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September 10, 2025Open Access

Recommender Systems in E-commerce: State-of-the-art Methods for Improving Personalized Recommendations in Online Shopping Platforms

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Review article explores state-of-the-art methods to improve personalized recommendations, highlighting challenges like scalability and diversity.

Recommender systems significantly enhance user experience in e-commerce, driving sales and engagement.
This overview includes algorithms like collaborative filtering and content-based filtering, addressing challenges in scalability and privacy.
The examination of machine learning approaches reveals innovative frameworks such as deep learning and reinforcement learning.
Insights from the review provide potential future directions in developing effective recommender systems.

Arimondo Scrivano (2025) studied this question.

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September 10, 2025Open Access

View Full Paper

Ask AI

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Authors

Review article explores state-of-the-art methods to improve personalized recommendations, highlighting challenges like scalability and diversity.

Recommender systems significantly enhance user experience in e-commerce, driving sales and engagement.
This overview includes algorithms like collaborative filtering and content-based filtering, addressing challenges in scalability and privacy.
The examination of machine learning approaches reveals innovative frameworks such as deep learning and reinforcement learning.
Insights from the review provide potential future directions in developing effective recommender systems.

Arimondo Scrivano (2025) studied this question.

View Full Paper

Ask AI

Bookmark