What Chinese cruisers want: An analysis of product preferences

Cruise travel has become increasingly popular, and the number of cruisers is growing rapidly. China plays a dominant role in worldwide tourism owing to its large population and fast economic development. It is a potential major market for the cruise industry. The number of inbound and outbound Chinese travelers continues to increase, generating growing interest in cruise travel. Investigating what Chinese cruisers want from cruise travel has great significance; however, little research has been conducted concerning Chinese travelers\u27 preferences in the cruise product domain. The purpose of the present study is to address this gap by investigating Chinese travelers\u27 cruise product preferences. This study also features an exploratory analysis on motivational factors in a Chinese context. Theoretical and practical implications are discussed

Radical-Enhanced Chinese Character Embedding

We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage. However, existing Chinese processing algorithms typically regard word or character as the basic unit but ignore the crucial radical information. In this paper, we fill this gap by leveraging radical for learning continuous representation of Chinese character. We develop a dedicated neural architecture to effectively learn character embedding and apply it on Chinese character similarity judgement and Chinese word segmentation. Experiment results show that our radical-enhanced method outperforms existing embedding learning algorithms on both tasks.Comment: 8 pages, 4 figure

Binary Classification with Positive Labeling Sources

To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic labeling sources rather than manual annotation. Existing works of WS for binary classification typically assume the presence of labeling sources that are able to assign both positive and negative labels to data in roughly balanced proportions. However, for many tasks of interest where there is a minority positive class, negative examples could be too diverse for developers to generate indicative labeling sources. Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only. We propose WEAPO, a simple yet competitive WS method for producing training labels without negative labeling sources. On 10 benchmark datasets, we show WEAPO achieves the highest averaged performance in terms of both the quality of synthesized labels and the performance of the final classifier supervised with these labels. We incorporated the implementation of \method into WRENCH, an existing benchmarking platform.Comment: CIKM 2022 (short

Factors influencing voluntary premarital medical examination in Zhejiang province, China: a culturally-tailored health behavioral model analysis

BACKGROUND: Premarital medical examination (PME) compliance rate has dropped drastically since it became voluntary in China in 2003. This study aimed to establish a prediction model to be a theoretic framework for analyzing factors affecting PME compliance in Zhejiang province, China. METHODS: A culturally-tailored health behavioral model combining the Health Behavioral Model (HBM) and the Theory of Reasoned Action (TRA) was established to analyze the data from a cross-sectional questionnaire survey (n = 2,572) using the intercept method at the county marriage registration office in 12 counties from Zhejiang in 2010. Participants were grouped by high (n = 1,795) and low (n = 777) social desirability responding tendency (SDRT) by Marlowe-Crowne Social Desirability Scale (MCSDS). A structural equation modeling (SEM) was conducted to evaluate behavioral determinants for their influences on PME compliance in both high and low SDRT groups. RESULTS: 69.8% of the participants had high SDRT and tended to overly report benefits and underreport barriers, which may affect prediction accuracy on PME participation. In the low SDRT group, the prediction model showed the most influencing factor on PME compliance was behavioral intention, with standardized structural coefficients (SSCs) being 0.75 (P < 0.01), and the intention was positively determined by individual’s attitude toward PME (SSCs = 0.48, P < 0.01) and subjective norms (SSCs = 0.22, P < 0.01) and negatively determined by perceived threat (SSCs = -0.08, P = 0.028). Attitudes and subjective norms were more crucial predictors for PME compliance than perceived threat (SSCs = 0.36, 0.269, and -0.06, respectively). County environmental factors played a role in PME compliance while less influential than behavioral determinates (16% vs. 84% in across factor variance partition coefficient). CONCLUSIONS: PME compliance might be influenced by demographic, behavioral, and social environmental factors. The verified prediction model was tested to be an effective theoretic framework for the prediction of factors affecting voluntary PME compliance. It also should be noted that internationally available behavioral theories and models need to be culturally tailored to adapt to particular populations. This study has provided new insights for establishing a theoretical model to understand health behaviors in China

Self-Supervised Multi-Modal Sequential Recommendation

With the increasing development of e-commerce and online services, personalized recommendation systems have become crucial for enhancing user satisfaction and driving business revenue. Traditional sequential recommendation methods that rely on explicit item IDs encounter challenges in handling item cold start and domain transfer problems. Recent approaches have attempted to use modal features associated with items as a replacement for item IDs, enabling the transfer of learned knowledge across different datasets. However, these methods typically calculate the correlation between the model's output and item embeddings, which may suffer from inconsistencies between high-level feature vectors and low-level feature embeddings, thereby hindering further model learning. To address this issue, we propose a dual-tower retrieval architecture for sequence recommendation. In this architecture, the predicted embedding from the user encoder is used to retrieve the generated embedding from the item encoder, thereby alleviating the issue of inconsistent feature levels. Moreover, in order to further improve the retrieval performance of the model, we also propose a self-supervised multi-modal pretraining method inspired by the consistency property of contrastive learning. This pretraining method enables the model to align various feature combinations of items, thereby effectively generalizing to diverse datasets with different item features. We evaluate the proposed method on five publicly available datasets and conduct extensive experiments. The results demonstrate significant performance improvement of our method

Tryptophan-rich domains of Plasmodium falciparum SURFIN4.2 and Plasmodium vivax PvSTP2 interact with membrane skeleton of red blood cell

Additional file 1: Table S1. Primers for PCR amplification and plasmid construction