Improved Collaborative Recommender systems for Healthcare application | IEEE Conference Publication | IEEE Xplore

Improved Collaborative Recommender systems for Healthcare application


Abstract:

Recommender System (RS) is an active research study that has been widely investigated by several research communities in recent years due to the rapid growth of Internet ...Show More

Abstract:

Recommender System (RS) is an active research study that has been widely investigated by several research communities in recent years due to the rapid growth of Internet services. Healthcare application systems aim to present recommendations from reviews presented through online user reviews posted by several other patients. The study presented here aims to analyze Online Collaborative Filtering (ONCF), which adopts a multi-layer neural network architecture for Collaborative Filtering (ONCF) combined with Collaborative Filtering (CF). The purpose of the study is to identify appropriate methods that discover significant relationships or similarities between the patients who have interrelated similarities which would be useful for the appropriate decision-making process. To provide improvements, methods involving ONCF, and Extended Recurrent Convolutional Neural Networks (ERCNN) were analyzed in the study. From the drawbacks analyzed from ONCF and ERCNN, an improved combinational hybrid approach combines both approaches which are termed in the paper as ERCNN-ONCF framework. The research findings of the paper include a proposed and existing system based on Root Mean Square Error leading to outcomes resulting in a value approximating 0.81 which outperforms other approaches including Positive Matrix Factorization (PMF), Automated Recommender (AutoRec), Restricted Boltzmann Machine (RBM), and Neural Autoregressive Distribution Estimation (NADE). The study also summarizes the performance of the proposed system by comparing the results with the neighborhood approach and probability-based schemes leading to results that were promising.
Date of Conference: 18-19 April 2024
Date Added to IEEE Xplore: 07 August 2024
ISBN Information:
Conference Location: Chikkaballapur, India

I. Introduction

Because of the exponential growth of data available on the Internet, the recommendation process has become a crucial component in the healthcare recommendation process [1]. Personalization is thus an essential factor in enabling a better user experience for undergoing detailed analysis of patients. This is quiet among any decision-making processes based on data analyzed as analyzed with basic and other hybrid schemes [2]. Deep Learning (DL) has made significant progress in recent years across various applications, including speech recognition and computer vision and hence it is multi-disciplinary [3], [4]. The recommendation is a subclass of information filtering systems that seeks to predict the ‘rating’ or ‘preference’ that a user provides based on his personal preference. There exist numerous ways of recommendation filtering schemes to build appropriate recommendation system. This paper illustrates a comprehensive study on developing a recommendation process that uses a collaborative filtering approach, which finds similarities between ratings, purchase patterns, and association rule mining framework [5].

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References

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