Visual analytics and artificial intelligence for marketing

In today’s online environments, such as social media platforms and e-commerce websites, consumers are overloaded with information and firms are competing for their attention. Most of the data on these platforms comes in the form of text, images, or other unstructured data sources. It is important to understand which information on company websites and social media platforms are enticing and/or likeable by consumers. The impact of online visual content, in particular, remains largely unknown. Finding the drivers behind likes and clicks can help (1) understand how consumers interact with the information that is presented to them and (2) leverage this knowledge to improve marketing content. The main goal of this dissertation is to learn more about why consumers like and click on visual content online. To reach this goal visual analytics are used for automatic extraction of relevant information from visual content. This information can then be related, at scale, to consumer and their decisions

KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Understanding consumers’ emotions and sensory experience for beauty care products

Doctor of PhilosophyDepartment of Food, Nutrition, Dietetics and HealthMartin TalaveraUnderstanding consumer experience related to hedonic, sensory, and emotional aspects of products is the key to driving consumer-centric product design for the beauty care category. This dissertation conducted three independent studies aiming to explore consumer experience of beauty care products from two perspectives: liking and beyond liking (emotions), based on conventional sensory and consumer data and online product reviews. The objective of Chapter 2 was to develop an emotion lexicon that could be used to profile consumers’ emotional responses to beauty care products in sensory and consumer tests. The lexicon was developed in four main steps: sourcing terms from online product reviews, term identification and categorization, term refinement, and term validation. The final emotion lexicon consists of 37 positive emotions and 2 negative emotions. Recommendations on the application of this lexicon to each of the three categories of beauty care (skincare, hair care and makeup) were provided. The validated emotion lexicon from this study is readily applicable to other emotion research for skincare, hair care and makeup. Chapter 3 explored sensory drivers of liking and emotional associations for beauty care products. Hand creams were used as testing samples to be evaluated for sensory characteristics and consumer perception. First, the sensory space (aroma, appearance, texture & skinfeel) of twelve hand creams was profiled by a highly trained descriptive panel using a modified flavor/texture profile approach. Then, seven hand creams selected from the descriptive sensory space were rated for overall liking, emotions using the lexicon developed from Chapter 2, and consumer characterization using check-all-that-apply (CATA) in a home use test (HUT) with a hundred female consumers from the Kansas City area. Cluster analysis and external preference mapping identified three consumer clusters with different liking patterns: the thick & waxy-texture likers, mild scent & low-medium-thickness likers, and strong-scent likers. Consumers with different liking patterns differed in their emotional associations with sensory characteristics of hand creams. However, high intensities of certain aroma attributes seemed to elicit high-arousal emotions for all groups. The findings of this study could guide the development of new hand cream products targeting different consumer segments. Chapter 4 explored consumer experience for hand cream products from the “voice of consumers”-online product reviews. A total of 17, 581 reviews representing 46 hand creams of different brands, price points, and sensory attributes were collected from Amazon and Ulta Beauty using a scraping software. Topic modeling using Latent Dirichlet allocation (LDA) identified five major topics consumers mentioned in these online reviews: greasiness & residue of the product, scent/fragrances of the product, skin feel & efficacy of the product, consumers’ skin issues, and occasions when to apply the product. Term frequency–inverse document frequency (tf-idf) calculated for each rating group suggested that unpleasant scent and overall dissatisfied quality were the main reasons why consumers gave a rating lower than 4 stars. High efficacy and desirable skinfeel were the drivers for 5 stars. These findings highlighted the importance of sensory experience and perception of efficacy in consumers’ whole product experience

Preference Modeling in Data-Driven Product Design: Application in Visual Aesthetics

Creating a form that is attractive to the intended market audience is one of the greatest challenges in product development given the subjective nature of preference and heterogeneous market segments with potentially different product preferences. Accordingly, product designers use a variety of qualitative and quantitative research tools to assess product preferences across market segments, such as design theme clinics, focus groups, customer surveys, and design reviews; however, these tools are still limited due to their dependence on subjective judgment, and being time and resource intensive. In this dissertation, we focus on a key research question: how can we understand and predict more reliably the preference for a future product in heterogeneous markets, so that this understanding can inform designers' decision-making? We present a number of data-driven approaches to model product preference. Instead of depending on any subjective judgment from human, the proposed preference models investigate the mathematical patterns behind users’ choice and behavior. This allows a more objective translation of customers' perception and preference into analytical relations that can inform design decision-making. Moreover, these models are scalable in that they have the capacity to analyze large-scale data and model customer heterogeneity accurately across market segments. In particular, we use feature representation as an intermediate step in our preference model, so that we can not only increase the predictive accuracy of the model but also capture in-depth insight into customers' preference. We tested our data-driven approaches with applications in visual aesthetics preference. Our results show that the proposed approaches can obtain an objective measurement of aesthetic perception and preference for a given market segment. This measurement enables designers to reliably evaluate and predict the aesthetic appeal of their designs. We also quantify the relative importance of aesthetic attributes when both aesthetic attributes and functional attributes are considered by customers. This quantification has great utility in helping product designers and executives in design reviews and selection of designs. Moreover, we visualize the possible factors affecting customers' perception of product aesthetics and how these factors differ across different market segments. Those visualizations are incredibly important to designers as they relate physical design details to psychological customer reactions. The main contribution of this dissertation is to present purely data-driven approaches that enable designers to quantify and interpret more reliably the product preference. Methodological contributions include using modern probabilistic approaches and feature learning algorithms to quantitatively model the design process involving product aesthetics. These novel approaches can not only increase the predictive accuracy but also capture insights to inform design decision-making.PHDDesign ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145987/1/yanxinp_1.pd

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

User Modeling and User Profiling: A Comprehensive Survey

The integration of artificial intelligence (AI) into daily life, particularly through information retrieval and recommender systems, has necessitated advanced user modeling and profiling techniques to deliver personalized experiences. These techniques aim to construct accurate user representations based on the rich amounts of data generated through interactions with these systems. This paper presents a comprehensive survey of the current state, evolution, and future directions of user modeling and profiling research. We provide a historical overview, tracing the development from early stereotype models to the latest deep learning techniques, and propose a novel taxonomy that encompasses all active topics in this research area, including recent trends. Our survey highlights the paradigm shifts towards more sophisticated user profiling methods, emphasizing implicit data collection, multi-behavior modeling, and the integration of graph data structures. We also address the critical need for privacy-preserving techniques and the push towards explainability and fairness in user modeling approaches. By examining the definitions of core terminology, we aim to clarify ambiguities and foster a clearer understanding of the field by proposing two novel encyclopedic definitions of the main terms. Furthermore, we explore the application of user modeling in various domains, such as fake news detection, cybersecurity, and personalized education. This survey serves as a comprehensive resource for researchers and practitioners, offering insights into the evolution of user modeling and profiling and guiding the development of more personalized, ethical, and effective AI systems.Comment: 71 page