AutoTrustRec: Recommender System with Social Trust and Deep Learning using AutoEncoder

In today’s digital world healthcare is one core area of the medical domain. A healthcare system is required to analyze a large amount of patient data which helps to derive insights and assist the prediction of diseases. This system should be intelligent in order to predict a health condition by analyzing a patient’s lifestyle, physical health records and social activities. The health recommender system (HRS) is becoming an important platform for healthcare services. In this context, health intelligent systems have become indispensable tools in decision making processes in the healthcare sector. Their main objective is to ensure the availability of the valuable information at the right time by ensuring information quality, trustworthiness, authentication and privacy concerns. As people use social networks to understand their health condition, so the health recommender system is very important to derive outcomes such as recommending diagnoses, health insurance, clinical pathway-based treatment methods and alternative medicines based on the patient’s health profile. Recent research which targets the utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed which reduces the workload and cost in health care. In the healthcare sector, big data analytics using recommender systems have an important role in terms of decision-making processes with respect to a patient’s health. This paper gives a proposed intelligent HRS using Restricted Boltzmann Machine (RBM)-Convolutional Neural Network (CNN) deep learning method, which provides an insight into how big data analytics can be used for the implementation of an effective health recommender engine, and illustrates an opportunity for the health care industry to transition from a traditional scenario to a more personalized paradigm in a tele-health environment. By considering Root Square Mean Error (RSME) and Mean Absolute Error (MAE) values, the proposed deep learning method (RBM-CNN) presents fewer errors compared to other approaches.

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Exploit Multi Layer Deep Learning and Latent Factor to Handle Sparse Data for E-commerce Recommender System

Proceedings of the International Conferences on Information System and Technology ◽

10.5220/0009910603430351 ◽

2019 ◽

Author(s):

. Hanafi ◽

Abdul Samad Hasan Basari

Keyword(s):

Deep Learning ◽

Recommender System ◽

Sparse Data ◽

Latent Factor

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Group-Aware Social Trust Management for a Movie Recommender System

Lecture Notes in Electrical Engineering - IT Convergence and Services ◽

10.1007/978-94-007-2598-0_52 ◽

2011 ◽

pp. 495-501 ◽

Cited By ~ 1

Author(s):

Mucheol Kim ◽

Young-Sik Jeong ◽

Jong Hyuk Park ◽

Sang Oh Park

Keyword(s):

Recommender System ◽

Trust Management ◽

Social Trust

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

Applied Sciences ◽

10.3390/app11198993 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8993

Author(s):

Qinglong Li ◽

Jaekyeong Kim

Keyword(s):

Deep Learning ◽

Online Education ◽

Recommender Systems ◽

Recommender System ◽

Information Overload ◽

Personalized Recommendation ◽

Course Content ◽

User Behaviors ◽

Real World Datasets ◽

High Level

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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