Personal preferences analysis of user interaction based on social networks

Tsai, Cheng‐Hung; Liu, Han–Wen; Ku, Tsun; Chien, Wu-Fan

doi:10.1109/cccs.2015.7374148

Cited by 6 publications

(3 citation statements)

References 24 publications

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“…The number of user of social network had a breakthrough of ten million at 2015, according to the research of Institute for Information Industry. [9]Among those under 30 and between from 50 to 59, the proportions of using social network were 96% and 70%, respectively, which show an extremely high penetration rate. As for the period of using social network, a research shows that there were more than 70% of people using it over a year.…”

Section: Related Workmentioning

confidence: 96%

Personal Behavior Composite Data Analysis Based on Mobile Networks

Tsai

Zhu

Liu

et al. 2017

Proceedings of the 5th IIAE International Conference on Industrial Application Engineering 2017

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The paper mainly proposes the research of solving the analysis of APP operation behavior and social personal behavior preference. Through the paper put forward that the three modules implementation for data collection and analysis, which are processed according to the algorithm of each module, and its effective in the analysis of the personalized data overlapping of APP operation behavior and social network platform (Facebook). The planning of the modules presented in the paper. After the system actual verification, on the APP, the total average stay time of assignment increased by 55.96%, total average bounce rate of assignment reduced 53.44%, total average click through rate of assignment increased 1.1 times, total average click through rate of commodity increased 1.3 times, total average redemption rate of commodity increased 2.29 times, total average purchase rate of the commodity increased 12 times.

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Section: Related Workmentioning

confidence: 96%

Personal Behavior Composite Data Analysis Based on Mobile Networks

Tsai

Zhu

Liu

et al. 2017

Proceedings of the 5th IIAE International Conference on Industrial Application Engineering 2017

Self Cite

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“…User preferences captured by their model offer explanations for point of interest recommendation and user recommendation. Tsai et al (2016) analyzed data on users' personal interactions (e.g. clicking on a fan page; liking an article; sharing data) on Facebook.…”

Section: Online User Surveys Via Social Mediamentioning

confidence: 99%

Using social media to explore regional cuisine preferences in China

Zhang

Yue

Zhou

et al. 2019

OIR

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Purpose Food plays an important role in every culture around the world. Recently, cuisine preference analysis has become a popular research topic. However, most of these studies are conducted through questionnaires and interviews, which are highly limited by the time, cost and scope of data collection, especially when facing large-scale survey studies. Some researchers have, therefore, attempted to mine cuisine preferences based on online recipes, while this approach cannot reveal food preference from people’s perspective. Today, people are sharing what they eat on social media platforms by posting reviews about the meal, reciting the names of appetizers or entrees, and photographing as well. Such large amount of user-generated contents (UGC) has potential to indicate people’s preferences over different cuisines. Accordingly, the purpose of this paper is to explore Chinese cuisine preferences among online users of social media. Design/methodology/approach Based on both UGC and online recipes, the authors first investigated the cuisine preference distribution in different regions. Then, dish preference similarity between regions was calculated and few geographic factors were identified, which might lead to such regional similarity appeared in our study. By applying hierarchical clustering, the authors clustered regions based on dish preference and ingredient usage separately. Findings Experimental results show that, among 20 types of traditional Chinese cuisines, Sichuan cuisine is most favored across all regions in China. Geographical proximity is the more closely related to differences of regional dish preference than climate proximity. Originality/value Different from traditional definitions of regions to which cuisine belong, the authors found new association between region and cuisine based on dish preference from social media and ingredient usage of dishes. Using social media may overcome problems with using traditional questionnaires, such as high costs and long cycle for questionnaire design and answering.

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“…In this research, 30 research papers have been collected from different social media providers such as Facebook, Instagram, and Twitter. After analyzing the data given in these papers, the different methods used were examined.…”

Section: Introductionmentioning

confidence: 99%