Towards A Better Design of Online Marketplaces

Online markets are staggering in volume and variety. These online marketplaces are transforming lifestyles, expanding the boundaries of conventional businesses, and reshaping labor force structures. To fully realize their potential, online marketplaces must be designed carefully. However, this is a significant challenge. This dissertation studies individual behavior and interactions in online marketplaces, and examines how to enhance efficiency and outcomes of these online marketplaces by providing actionable operational policy recommendations. An important question in the context of open-ended innovative service marketplaces is how to manage information when specifying design problems to achieve better outcomes. Chapter 1 investigates this problem in the context of online crowdsourcing contests where innovation seekers source innovative products (designs) from a crowd of competing solvers (designers). We propose and empirically test a theoretical model featuring different types of information in the problem specification (conceptual objectives, execution guidelines), and the corresponding impact on design processes and submission qualities. We find that, to maximize the best solution quality in crowdsourced design problems, seekers should always provide more execution guidelines, and only a moderate number of conceptual objectives. Building on the same research setting, Chapter 2 looks into another important yet challenging problem---how the innovation seeker should provide interim performance feedback to the solvers in online service marketplaces where seekers and solvers can interact dynamically. In particular, we study whether and when the seeker should provide such interim performance feedback. We empirically examine these research questions using a dataset from a crowdsourcing platform. We develop and estimate a dynamic structural model to understand contestants’ behavior, compare alternative feedback policies using counter-factual simulations, and find providing feedback throughout the contest may not be optimal. The late feedback policy, i.e., providing feedback only in the second half of the contest, leads to a better overall contest outcome. Moving to a wider application, Chapter 3 leverages consumer clickstream information in e-commerce marketplaces to help market organizers improve demand estimation and pricing decisions. These decisions can be challenging, as e-commerce marketplaces offer an astonishing variety of product choices and face extremely diversified consumer decision journeys. We provide a novel solution to these challenges by combining econometric and machine learning (Graphical Lasso) approaches, leveraging customer clickstream information to learn the product correlation network, and creating high-dimensional choice models that easily scale and allow for flexible substitution patterns. Our model offers better in- and out-of-sample demand forecasts and enhanced pricing recommendations in various synthetic datasets and in a real-world empirical setting.PHDBusiness AdministrationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163283/1/jiangzh_1.pd

AudioFormer: Audio Transformer learns audio feature representations from discrete acoustic codes

We propose a method named AudioFormer,which learns audio feature representations through the acquisition of discrete acoustic codes and subsequently fine-tunes them for audio classification tasks. Initially,we introduce a novel perspective by considering the audio classification task as a form of natural language understanding (NLU). Leveraging an existing neural audio codec model,we generate discrete acoustic codes and utilize them to train a masked language model (MLM),thereby obtaining audio feature representations. Furthermore,we pioneer the integration of a Multi-Positive sample Contrastive (MPC) learning approach. This method enables the learning of joint representations among multiple discrete acoustic codes within the same audio input. In our experiments,we treat discrete acoustic codes as textual data and train a masked language model using a cloze-like methodology,ultimately deriving high-quality audio representations. Notably,the MPC learning technique effectively captures collaborative representations among distinct positive samples. Our research outcomes demonstrate that AudioFormer attains significantly improved performance compared to prevailing monomodal audio classification models across multiple datasets,and even outperforms audio-visual multimodal classification models on select datasets. Specifically,our approach achieves remarkable results on datasets including AudioSet (2M,20K),and FSD50K,with performance scores of 53.9,45.1,and 65.6,respectively. We have openly shared both the code and models: https://github.com/LZH-0225/AudioFormer.git.Comment: 9 pages, 4 figure

The Role of Problem Specification in Crowdsourcing Contests for Design Problems: A Theoretical and Empirical Analysis

This paper studies the role of seekers' problem specification in crowdsourcing contests for design problems. Platforms hosting design contests offer detailed guidance for seekers to specify their design problems when launching a contest. Yet, problem specification in such crowdsourcing contests is something the theoretical and empirical literature has largely overlooked. We aim to fill this gap by offering an empirically-validated model to generate insights for the provision of information at contest launch. We develop a game-theoretic model featuring different types of information (categorized as “conceptual objectives” or “execution guidelines”) conveyed in problem specifications, and assess their impact on design processes. Real-world data is used to empirically test hypotheses generated from the model, and a quasi-natural experiment provides further empirical evidence for our predictions and recommendations. We show theoretically and verify empirically that, with more conceptual objectives disclosed in the problem specification, the number of participants in a contest decreases, but the trial effort provision by each participant does not change; with more execution guidelines disclosed in the problem specification, the trial effort provision by each participant increases, but the number of participants in a contest does not change. With that knowledge, we are able to formulate seekers' optimal decisions on problem specifications, and find that, to maximize the expected quality of the best solution to crowdsourced design problems, seekers should always provide more execution guidelines, and only a moderate number of conceptual objectives.https://deepblue.lib.umich.edu/bitstream/2027.42/146143/1/1388_Jiang.pd

Influence of dust on temperature measurement using infrared thermal imager

Temperature measurement by infrared thermal imager is an attractive technique in many fields, and it is of great importance to ensure the measurement accuracy of the infrared thermal imager. Aiming at the influence of dust on the temperature measurement of infrared thermal imager, this paper summarized the dust influence into three categories: dust on the surface of the measured object, dust on the infrared thermal imager’s lens and dust in the optical path between the measured object and the infrared thermal imager, and conducted three dust experiments. To quantify the measurement errors caused by dust, the infrared thermal image features that are affected by dust are extracted and a compensation model is established based on polynomial regression. The results indicate that dust can introduce measurement errors of infrared thermal imager and the proposed compensation method can compensate for the measurement errors caused by dust and improve the accuracy of infrared thermal imager

Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme

BACKGROUND: Gene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene expression data, usually in the form of large matrix, may contain missing values. The downstream analysis methods that postulate complete matrix input are thus not applicable. Several methods have been developed to solve this problem, such as K nearest neighbor impute method, Bayesian principal components analysis impute method, etc. In this paper, we introduce a novel imputing approach based on the Support Vector Regression (SVR) method. The proposed approach utilizes an orthogonal coding input scheme, which makes use of multi-missing values in one row of a certain gene expression profile and imputes the missing value into a much higher dimensional space, to obtain better performance. RESULTS: A comparative study of our method with the previously developed methods has been presented for the estimation of the missing values on six gene expression data sets. Among the three different input-vector coding schemes we tried, the orthogonal input coding scheme obtains the best estimation results with the minimum Normalized Root Mean Squared Error (NRMSE). The results also demonstrate that the SVR method has powerful estimation ability on different kinds of data sets with relatively small NRMSE. CONCLUSION: The SVR impute method shows better performance than, or at least comparable with, the previously developed methods in present research. The outstanding estimation ability of this impute method is partly due to the use of the most missing value information by incorporating orthogonal input coding scheme. In addition, the solid theoretical foundation of SVR method also helps in estimation of performance together with orthogonal input coding scheme. The promising estimation ability demonstrated in the results section suggests that the proposed approach provides a proper solution to the missing value estimation problem. The source code of the SVR method is available from for non-commercial use