Lifestyles in Amazon: Evidence from online reviews enhanced recommender system

2019 ◽  
Vol 62 (6) ◽  
pp. 689-706 ◽  
Author(s):  
Yinghui Huang ◽  
Hui Liu ◽  
Weiqing Li ◽  
Zichao Wang ◽  
Xiangen Hu ◽  
...  
Keyword(s):  
Personality Traits ◽  
Recommender System ◽  
Consumer Preference ◽  
Addition Product ◽  
Online Reviews ◽  
Data Driven ◽  
Big Five Personality ◽  
Predicting Performance ◽  
Wide Range ◽  
Data Driven Approach

Online lifestyles have been shown to reflect and affect consumers’ preferences across a wide range of online scenarios. In the context of e-commerce, it still remains unclear whether online lifestyles are practically influential in predicting consumers’ purchasing preferences across different product categories, especially considering its potential influence over the widely used personality traits. In this study, we provide the first, to the best of our knowledge, quantitative demonstration of online lifestyles in predicting consumers’ online purchasing preferences in e-commerce by using a data-driven approach. We first construct an online lifestyles lexicon including seven distinct dimensions using text mining approaches based on consumers’ language use behaviors. We then incorporate the lexicon in a typical e-commerce recommender system to predict consumers’ purchasing preferences. Experimental results on Amazon Review Dataset show that online lifestyles and all its subdimensions significantly improve preference predicting performance and outperform the widely used Big Five personality traits as a whole. In addition, product types significantly moderate the influence of online lifestyle on consumer preference. The strong empirical evidence indicates that the big e-commerce consumer data facilitates more specialized market psychographic segmentation, which advances data-driven marketing decision-making.

Multifractal Mice: Measuring Task Engagement and Readiness-to-hand via Hand Movement

2020 ◽  
Author(s):  
Daniel Bennett
Keyword(s):  
Hand Movement ◽  
Task Engagement ◽  
Computational Method ◽  
Data Driven ◽  
Prior Work ◽  
Wide Range ◽  
Input Signals ◽  
Data Driven Approach ◽  
Future Work ◽  
And Task

We introduce an unobtrusive, computational method for measuring readiness-to-hand and task-engagement during interaction."Readiness-to-hand" is an influential concept describing fluid, intuitive tool use, with attention on task rather than tool; it has longbeen significant in HCI research, most recently via metrics of tool-embodiment and immersion. We build on prior work in cognitivescience which relates readiness-to-hand and task engagement to multifractality: a measure of complexity in behaviour. We conduct areplication study (N=28), and two new experiments (N=44, N=30), which show that multifractality correlates with task-engagement and other features of readiness-to-hand overlooked in previous measures, including familiarity with task. This is the first evaluation of multifractal measures of behaviour in HCI. Since multifractality occurs in a wide range of behaviours and input signals, we support future work by sharing scripts and data (https://osf.io/2hm9u/), and introducing a new data-driven approach to parameter selection

Innovative Teachers, Who Are You? Innovative Teacher Analysis in the Light of The Model оf the ‘Big Five’ Personality Traits

2021 ◽  
Author(s):  
Rina Cohen
Keyword(s):  
Academic Success ◽  
Personality Traits ◽  
Big Five ◽  
Quantitative Methods ◽  
Big Five Personality Traits ◽  
Big Five Personality ◽  
Pedagogical Innovation ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
Wide Range

In the 21st century, reality, characterized by volatility, uncertainty, complexity and ambiguity, together termed VUCA, change constantly occurs throughout social, technological, economic, environmental, educational, and political (STEEEP model) aspects of society. Therefore, education systems need to adopt innovative approaches to adapt to the frequently changing world. In this study, educational and pedagogical innovation is regarded as including whatever constitutes a change in all areas to which education relates. As teachers are one of the most crucial factors in influencing students’ academic success, and as they must rapidly adapt and constantly innovate to adequately prepare their students for ever-changing circumstances, it is essential to identify traits of innovative teachers. The main goal of this study is to characterize the personality traits of innovative teachers according to the Big Five Personality Traits model, referred to as the NEO-AC model, using qualitative and quantitative methods. The findings show that innovative teachers perceive themselves as first and foremost open to experiences. They are curious people with highly developed imaginations and a wide range of interests. Innovative teachers also may be unconventional, capable of putting together plans and projects from several different disciplines.

Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

2018 ◽  
Vol 10 (02) ◽  
pp. 1840001 ◽  
Author(s):  
Catherine M. Sweeney-Reed ◽  
Slawomir J. Nasuto ◽  
Marcus F. Vieira ◽  
Adriano O. Andrade
Keyword(s):  
Empirical Mode Decomposition ◽  
Data Driven ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Data Driven Approach

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

scholarly journals A model of the relationships between the Big Five personality traits and the motivations to deliver word-of-mouth online

Psihologija ◽  
2018 ◽  
Vol 51 (2) ◽  
pp. 215-227 ◽  
Author(s):  
Bogdan Anastasiei ◽  
Nicoleta Dospinescu
Keyword(s):  
Personality Traits ◽  
Big Five ◽  
Word Of Mouth ◽  
Negative Relationship ◽  
Online Reviews ◽  
Big Five Personality Traits ◽  
Big Five Personality ◽  
The Social ◽  
Concern For Others ◽  
Negative Feelings

The goal of this research is to establish the relationships between the Big Five personality traits ? Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness ? and the motivations to deliver electronic word-of-mouth (eWOM) in the social media. The research method was based on a survey administered to 262 subjects, mostly students and young professionals. The personality traits that are most related to eWOM are Extraversion (that influences the need for social appreciation and Positive self-enhancement) and Openness to experience (that determines the concern for others and the desire to help good companies). Conscientiousness has a negative relationship with the tendency to Vent negative feelings about a bad buy, while Neuroticism has a slight influence on the motivation to get Social benefits and Self-enhancement. Knowing the prevalent personality traits and motivations of the eWOM transmitters, the company communication strategist can figure out the most proper ways to approach them. This paper is one of the few that throughly investigates the relationship between personality traits and the intrinsic motivations to write online reviews about companies and brands.

scholarly journals Encoding Text Information with Graph Convolutional Networks for Personality Recognition

2020 ◽  
Vol 10 (12) ◽  
pp. 4081
Author(s):  
Zhe Wang ◽  
Chun-Hua Wu ◽  
Qing-Biao Li ◽  
Bo Yan ◽  
Kang-Feng Zheng
Keyword(s):  
Personality Traits ◽  
Social Engineering ◽  
Big Five Personality ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Wide Range ◽  
Benchmark Datasets ◽  
Text Information ◽  
Class Labels ◽  
Personality Recognition

Personality recognition is a classic and important problem in social engineering. Due to the small number and particularity of personality recognition databases, only limited research has explored convolutional neural networks for this task. In this paper, we explore the use of graph convolutional network techniques for inferring a user’s personality traits from their Facebook status updates or essay information. Since the basic five personality traits (such as openness) and their aspects (such as status information) are related to a wide range of text features, this work takes the Big Five personality model as the core of the study. We construct a single user personality graph for the corpus based on user-document relations, document-word relations, and word co-occurrence and then learn the personality graph convolutional networks (personality GCN) for the user. The parameters or the inputs of our personality GCN are initialized with a one-hot representation for users, words and documents; then, under the supervision of users and documents with known class labels, it jointly learns the embeddings for users, words, and documents. We used feature information sharing to incorporate the correlation between the five personality traits into personality recognition to perfect the personality GCN. Our experimental results on two public and authoritative benchmark datasets show that the general personality GCN without any external word embeddings or knowledge is superior to the state-of-the-art methods for personality recognition. The personality GCN method is efficient on small datasets, and the average F1-score and accuracy of personality recognition are improved by up to approximately 3.6% and 2.4–2.57%, respectively.

