Li-Chen Cheng

This study proposes a wine recommendation system based on heterogeneous graph transformers that integrates hybrid node features from users, reviews, and wine knowledge. We use real-world data from a wine e-commerce platform, including 1685 wines, 12,361 users, 71,507 reviews, and 71,507 ratings. A multi-relational knowledge graph is constructed to represent relationships among wines, customers, and content. Node features are generated via decoding-enhanced BERT with disentangled attention–based review embeddings and structured wine attributes, while the graph structure captures both objective and subjective signals. We evaluate three heterogeneous graph transformer variants and benchmark them against the baseline light graph convolution network model. Our best-performing configuration, heterogeneous graph transformer model C with hybrid features, achieves an average improvement of 2.11% in precision and 5.87% in normalized discounted cumulative gain (NDCG) across top-K recommendations. These results highlight the value of feature diversity and graph-based modeling for enhancing personalized product recommendations in e-commerce.

Background: Insomnia is common among psychiatric outpatients in Taiwan and often coexists with anxiety and depression. Early insomnia changes may predict long-term depression. Although CBT-I is effective, face-to-face therapy requires many resources. This study evaluated the effectiveness of a chatbot to enhance access to sleep training. Methods: This study recruited 80 patients from a psychosomatic outpatient clinic in Taiwan and randomly assigned them 1:1 to the intervention or control group. Due to withdrawals or incomplete assessments, 35 in the intervention group and 31 in the control group completed all procedures. The intervention group used a CBT-I chatbot for 4 weeks, while the control group received basic sleep education via a website. Sleep quality and mental health were assessed using the PSQI, BSRS-5, PHQ-9, BDI, and BAI. Results: The intervention group showed significant PSQI improvement (t (34) = 3.80, p < .001) and reduced BSRS-5, PHQ-9, BDI, and BAI scores ( p < .05). The control group showed no significant changes. Conclusions: A CBT-I chatbot significantly enhances sleep and mental health, offering accessible, effective support with broad clinical potential.

Background: The COVID-19 pandemic has profoundly affected mental health, leading to an increased prevalence of depression and insomnia. Currently, artificial intelligence (AI) and deep learning have thoroughly transformed health care–related mobile apps, offered more effective mental health support, and alleviated the psychological stress that may have emerged during the pandemic. Early reviews outlined the use of mobile apps for dealing with depression and insomnia separately. However, there is now an urgent need for a systematic evaluation of mobile apps that address both depression and insomnia to reveal new applications and research gaps. Objective: This study aims to systematically review and evaluate mobile apps targeting depression and insomnia, highlighting their features, effectiveness, and gaps in the current research. Methods: We systematically searched PubMed, Scopus, and Web of Science for peer-reviewed journal articles published between 2017 and 2023. The inclusion criteria were studies that (1) focused on mobile apps addressing both depression and insomnia, (2) involved young people or adult participants, and (3) provided data on treatment efficacy. Data extraction was independently conducted by 2 reviewers. Title and abstract screening, as well as full-text screening, were completed in duplicate. Data were extracted by a single reviewer and verified by a second reviewer, and risk of bias assessments were completed accordingly. Results: Of the initial 383 studies we found, 365 were excluded after title, abstract screening, and removal of duplicates. Eventually, 18 full-text articles met our criteria and underwent full-text screening. The analysis revealed that mobile apps related to depression and insomnia were primarily utilized for early detection, assessment, and screening (n=5 studies); counseling and psychological support (n=3 studies); and cognitive behavioral therapy (CBT; n=10 studies). Among the 10 studies related to depression, our findings showed that chatbots demonstrated significant advantages in improving depression symptoms, a promising development in the field. Additionally, 2 studies evaluated the effectiveness of mobile apps as alternative interventions for depression and sleep, further expanding the potential applications of this technology. Conclusions: The integration of AI and deep learning into mobile apps, particularly chatbots, is a promising avenue for personalized mental health support. Through innovative features, such as early detection, assessment, counseling, and CBT, these apps significantly contribute toward improving sleep quality and addressing depression. The reviewed chatbots leveraged advanced technologies, including natural language processing, machine learning, and generative dialog, to provide intelligent and autonomous interactions. Compared with traditional face-to-face therapies, their feasibility, acceptability, and potential efficacy highlight their user-friendly, cost-effective, and accessible nature with the aim of enhancing sleep and mental health outcomes.

Purpose: This study explores the career trajectories of Chief Executive Officers (CEOs) to uncover unique characteristics that contribute to their success. By utilizing web scraping and machine learning techniques, over two thousand CEO profiles from LinkedIn are analyzed to understand patterns in their career paths. This study offers an alternative approach compared to the predominantly qualitative research methods employed in previous research.Design/methodology/approach: This study proposes a framework for analyzing CEO career patterns. Job titles and company information are encoded using the Standard Occupational Classification (SOC) scheme. The study employs the Needleman-Wunsch optimal matching algorithm and an agglomerative approach to construct distance matrices and cluster CEO career paths.Findings: This study gathered data on the career transition processes of graduates from several renowned public and private universities in the United States via LinkedIn. Employing machine learning techniques, the analysis revealed diverse career trajectories. The findings offer career guidance for individuals from various academic backgrounds aspiring to become CEOs.Research limitations/implications: The building of a career sequence that takes into account the number of years requires integers. Numbers that are not integers have been rounded up to facilitate the optimal matching process but this approach prevents a perfectly accurate representation of time worked.Practical implications: This study makes an original contribution to the field of career pattern analysis by disclosing the distinct career path groups of CEOs using the rich LinkedIn online dataset. Note that our CEO profiles are not restricted in any industry or specific career paths followed to becoming CEOs. In light of the fact that individuals who hold CEO positions are usually perceived by society as successful, we are interested in finding the characteristics behind their success and whether either the title held or the company they remain at show patterns in making them who they are today.Originality/value: As a matter of fact, nearly all CEOs had previous experience working for a non-Fortune organization before joining a Fortune company. Of those who have worked for Fortune firms, the number of CEOs with experience in Fortune 500 forms exceeded those with experience in Fortune 1,000 firms.