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In the context of Internet technology, the integration of information technology and education is a powerful supplement to the traditional teaching model of higher education. Online learning has become the new development direction of the education industry in the network era. To address the problems of serious difficulty in completing online teaching tasks, difficulty in monitoring teaching effects, and fragmentation of course resources in universities, a multimodal music knowledge graph is constructed. A personalized learning strategy based on users' interest is proposed through the mining of online education data, and a music online education system has been developed on this basis. To improve the recommendation accuracy of the model, an embedding propagation knowledge graph recommendation method based on decay factors is proposed. The model considers the changes in the strength of user interest during the intra- and interlayer propagation of the knowledge graph interest map and focuses on higher-order user potential interest representations for enhancing the semantic relevance of multihop entities. The experimental results show that the proposed model brings a good prediction effect on several benchmark evaluation metrics and outperforms other comparative algorithms regarding recommendation accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of current music recommendation algorithms, such as low accuracy and poor timeliness, a personalized hybrid recommendation algorithm incorporating genetic features is proposed. The user-based collaborative filtering (UserCF) algorithm analyzes the degree of users' preference for music genes. The improved neural matrix decomposition collaborative filtering (B-NCF) algorithm calculates the correlation between similar users and constructs the adjacency relationship between users. The results of the two algorithms are fused by using a weighted hybrid approach to generate the recommendation list. Finally, the hybrid recommendation model is built on the Spark platform. The paper's traditional and hybrid recommendation algorithms are validated using the Yahoo Music dataset. The experimental results show that the advantages of the algorithm in this paper are more significant under the MAE and F1-measure indexes, and the recommendation accuracy and precision have been greatly improved; the hybrid algorithm can ensure the diversity of the recommended contents, the recommendation hit rate is higher, and the timeliness meets the demand of personalized music recommendation. [ABSTRACT FROM AUTHOR]

Background: Unintentional weight loss in older people has been linked to increased risk of mortality. We aimed to investigate common medical conditions and lifestyle factors, including body fat distribution, as potential determinants of recent and prospective unintentional weight loss in early old age. Methods: From the Medical Research Council (MRC) National Survey of Health and Development (NSHD), we included a total of 2234 study members aged 60–64 with information on unintentional weight loss in 2006–2010. Of these, 2136 also had information on unintentional weight loss recorded in 2015. Logistic regression was conducted to examine the associations between medical conditions, lifestyle, and body fat distribution at age 60–64 and unintentional weight loss at age 60–64 and 69. Results: A total of 109 of 2234 study members had unintentional weight loss at ages 60–64, and 166 of 2136 at age 69. Never smoking was associated with lower risk of unintentional weight loss at age 60–64 (OR = 0.29, 95%CI = 0.12–0.68 compared to current smokers), and this association remained when adjusted for other determinants. Greater waist-hip ratio (OR = 0.95, 95%CI = 0.91–0.99) and body fat-lean mass ratio (OR = 0.96, 95%CI = 0.94–0.99) were associated with less likelihood of unintentional weight loss at age 60–64. Never smoking and greater hip circumference at age 60–64 were associated with lower odds of unintentional weight loss at age 69. Conclusions: Smoking status and body fat distribution may help identify those at risk of unintentional weight loss in early old age. Their benefit in interventions to prevent age-associated weight loss needs to be further investigated. [ABSTRACT FROM AUTHOR]