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

Artificial consciousness and the consciousness-attention dissociation

Artificial Intelligence is at a turning point, with a substantial increase in projects aiming to implement sophisticated forms of human intelligence in machines. This research attempts to model specific forms of intelligence through brute-force search heuristics and also reproduce features of human perception and cognition, including emotions. Such goals have implications for artificial consciousness, with some arguing that it will be achievable once we overcome short-term engineering challenges. We believe, however, that phenomenal consciousness cannot be implemented in machines. This becomes clear when considering emotions and examining the dissociation between consciousness and attention in humans. While we may be able to program ethical behavior based on rules and machine learning, we will never be able to reproduce emotions or empathy by programming such control systems—these will be merely simulations. Arguments in favor of this claim include considerations about evolution, the neuropsychological aspects of emotions, and the dissociation between attention and consciousness found in humans. Ultimately, we are far from achieving artificial consciousness

Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

Empiricism without Magic: Transformational Abstraction in Deep Convolutional Neural Networks

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain