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 recommender system is an information filtering system that helps users select items that most match their preferences from a vast amount of information available. It has been widely applied in many domains such as e-commerce, healthcare, entertainment and so on. Currently, there are some efforts have been done for recommending interventions to improve elderly well-being in different aspects of successful ageing. However, the recommendations are focused on only a single aspect of successful ageing such as nutrition or health. There are many aspects of successful ageing that should be considered when given interventions to improve elderly wellbeing namely socialization, health, physical, cognitive, nutrition, spirituality and environment. This paper aims to propose a Hybrid Knowledge-Based and Collaborative Filtering (KBCF) recommendation model to recommend interventions based on multiaspects of successful ageing in order to improve the elderly wellbeing. The Knowledge-based (KB) recommendations are generated by consulting a knowledge base which is developed based on knowledge provided by the domain experts. The Collaborative Filtering (CF) approach is applied to find similar users based on elderly profiles generated from the result of assessments done to the elderly. The results of the KB recommendations and the CF recommendations are integrated and ranked to select the final recommendations. The result of experiments conducted using precision, recall and F1 measure shows that the proposed KBCF model outperforms the baseline models which are Basic Search, Collaborative Filtering and Knowledge-based. This result demonstrates the proposed KBCF model provides more accurate and meaningful intervention recommendations for improving the elderly wellbeing in multiaspects of successful ageing.
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