A collaborative filtering recommender system model for recommending intervention to improve elderly well-being

Azmi, Aini Khairani and Abdullah, Noraswaliza and Emran, Nurul Akmar (2019) A collaborative filtering recommender system model for recommending intervention to improve elderly well-being. International Journal of Advanced Computer Science and Applications, 10 (6). 131 - 138. ISSN 2158-107X

Text A COLLABORATIVE FILTERING RECOMMENDER SYSTEM MODEL FOR RECOMMENDING INTERVENTION TO IMPROVE ELDERLY WELL-BEING.PDF Download (448kB)

Abstract

In improving elderly well-being nowadays, people at home or health care centre are mostly focusing on guarding and monitoring the elderly using tools, such as CCTV, robots, and other appliances that require a great deal of cost and neat fixtures to prevent damage. Elderly observations using the recommender system are found to be implemented, but only focusing on one aspect such as nutrition and health. However, it is important to give interventions to an elderly by concentrating more on the multiple aspects of successful ageing such as social, environment, health, physical, mental and other so that it can help the elderly people in achieving successful ageing as well as improving their well-being. In this paper, two recommender system models are proposed to recommend interventions for improving elderly well-being in the multiple aspects of successful ageing. These models using a Collaborative Filtering (CF) technique to recommend interventions to an elderly based on the interventions given to other elderly who have similar conditions with the user. The process of recommending interventions involves the generation of user profiles presenting the elderly conditions in multiple aspects of successful ageing. It also applying the k-Nearest Neighbor (kNN) method to find users with similar conditions and recommending interventions based on the interventions given to the similar user. The experiment is conducted to determine the performance of the proposed Collaborative Filtering (CF) recommender system and Collaborative Filtering and Profile Matching (CFS) compared to the Basic Search (BS). The results of the experiment showed that Collaborative Filtering (CF) recommender system and Collaborative Filtering and Profile Matching (CFS) outperformed Basic Search (BS) in terms of precision, recall and F1 measure. This result showed that the proposed models are efficient to recommend interventions using elderly profiles based on many aspects of successful ageing.

Item Type:	Article
Uncontrolled Keywords:	Collaborative filtering, Elderly well-being, K-nearest neighbor, Recommender system, Successful ageing
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	03 Dec 2020 09:01
Last Modified:	18 Jul 2023 11:33
URI:	http://eprints.utem.edu.my/id/eprint/24805
Statistic Details:	View Download Statistic

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