DNNRec: A novel deep learning based hybrid recommender system

Kiran, R. Uday; Kumar, Poninder; Bhasker, Bharat

doi:10.1016/j.eswa.2019.113054

Cited by 101 publications

(40 citation statements)

References 11 publications

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“…The results showed that the proposed model was more effective than the traditional recommender system. Kiran, et al [36] proposed a hybrid recommender system that applies embedding techniques to learn nonlinear interaction factors and integrate user and item feature information. The results showed that the proposed method exhibited better prediction performance than that of traditional recommender systems.…”

Section: Deep Learning Technique In Recommender Systemmentioning

confidence: 99%

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Kim

2021

Applied Sciences

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Recently, the worldwide COVID-19 pandemic has led to an increasing demand for online education platforms. However, it is challenging to correctly choose course content from among many online education resources due to the differences in users’ knowledge structures. Therefore, a course recommender system has the essential role of improving the learning efficiency of users. At present, many online education platforms have built diverse recommender systems that utilize traditional data mining methods, such as Collaborative Filtering (CF). Despite the development and contributions of many recommender systems based on CF, diverse deep learning models for personalized recommendation are being studied because of problems such as sparsity and scalability. Therefore, to solve traditional recommendation problems, this study proposes a novel deep learning-based course recommender system (DECOR), which elaborately captures high-level user behaviors and course attribute features. The DECOR model can reduce information overload, solve high-dimensional data sparsity problems, and achieve high feature information extraction performance. We perform several experiments utilizing real-world datasets to evaluate the DECOR model’s performance compared with that of traditional recommendation approaches. The experimental results indicate that the DECOR model offers better and more robust recommendation performance than the traditional methods.

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Section: Deep Learning Technique In Recommender Systemmentioning

confidence: 99%

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Kim

2021

Applied Sciences

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“…The existing works does not address privacy in hybrid algorithms. Hence, we extend the recommender proposed by Kiran et al [20]. Kiran et al devised a novel hybrid deep learning-based recommender that uses side information and their primary focus was to improve the accuracy.…”

Section: Deep Learning-based Hybrid Recommender Systemmentioning

confidence: 99%

“…As observed in most of the deep learning-based recommender systems [20][21][22][23], accuracy can be improved by leveraging user-item rating matrix and side information. Thus, this improved accuracy comes at the cost of user's privacy, and our goal is to develop a novel deep learning-based privacy-preserving hybrid recommendation system.…”

Section: Privacy In Deep Learningmentioning

confidence: 99%

Privacy preserving hybrid recommender system based on deep learning

Selvaraj¹,

Sadasivam²

2021

Turk J Elec Eng & Comp Sci

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Deep learning models are widely being used to provide relevant recommendations in hybrid recommender systems. These hybrid systems combine the advantages of both content based and collaborative filtering approaches.However, these learning systems hamper the user privacy and disclose sensitive information. This paper proposes a privacy preserving deep learning based hybrid recommender system. In hybrid deep neural network, user's side information such as age, location, occupation, zip code along with user rating is embedded and provided as input. These embedding's pose a severe threat to individual privacy. In order to eliminate this breach of privacy, we have proposed a private embedding scheme that protects user privacy while ensuring that the nonlinear latent factors are also learnt. In this paper, we address the privacy in hybrid system using differential privacy, a rigorous mathematical privacy mechanism in statistical and machine learning systems. In the reduced feature set, the proposed adaptive perturbation mechanism is used to achieve higher accuracy. The performance is evaluated using three datasets with root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE), R squared, precision and recall. These evaluation metrics are compared with varying values of privacy parameter ϵ . The experimental results show that the proposed solution provides high user privacy with reasonable accuracy than the existing system. As the engine is generic, it can be used on any recommendation framework.

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“…Now, the relevant recommendations based on deep learning have been relatively perfect in theory and social perspective. Kiran et al (2020) [ 8 ] showed that the previous recommendation system is no longer sufficient to meet the current requirements under social diversity.…”

Section: Recent Related Workmentioning

confidence: 99%

The Construction of Online Course Learning Model of Piano Education from the Perspective of Deep Learning

Huang

Ding

2022

Computational Intelligence and Neuroscience

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This exploration aims at solving multiple teaching problems in piano online education course. On the premise of collaborative filtering, the K-means clustering algorithm is employed to apply the time data to the neural collaborative filtering algorithm, and the Improved Neu Matrix Factorization (Improved Neu MF) algorithm model is implemented. After the experiment, the relevant evaluation indexes are selected and the simulation test is operated on the relevant dataset. The test results show that root mean square error (RMSE) reaches 1.251 and mean absolute error (MAE) is 0.625. Indexes are adopted to evaluate the order of the model. The results suggest that the designed algorithm is better than the comparison algorithm, proving that the optimized model has better performance and can be used to construct an online course model. Based on deep learning, using the designed algorithm to build the online learning model of piano education can provide better, dynamic, and personalized online course recommendations for piano education. In this way, it can improve students’ learning efficiency, promote the online learning development of piano education, and have vital practical significance for disseminating art and culture.

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DNNRec: A novel deep learning based hybrid recommender system

Cited by 101 publications

References 11 publications

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

A Deep Learning-Based Course Recommender System for Sustainable Development in Education

Privacy preserving hybrid recommender system based on deep learning

The Construction of Online Course Learning Model of Piano Education from the Perspective of Deep Learning

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