in Artificial Intelligence in

2009 (pp. 452-461).

X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. S. Chua,

“Neural collaborative filtering,” Proceedings of the

26th International Conference on World Wide Web,

2017, pp. 173- 182.

Y. Koren, R. Bell, and C. Volinsky contributed to the study

"Matrix Factorization Techniques for Recommender

Systems," which appeared in IEEE Computer (Vol. 42,

No. 8), pp. 30-37, 2009.

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