A Collaborative Filtering Recommender System Model for Recommending Intervention to Improve Elderly Well-being

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.

A Hybrid Knowledge-Based and Collaborative Filtering Recommender System Model for Recommending Interventions to Improve Elderly Wellbeing

“…represent the socialization aspect, represent the health aspect, represent the cognitive aspect, represent the physical aspect, represent the nutrition aspect, represent the spirituality aspect and represent the environment aspect for user . The method to calculate the value of each aspect in the user profile was discussed in our previous work [12].…”

Assessment of cognitive games to improve the quality of life of Parkinson’s and Alzheimer’s patients

“…Literature about RSs presents interactions on how to improve the general well-being of elderly people. Initial research was proposed in Azmi et al 17 using Collaborative Filtering, which group users with similar conditions to recommend interventions, previously used on similar users. In Hammer et al, 18 the work describes the development of a display with which users interacted, and from which they obtained feedback after answering a series of quizzes.…”