Content‐Based Health Recommender Systems

Rana, Soumya Prakash; Dey, Maitreyee; Prieto, Javier; Dudley, Sandra

doi:10.1002/9781119711582.ch11

Cited by 7 publications

(3 citation statements)

References 41 publications

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“…Content-based recommendation can help to find important information, for example, in terms of books, articles, and videos, to support injury and illness avoidance and recovery [102]. For example, so-called tennis-elbows are a widespread issue in tennis sports -being confronted with such issues, users would be interested in ways to recover and get back to tennis as soon as possible.…”

Section: Recovery Actionsmentioning

confidence: 99%

Sports Recommender Systems: Overview and Research Issues

Felfernig,

Wundara,

Tran

et al. 2023

Preprint

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Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sport. These systems support people in sports, for example, by the recommendation of healthy and performance-boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different working examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss open research issues.

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Section: Recovery Actionsmentioning

confidence: 99%

Sports Recommender Systems: Overview and Research Issues

Felfernig,

Wundara,

Tran

et al. 2023

Preprint

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“…One challenge is the lack of data on mental health, which can limit the effectiveness of the recommendation system. Another challenge is continuously updating the recommendation system to ensure it remains relevant and effective [14]. Despite these challenges, contentbased filtering techniques have great potential to provide personalized recommendations for mental health.…”

Section: B Content-based Filteringmentioning

confidence: 99%

Exploring the Impact of Hybrid Recommender Systems on Personalized Mental Health Recommendations

Mazlan,

Abdullah,

Ahmad

2023

IJACSA

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Personalized mental health recommendations are crucial in addressing the diverse needs and preferences of individuals seeking mental health support. This research aims to study the investigates the impact of hybrid recommender systems on the provision of personalized recommendations for mental health interventions. This paper explores the integration of various recommendation techniques, including collaborative filtering, content-based filtering, and knowledge-based filtering, within the hybrid system to leverage their respective strengths for Personalized Mental Health Recommendations. Additionally, this paper discusses the challenges and considerations involved in combining multiple techniques, such as data integration and algorithm selection for Hybrid Recommender System for this domain. Furthermore, this paper also discusses the data sources that are typically used in hybrid recommender systems for mental health and evaluation metrics that are employed to assess the effectiveness of the hybrid recommender system. Future research opportunities, including incorporating emerging technologies and leveraging novel data sources, are identified to further enhance the performance and relevance of hybrid recommender systems in the mental health domain. The findings of this research contribute to the advancement of personalized mental health support and the development of effective recommendation systems tailored to individual mental health needs.

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“…A short audit of a few significant contributions from the current literature is given. Soumya Prakash Rana (2020) [8] proposed arrangement, health recommender systems (HRS) have arisen for patientsituated decision-making to suggest better medical care guidance in light of profile health records (PHR) and patient data sets. The HRS can upgrade medical services frameworks and at the same time oversee patients experiencing a scope of various sicknesses utilizing prescient investigation and suggesting fitting therapies.…”

Section: Literature Surveymentioning

confidence: 99%

Semantic Web Recommender System over Different Operating Platforms

Sharif

Faraj

2022

UHD J SCI TECH

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Semantic-Web Recommender System (SWRS) evaluation over different operating systems (OSs) used to facilitate and improve human electronic recommendation management (HERM). The HERM is address the needs of user and dataset of movie in our proposed system through internetworking means which increase the speed of automated recommendation and enhance the goodness of SWRS and services also electronically to select right movies-title to user demand. Furthermore, it will be a benefit for selection a right favor by user for right selection from (i.e., 3000 records in dataset of movie-Lens) in the backend. There are a direct relation between time-consume of selection movie-title, also the time-consume, and accuracy. The two-mentioned parameters, namely, time-consume and accuracy over two different operation system (OSs) which designed by web technology Python. In our research, SWR system is proposed; it is provide with some recommendation methods. The system designed and improved using content-based algorithm (CBA). Investigational results indicate that the developed algorithm technique confident a reasonable performance such as accuracy and time consuming compared to other existing works with a testing average accuracy of 85.63 for windows and 88.35 for Linux operating system. In conclusion, SWRS investigated on two different operating platforms and could be seen that the Linux is faster than windows in accuracy and time consuming.

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Content‐Based Health Recommender Systems

Cited by 7 publications

References 41 publications

Sports Recommender Systems: Overview and Research Issues

Sports Recommender Systems: Overview and Research Issues

Exploring the Impact of Hybrid Recommender Systems on Personalized Mental Health Recommendations

Semantic Web Recommender System over Different Operating Platforms

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