DO HEALTH CLAIMS MATTER FOR CONSUMER PREFERENCE ON TEA BEVERAGE? EXPERIMENTAL EVIDENCE FROM TAIWAN

This paper aims to identify consumer preference for tea drinking products in Taiwan by applying conjoint analysis and investigate whether health claims as attributes would influence consumer’s choice behavior. From 1 July to 31 August 2005, 620 consumers of tea drinking products participated in the choice-based conjoint experiment, which conducted in the city of Taipei, Taichung, Tainan, and Kaohsiung in Taiwan. The data were collected in supermarket using questionnaire for personal interviews. Overall, the estimated individual models fit the data well using Conditional Logit Model. Regarding the result of “Original Tea”, consumer’s order ranking of tea category is green tea, oolong tea, and black tea. The most importance on the standard that health claims have positive influence on higher likelihood of purchasing tea drinks. In addition, consumer prefers to tea drinks with Catechins, processing technology using cold extraction, and paper package. However, it could be seen that as the price increases the utility for the consumer decreases. Also, we report the negative relationship between price and purchasing intention. It is found that respondents preferred to tea drinking products with health claims. This result stands for consumer’s concern on their health status by intaking additives like Catechins. Our results also suggest that respondents prefer that tea drinks include less sugar that implies that the product is produced “light”.Tea Drinking Products, Consumer Preference, Health Claims, Conjoint Analysis, Conditional Logit Model, Agricultural and Food Policy, Consumer/Household Economics, Demand and Price Analysis, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Health Economics and Policy,

Super-resolution image transfer by a vortex-like metamaterial

We propose a vortex-like metamaterial device that is capable of transferring image along a spiral route without losing subwavelength information of the image. The super-resolution image can be guided and magnified at the same time with one single design. Our design may provide insights in manipulating super-resolution image in a more flexible manner. Examples are given and illustrated with numerical simulations.Comment: 7 pages, 6 figure

When Social Influence Meets Item Inference

Research issues and data mining techniques for product recommendation and viral marketing have been widely studied. Existing works on seed selection in social networks do not take into account the effect of product recommendations in e-commerce stores. In this paper, we investigate the seed selection problem for viral marketing that considers both effects of social influence and item inference (for product recommendation). We develop a new model, Social Item Graph (SIG), that captures both effects in form of hyperedges. Accordingly, we formulate a seed selection problem, called Social Item Maximization Problem (SIMP), and prove the hardness of SIMP. We design an efficient algorithm with performance guarantee, called Hyperedge-Aware Greedy (HAG), for SIMP and develop a new index structure, called SIG-index, to accelerate the computation of diffusion process in HAG. Moreover, to construct realistic SIG models for SIMP, we develop a statistical inference based framework to learn the weights of hyperedges from data. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental result validates our ideas and demonstrates the effectiveness and efficiency of the proposed model and algorithms over baselines.Comment: 12 page

Ontology-based Fuzzy Markup Language Agent for Student and Robot Co-Learning

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.Comment: This paper is submitted to IEEE WCCI 2018 Conference for revie