Will the relevance of review language and destination attractions be helpful? A data-driven approach

2020 ◽  
pp. 135676672095035
Author(s):  
Sunyoung Hlee ◽  
Hyunae Lee ◽  
Chulmo Koo ◽  
Namho Chung
Keyword(s):  
Online Reviews ◽  
Data Driven ◽  
Online Review ◽  
Cognitive Fit ◽  
Theoretical Contribution ◽  
Language Style ◽  
Data Driven Approach ◽  
Managerial Implications ◽  
Tourism Destinations ◽  
The Impact

Because tourism destinations are difficult to assess in certain standard aspects, the factors that contribute to the helpfulness of reviews remain largely unknown. Moreover, the helpfulness of online reviews has not been explored in terms of the interaction between language style (high- vs. low-cognitive) and attraction type (hedonic vs. utilitarian). Hence, this study examines the impact of language style on the helpfulness of an online review of an attraction, depending on the type of attraction and the meaning of the destination. This study’s data included 8,032 reviews of four attractions (2 hedonic x 2 utilitarian), drawn from TripAdvisor in two different meanings of destinations. Specifically, our findings indicate that when a reviewer posts a utilitarian attraction of the destination, high-cognitive language is perceived to be more helpful. First, we discuss the theoretical contribution of our study using cognitive fit theory, and then provide the study’s managerial implications.

A non-parametric, data-driven approach to despiking ocean surface wave time series

2021 ◽  
Author(s):  
Mika P. Malila ◽  
Patrik Bohlinger ◽  
Susanne Støle-Hentschel ◽  
Øyvind Breivik ◽  
Gaute Hope ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Wave ◽  
Missing Values ◽  
Computational Cost ◽  
Ocean Surface ◽  
Data Driven ◽  
Storm Event ◽  
Wide Range ◽  
Data Driven Approach ◽  
Ocean Surface Wave

AbstractWe propose a methodology for despiking ocean surface wave time series based on a Bayesian approach to data-driven learning known as Gaussian Process (GP) regression. We show that GP regression can be used for both robust detection of erroneous measurements and interpolation over missing values, while also obtaining a measure of the uncertainty associated with these operations. In comparison with a recent dynamical phase space-based despiking method, our data-driven approach is here shown to lead to improved wave signal correlation and spectral tail consistency, although at a significant increase in computational cost. Our results suggest that GP regression is thus especially suited for offline quality control requiring robust noise detection and replacement, where the subsequent analysis of the despiked data is sensitive to the accidental removal of extreme or rare events such as abnormal or rogue waves. We assess our methodology on measurements from an array of four co-located 5-Hz laser altimeters during a much-studied storm event the North Sea covering a wide range of sea states.

scholarly journals Shearlets as feature extractor for semantic edge detection: the model-based and data-driven realm

2020 ◽  
Vol 476 (2243) ◽  
pp. 20190841
Author(s):  
Héctor Andrade-Loarca ◽  
Gitta Kutyniok ◽  
Ozan Öktem
Keyword(s):  
Edge Detection ◽  
High Performance ◽  
Hybrid Methods ◽  
Tomographic Reconstruction ◽  
Data Driven ◽  
Model Based ◽  
Edge Classification ◽  
Wide Range ◽  
Data Driven Approach ◽  
Sparse Approximations

Semantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox and limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method that is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model class of images, and the data-driven method of a suitably designed convolutional neural network. We show that it avoids the distracted supervision paradox and achieves high performance in semantic edge detection. In addition, our approach requires significantly fewer parameters than a pure data-driven approach. Finally, we present several applications such as tomographic reconstruction and show that our approach significantly outperforms former methods, thereby also indicating the value of such hybrid methods for biomedical imaging.

scholarly journals A data-driven approach to measure restaurant performance by combining online reviews with historical sales data

2021 ◽  
Vol 94 ◽  
pp. 102830
Author(s):  
Elizabeth Fernandes ◽  
Sérgio Moro ◽  
Paulo Cortez ◽  
Fernando Batista ◽  
Ricardo Ribeiro
Keyword(s):  
Online Reviews ◽  
Data Driven ◽  
Sales Data ◽  
Data Driven Approach
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