SVD based Privacy Preserving Recommendation Model using Optimized Hybrid Item-based Collaborative Filtering

Sahoo, Abhaya Kumar; Pradhan, Chittaranjan; Mishra, Bhabani Shankar Prasad

doi:10.1109/iccsp.2019.8697950

Cited by 15 publications

(5 citation statements)

References 14 publications

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“…The performance of the CNN-Bi-LSTM is evaluated using precision, sensitivity, area under curve (AUC), and hit rate (HR) that is evaluated using the formulas represented in Eqs. (16)(17)(18)(19).…”

Section: Results and Empirical Analysismentioning

confidence: 99%

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An E-Commerce Based Personalized Health Product Recommendation System Using CNN-Bi-LSTM Model

2023

IJIES

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With the increasing complexity of contemporary E-commerce recommender systems, personalized health product recommendations have become a challenging task. Existing methods predominantly rely on latent natural language features extracted from product descriptions, lacking transparency and user engagement. While recent itemrepresentation-learning algorithms integrate pre-defined attribute data, they still struggle to provide accurate recommendations and detailed explanations due to sporadic relationships between stated qualities and the recommendation process. To address these challenges, we propose an E-commerce based personalized health product recommendation system using a CNN-Bi-LSTM model. Our system leverages pre-trained CNN-based transfer learning models, such as AlexNet, Google Net, and ResNet-50, for predicting health products, while the Bi-LSTM acts as a numerical function to provide ratings. The CNN-Bi-LSTM recommendation system utilizes attribute-specific representation extraction methods, resulting in a more robust compatibility model and improved recommendation effectiveness for health products. The proposed approach is evaluated on two datasets: The health-product dataset and the Flipkart health product dataset. The health-product dataset comprises 20,726 products, including health supplements (14,871 items) and fitness-related products (13,663 items) recommended by experts. The Flipkart health product dataset contains 21,889 healthcare products with essential information like name, price, MRP, discount, and ratings. Comparative analysis against existing methods, such as W-RNN and Improved SVM, demonstrates the superiority of our CNN-Bi-LSTM model. The evaluation metrics, including accuracy (95.52%), recall (88.31%), and user coverage (89.51%), highlight the effectiveness of our recommendation system in providing more accurate and relevant personalized health product recommendations. For the aforementioned dataset, the AUC and hit rate of the proposed method are contrasted with those of existing methods. The findings were gathered to demonstrate that the suggested approach performs better when compared to the current recommendation systems.

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Section: Results and Empirical Analysismentioning

confidence: 99%

“…The Collaborative assessment and recommendation engine (CARE) [18] uses CF techniques to predict patient disease. It compares the patient's medical history to similar cases.…”

Section: Related Workmentioning

confidence: 99%

An E-Commerce Based Personalized Health Product Recommendation System Using CNN-Bi-LSTM Model

2023

IJIES

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“…Because each approach mentioned earlier has its advantages and disadvantages, hybrid methods, as shown in Figure 5 (Inspired from a figure in [15]), combine the benefits of different approaches to create a system that performs well in a wide range of applications [16]. It is possible to combine the recommended methodologies (CF and CBF) in a hybrid strategy to receive the most advantage, generate better results, and decrease the risks and challenges connected with these applications [14] [17].…”

Section: Hybrid Methodsmentioning

confidence: 99%

A Recent Trends in eBooks Recommender Systems: A Comparative Survey

Saleh,

Taqa

2024

Iraqi Journal of Science

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The great progress in information and communication technology has led to a huge increase in data available. Traditional systems can't keep up with this growth and can't handle this huge amount of data. Recommendation systems are one of the most important areas of research right now because they help people make decisions and find what they want among all this data. This study looked at the research trends published in Google Scholar within the period 2018-2022 related to recommending, reviewing, analysing, and comparing ebooks research papers. At first, the research papers were collected and classified based on the recommendation model used, the year of publication, and then they were compared in terms of techniques, datasets utilised, problems, contributions, and evaluation methods used. It was found that many in-depth studies of book recommendation systems directly affect how those systems grow. Many researchers interested in book recommendation systems can learn about the many parts of the field by looking at how the study was put together.

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“…The matrix factorization technique uses a matric structure, namely rows as users and columns as items. This technique factorizes 𝑚 × 𝑛 of A matrix into three matrices; the form of singular value decomposition is shown below (Akter et al, 2017;Sahoo et al, 2019).…”

Section: Singular Value Decomposition (Svd)mentioning

confidence: 99%

Collaborative Filtering Approach: Skincare Product Recommendation Using Singular Value Decomposition (Svd)

Nissa

Primandari

Thalib³

2023

Medstat

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The recommendation system provides recommendations for something, be it goods, songs, or movies. The term system is not limited to a service system but concerns a model that can provide recommendations. With recent technological advances, many companies provide various skincare products because current generations are increasingly aware of self-care. With various choices, someone may experience confusion in determining the product they want to buy. Therefore, we need a system that can provide product recommendations run on any platform we use. The most common method for recommendation systems often comes with Collaborating Filtering (CF) where it relies on the past user and item dataset. The singular value decomposition (SVD) method uses a matrix factorization technique that predict the user's rating based on historical ratings. The measurement of the model's accuracy is the RMSE average of 1.01276, indicating that this value results from the best parameters. The results focus on showing skincare product recommendations to users sorted based on rating predictions.

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SVD based Privacy Preserving Recommendation Model using Optimized Hybrid Item-based Collaborative Filtering

Cited by 15 publications

References 14 publications

An E-Commerce Based Personalized Health Product Recommendation System Using CNN-Bi-LSTM Model

An E-Commerce Based Personalized Health Product Recommendation System Using CNN-Bi-LSTM Model

A Recent Trends in eBooks Recommender Systems: A Comparative Survey

Collaborative Filtering Approach: Skincare Product Recommendation Using Singular Value Decomposition (Svd)

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