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

Nissa, Farhatun; Primandari, Arum Handini; Thalib, Achmad Kurniansyah

doi:10.14710/medstat.15.2.139-150

Cited by 2 publications

(1 citation statement)

References 16 publications

(20 reference statements)

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“…In the various book recommender systems that have been developed [7,8], or that use SVD techniques in other domains such as skincare [10], e-commerce products [11], and music [12], the system does not provide intensive interaction between the system and the users. This limits users' flexibility in searching for books that suit their interests.…”

Section: Introductionmentioning

confidence: 99%

Chatbot-Based Book Recommender System Using Singular Value Decomposition

Attalariq,

Baizal

2023

josh

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In the era of information overload, finding the right book that matches one's preferences and interests has become a challenging task for users as many online book provider service websites such as Amazon, Goodreads, and Gramedia provide books of various types and choices. Recommender systems can be used in addressing such issues, it works by filtering information that provides predictions and suggests the best product or service to the user. Currently, various book recommender systems have been developed, but the systems do not provide interaction between the user and the system. Therefore, we propose a recommender system built with a conversational approach so that it can interact with natural language. Recommender system built using matrix factorization method with Singular Value Decomposition (SVD) algorithm, SVD is proven to have advantages for handling large datasets, extracting features, reducing noise and dimensionality so as to speed up computation. We performed two types of evaluation on the system. First, we tested the prediction accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Second, we use questionnaires to measure user satisfaction levels. The evaluation of the system shows that the results of the prediction accuracy obtain an MAE value of 0.6481 and an RMSE value of 0.8287. Then, the accuracy performance of the system found that 83.2% of users get recommendations according to their interests. The user satisfaction with the whole system is 87.9%. The system built can provide a fairly good recommendation performance, and the chatbot can interact well with users based on the evaluation results obtained.

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Section: Introductionmentioning

confidence: 99%

Chatbot-Based Book Recommender System Using Singular Value Decomposition

Attalariq,

Baizal

2023

josh

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Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

Bodduluri,

Palma,

Kurti

et al. 2024

IEEE Access

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This article presents a systematic literature review on hybrid recommendation systems (HRS) in the e-commerce sector, a field characterized by constant innovation and rapid growth. As the complexity and volume of digital data increases, recommendation systems have become essential in guiding customers to services or products that align with their interests. However, the effectiveness of singlearchitecture recommendation algorithms is often limited by issues such as data sparsity, challenges in understanding user needs, and the cold start problem. Hybridization, which combines multiple algorithms in different methods, has emerged as a dominant solution to these limitations. This approach is utilized in various domains, including e-commerce, where it significantly improves user experience and sales. To capture the recent trends and advancements in HRS within e-commerce over the past six years, we review the state-of-the-art overview of HRS within e-commerce. This review meticulously evaluates existing research, addressing primary inquiries and presenting findings that contribute to evidence-based decisionmaking, understanding research gaps, and maintaining transparency. The review begins by establishing fundamental concepts, followed by detailed methodologies, findings from addressing the research questions, and exploration of critical aspects of HRS. In summarizing and incorporating existing research, this paper offers valuable insights for researchers and outlines potential avenues for future research, ultimately providing a comprehensive overview of the current state and prospects of HRS in e-commerce.

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

Cited by 2 publications

References 16 publications

Chatbot-Based Book Recommender System Using Singular Value Decomposition

Chatbot-Based Book Recommender System Using Singular Value Decomposition

Exploring the Landscape of Hybrid Recommendation Systems in E-Commerce: A Systematic Literature Review

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