China has the largest aging population of all countries and faces severe aging issues. As an important indicator to measure the quality of life, the life satisfaction of elderly Chinese people has received increasing attention. Based on the cross-sectional survey data collected from 2002 to 2014, which were provided by the CLHLS (Chinese Longitudinal Healthy Longevity Survey) project as open datasets, this study investigated how the influence and importance of factors associated with life satisfaction in the elderly have changed during these years. In view of previous research and questionnaire data, demographic, physiological, psychological, economic and social characteristics were selected as potential influencing factors of life satisfaction. With the R programming language, we used IV (information value) as the indicator to measure the influence of associated factors and determined the importance of each factor by establishing a random forest model for each year. Data visualization was used to demonstrate change in each factor with the Microsofot PowerPoint 2016. The results show that, for most factors, their influence has fluctuated. Since 2002, the most significant factors have always been self-rated health, self-evaluation of economic level, economic self-sufficiency and bright personality.

The study investigates whether information-seeking behavior models and theories obtained in previous research are applicable to more complex tasks. It also aims to gather students’ opinions on the importance and helpfulness of various traditional and online information sources in their thesis-writing process. This study would help to develop a better understanding of the roles and impacts of these information sources in the current networked academic infrastructure. Inspired by the Information Search Process model, we divided the process into six stages and conducted three separate surveys that covered students’ feelings, thoughts and actions, as well as other important factors that might affect their behaviors in each of the stages. Our study shows that both the feelings and thoughts of students changed during the different stages of the process, and that they were generally consistent with the descriptions in the Information Search Process model. The study indicates that it is beneficial to use the Information Search Process model as the starting point for studying the student thesis-writing processes. As the outcome of the study, we ultimately proposed a multi-stage model for Chinese undergraduate students’ thesis-writing process.

To leverage user behavior data from the Internet more effectively in recommender systems, this paper proposes a novel collaborative filtering (CF) method called Local Collaborative Filtering (LCF). LCF utilizes local similarities among users and integrates their data using the law of large numbers (LLN), thereby improving the utilization of user behavior data. Experiments are conducted on the Steam game dataset, and the results of LCF align with real-world needs.