School of Design Art, Changsha University of Technology, Hunan, Changsha 410114, China.
Comput Intell Neurosci. 2022 Oct 4;2022:4592468. doi: 10.1155/2022/4592468. eCollection 2022.
As the world's population continues to increase, the proportion of elderly people is also rising. The existing elderly public service system is no longer able to meet the needs of the elderly for their daily lives. The elderly population is significantly less receptive to emerging matters than the younger population, resulting in the public elderly service system not being able to access the initial data of elderly users in a timely manner, which causes the system to make incorrect recommendations. Therefore, the elderly cannot enjoy all kinds of online services provided by the Internet platform. In order to solve this problem, an elderly intelligent recommendation method based on hybrid collaborative filtering is proposed. First, the data of elderly users and elderly service items are scored, and modelling is completed by a collaborative filtering algorithm. Then, the XGBoost model is combined to solve the optimal objective function, so that the recommended data set with the highest score in the nearest neighbour set is obtained. The experimental results show that the proposed hybrid algorithm effectively solves the cold start phenomenon that occurs when the elderly population uses the web to make recommendations for elderly services. In addition, the proposed hybrid algorithm has a higher recommendation footprint accuracy than other recommendation algorithms.
随着世界人口的不断增加，老年人的比例也在上升。现有的老年人公共服务体系已经不能满足老年人日常生活的需求。老年人对新兴事物的接受程度明显低于年轻人，导致公共老年人服务系统无法及时获取老年人用户的初始数据，从而导致系统做出错误的推荐。因此，老年人无法享受到互联网平台提供的各种在线服务。为了解决这个问题，提出了一种基于混合协同过滤的老年人智能推荐方法。首先，对老年人用户和老年人服务项目的数据进行评分，通过协同过滤算法完成建模。然后，结合 XGBoost 模型来求解最优目标函数，从而得到最近邻集中得分最高的推荐数据集。实验结果表明，所提出的混合算法有效地解决了老年人在使用网络推荐老年人服务时出现的冷启动现象。此外，与其他推荐算法相比，所提出的混合算法具有更高的推荐足迹精度。
