Book Recommendation Model Based on Wide and Deep Model

Ma, Yihan; Jiang, Jieteng; Dong, Shikui; Li, Chunmei; Yan, Xiyu

doi:10.1109/aiid51893.2021.9456524

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…In a separate study in 2020, researchers from France and Italy utilized a deep neural network for inferring the underlying cause of death, achieving over 97% accuracy (10). However, due to country-specific variations, validating the model for cross-country applicability remains an ongoing research challenge (4). Using an attention mechanism, the model exhibited an accuracy range of 80.9% to 81.7% (11).…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, there has been a focus on utilizing artificial intelligence (AI) techniques to automatically identify the primary causes of death. Despite deep neural network models achieving 97.8% accuracy in this task (4), their practical applications are restricted by performance limitations. This study employs the Wide and Deep framework to enhance the accuracy and stability of deep learning models, aiming to better predict the underlying causes of death and improve cause of death surveillance.…”

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confidence: 99%

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Advancing Underlying Cause of Death Inference Through Wide and Deep Model

Fang,

Huang,

Yin

et al. 2024

China CDC Weekly

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Introduction: Accurately filling out death certificates is essential for death surveillance. However, manually determining the underlying cause of death is often imprecise. In this study, we investigate the Wide and Deep framework as a method to improve the accuracy and reliability of inferring the underlying cause of death.Methods: Death report data from national-level cause of death surveillance sites in Fujian Province from 2016 to 2022, involving 403,547 deaths, were analyzed. The Wide and Deep embedded with Convolutional Neural Networks (CNN) was developed. Model performance was assessed using weighted accuracy, weighted precision, weighted recall, and weighted area under the curve (AUC). A comparison was made with XGBoost, CNN, Gated Recurrent Unit (GRU), Transformer, and GRU with Attention.Results: The Wide and Deep achieved strong performance metrics on the test set: precision of 95.75%, recall of 92.08%, F1 Score of 93.78%, and an AUC of 95.99%. The model also displayed specific F1 Scores for different cause-of-death chain lengths: 97.13% for single causes, 95.08% for double causes, 91.24% for triple causes, and 79.50% for quadruple causes.Conclusions: The Wide and Deep significantly enhances the ability to determine the root causes of death, providing a valuable tool for improving causeof-death surveillance quality. Integrating artificial intelligence (AI) in this field is anticipated to streamline death registration and reporting procedures, thereby boosting the precision of public health data.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Advancing Underlying Cause of Death Inference Through Wide and Deep Model

Fang,

Huang,

Yin

et al. 2024

China CDC Weekly

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“…The simulation findings demonstrated that the suggested RS's accuracy is noticeably higher than that of the current techniques. Kiran R et al [16] suggested a unique DL hybrid recommender system to close the loopholes in CF systems and use DL to reach state-of-theart predicted accuracy. The method learns non-linear latent variables by representing users and items using embeddings.…”

Section: Literature Reviewmentioning

confidence: 99%

An Effective Book Recommendation System using Weighted Alternating Least Square (WALS) Approach

Murugesan

2024

IJACSA

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Book recommendation systems are essential resources for connecting people with the correct books, encouraging a love of reading, and sustaining a vibrant literary ecosystem in an era when information overload is a prevalent problem. With the emergence of digital libraries and large online book retailers, readers may no longer find their next great literary journey without the help of customized book suggestions. This work offers a novel way to improve book recommendation systems using the Weighted Alternating Least Squares (WALS) technique, which is intended to uncover meaningful patterns in user ratings. The suggested approach minimizes the Root Mean Square Error (RMSE), a crucial indicator of recommendation system (RS) performance, in order to tackle the problem of optimizing recommendations. By representing user-item interactions as a matrix factorization problem, the WALS approach improves the recommendation process. In contrast to conventional techniques, WALS adds weighted elements that highlight specific user-item pairings' significance, increasing the recommendations' accuracy. Through an empirical study, the proposed approach demonstrates a significant reduction in RMSE when compared to standard RS, highlighting its effectiveness in enhancing the quality of book recommendations. By leveraging weighted matrix factorization, the proposed method adapts to the nuanced preferences and behaviors of users, resulting in more accurate and personalized book recommendations. This advancement in recommendation technology is poised to benefit both readers and the book industry by fostering more engaging and satisfying reading experiences.

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Book recommendation system: reviewing different techniques and approaches

Devika,

Milton

2024

Int J Digit Libr

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Book Recommendation Model Based on Wide and Deep Model

Cited by 5 publications

References 7 publications

Advancing Underlying Cause of Death Inference Through Wide and Deep Model

Advancing Underlying Cause of Death Inference Through Wide and Deep Model

An Effective Book Recommendation System using Weighted Alternating Least Square (WALS) Approach

Book recommendation system: reviewing different techniques and approaches

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