To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant colony optimization (SEACO) algorithm to achieve fast, accurate, real-time interactive and high-quality learning path recommendations. Consequently, an online personalized learning path optimization model with a time window was constructed first. This model not only considers the learning order of the recommended learning resources, but also further takes the review behavior pattern of learners into consideration, which improves the quality of the learning path recommendation. Then, this study constructed a SEACO algorithm suitable for online personalized learning path recommendation, from the perspective of optimal learning path prediction, which predicts path pheromone evolution by mining historical data, injecting the domain knowledge of learning path prediction that can achieve best learning effects extracted from domain experts and reducing invalid search, thus improving the speed and accuracy of learning path optimization. A simulation experiment was carried out on the proposed online personalized learning path recommendation model by using the real leaner learning behavior data set from the British “Open University” platform. The results illustrate that the performance of the proposed online personalized learning path recommendation model, based on the SEACO algorithm for improving the optimization speed and accuracy of the learning path, is better than traditional ACO algorithm, and it can quickly and accurately recommend the most suitable learning path according to the changing needs of learners in a limited time.

The utilization of user privacy data in the health data tracking system (HDTS) offers numerous benefits for businesses and public services, contingent upon users’ privacy sharing intentions. However, previous research neglected users’ preferences for public services and focused mainly on personalized services. Additionally, traditional privacy calculus theory has a limited focus on data security, leaving gaps in understanding individual and societal aspects. This study aims to fill these gaps by examining the influence of risk perception and factors like potential loss expectations, perceived personalized service benefits, group value identification, perceived public service utility, and perceived privacy on privacy sharing intentions in the context of personalized and public services. The results indicate a positive relationship between individual privacy protection perception and data sharing intention, as well as a positive relationship between group value identification and perceived public service utility with individuals’ privacy sharing intentions. Moreover, this research uncovers the moderating effect of information type sensitivity on the impact of perceived privacy and perceived public service utility on privacy sharing intentions, while there is no moderating effect of information type sensitivity on the relationship between group value identification and privacy sharing intentions. We recommend improving individual privacy education, ensuring data use transparency, and fostering identification with common group values to increase users’ privacy sharing intentions.

In the era of information overload and repetitive reviews, there has been limited exploration into the influence of consumers’ cultural adaptation to creative symbols in theme parks on the usefulness of online reviews, which is significant for enhancing tourism experiences, targeted marketing, personalized services, and informed tourism choices. This study aims to bridge this gap by examining how cultural adaptation factors interact and impact the review usefulness, and by considering the role of cultural adaptation in simplifying information during consumer decision-making processes. Additionally, the study investigates how consumers’ decision reference points, represented by advanced ticket levels, moderate their attention to attribute consistency when evaluating the review usefulness. A sample of 5929 valid consumer reviews of Disney theme parks from 2019 to 2022 on Meituan.com is analyzed using latent semantic analysis and Tobit regression to test the proposed hypotheses. We find that high symbolic creativity reviews stimulate cultural adaptation and increase attention to service attributes when evaluating review usefulness. Moreover, advanced ticket levels do not moderate the usefulness of extremely negative reviews. However, they do have a moderating effect on the usefulness of extremely positive reviews, with the direction of moderation differing based on the levels of symbolic creativity.