Recommendation of diet using hybrid collaborative filtering learning methods
- Title
- Recommendation of diet using hybrid collaborative filtering learning methods
- Creator
- Vairale V.S.; Shukla S.
- Description
- These days, various recommender systems exist for online advertisement services which recommend the products considering users interests. Similarly, health recommendation systems are becoming most important component in individuals life. Due to the modernization and busy schedule, people give less concern to their eating patterns. This leads to various health issues like obesity, thyroid disorder, diabetes and others. Every individual has different health issues and food habits. Therefore, diet recommendations should be suggested by considering their personal health profile and food preferences. So, it becomes essential to analyze individuals health concerns before recommending the diet with required nutrient values. Thus, it helps people to minimize the further risks associated with the current health conditions. The proposed diet and exercise recommender framework suggests a balanced diet for thyroid patients. It takes care of the food intake with necessary nutrients requirement based on thyroid disorders. This paper applies K-nearest neighbor collaborative filtering models using various similarity measures. The paper assessed two-hybrid learning methods, KNN with alternating least squares: KNN-ALS and KNN with stochastic gradient decent: KNN-SGD. The experimental setup analyzed and evaluated the performances of all algorithms using mean absolute error (MAE) and root mean squared error (RMSE) values. Springer Nature Singapore Pte Ltd 2020.
- Source
- Lecture Notes in Networks and Systems, Vol-119, pp. 309-318.
- Date
- 2020-01-01
- Publisher
- Springer
- Subject
- Collaborative filtering; K-nearest neighbor; Matrix factorization; Recommender system; Thyroid disorder
- Coverage
- Vairale V.S., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, Karnataka, India; Shukla S., CHRIST (Deemed to be University), Lavasa, Pune, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Vairale V.S.; Shukla S., “Recommendation of diet using hybrid collaborative filtering learning methods,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18847.