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With the explosively increasing global old-age population, elderly care services have gradually become a key industry of social concern. Society's progress, especially the revolution and development of the Internet (Dana et al., 2022; Xiao et al., 2020a) and intelligent software (Xiao et al., 2018), has spawned a range of pension services, which has crucial impacts on older people's service selection. However, due to their particularity, the elderly group has some distinctive characteristics such as health problems, old knowledge structure, and unskilled operation of electronic products when surfing the Internet. Meanwhile, Web service recommendation is now being researched as one of the basic research topics in the SOC sector. Function-based Web service recommendation, social network-based Web service recommendation, and collaborative filtering Web service recommendation are the three categories of research in this field. Therefore, it is of great practical significance compared with other age groups to establish an efficient service recommendation system for the elderly to achieve precise service recommendations.
A recommendation system (RS) is used to solve the problem of information overload through a large number of Web services (Dang et al., 2021). In particular, it searches for the most relevant content based on the user's specific preferences. Collaborative filtering is one of the most commonly used algorithms in traditional service recommendation systems (Xiao et al., 2020b), with the characteristics of simplicity and intuitiveness (Salhi et al., 2021). With the deepening of services research, recommendation algorithm based on user profiles can recommend services that suit customers requirements and preferences more. User profile builds different models aimed at different customers. Peng et al. (2018) presented a multi-view ensemble framework for constructing user profiles based on the data of grid users to identify the electric-change users accurately. Ahn & Shi (2009) developed a simple and low-cost movie recommendation system harnessing vast cultural metadata about movies existing on the Web and proved the potential of cultural metadata.
The pension industry should seize the opportunity to develop Internet+ and active use of Internet technology (Hairui, 2016; Trapp et al., 2022). However, the pension service recommendation's current development still has problems (Meng et al., 2020). Aiming at the elderly population, their objective conditions, including health status, consumption habits, and economic status, largely determine their demands. Current approaches have a series of difficulties in capturing the needs and interests of senior citizens. Besides this, although recommendation algorithms have had in-depth research in e-commerce, service recommendation in the pension industry is just at the beginning stage. There is an excellent need for pension industry-oriented research to capture the preference of elderly customers and recommend appropriate services for them.