The Effect of Platform-developed AI Price Recommendations Adoption on E-Commerce Platform Sales Distribution

Past studies on the long-tail phenomenon in digital markets or platforms have examined how buyer-side technologies affect the sales distribution of products. However, these studies did not fully explain how emerging platform-developed AI pricing agents, as a seller-side technology, can affect the sales distribution of products on e-commerce platforms. To fill this gap, we use a proprietary dataset from an e-commerce platform in Asia to empirically quantify the effect of sellers’ adoption of platform-developed AI price recommendations on the aggregate sales distribution of products. By employing a Heckman-type model for product-level analysis, we find that AI pricing adoption leads to a more diverse sales distribution of products, and this effect is accentuated when market competition is more intense. Our category-level analysis affirms this finding. Mechanism evaluations reveal products’ prices recommended by AI pricing agents help improve sellers’ product visibility, conversation rate, and average quantity purchased

A Survey on Popularity Bias in Recommender Systems

Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-known items in a catalogue. Existing research, however, suggests that in many situations today's recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and we review existing approaches to detect, quantify and mitigate popularity bias in recommender systems. Our survey therefore includes both an overview of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. We furthermore critically discuss today's literature, where we observe that the research is almost entirely based on computational experiments and on certain assumptions regarding the practical effects of including long-tail items in the recommendations.Comment: Under review, submitted to UMUA

Understanding information diversity in the era of repurposable crowdsourced data

Organizations successfully leverage information technology for the acquisition of knowledge for decision-making through information crowdsourcing, which is gathering information from a group of people about a phenomenon of interest to the crowdsourcer. Information crowdsourcing has been used to drive business insight and scientific research, providing crowdsourcers access to information outside their traditional reach. Crowdsourcers seek high-quality data for their information crowdsourcing projects and require contributors who can provide data that meet predetermined requirements. Crowdsourcers recruit contributors with high levels of relevant knowledge or train contributors to ensure the quality of data they collect. However, when crowdsourced data needs to fit more than a single usage scenario because the requirements of the project changed or the data needs to be repurposed for tasks other than the one(s) for which it was initially collected, the ability of contributors to provide diverse data that can meet multiple requirements is also desirable. In this thesis, I investigate how the domain knowledge a contributor possesses affects the diversity and quality of data they report. Using an experiment in which 84 students randomly assigned to three knowledge conditions reported information about artificial stimuli, I found that explicitly trained contributors provided less diverse data than either implicitly trained or untrained contributors. In addition, I looked at the longitudinal effect of knowledge on the diversity of data reported by contributors. Using review data from Amazon.com and organism sighting data from NLNature.com (a citizen science data crowdsourcing platform), I studied the impact of knowledge on the diversity and quality of crowdsourced data. The results show that experience reduced the diversity and usefulness of contributed data. The study provides insights for crowdsourcers in industry and academia on how to manage and utilize their crowds effectively to collect high-quality reusable data

University catalog, 2016-2017

The catalog is a comprehensive reference for your academic studies. It includes a list of all degree programs offered at MU, including bachelors, masters, specialists, doctorates, minors, certificates, and emphasis areas. It details the university wide requirements, the curricular requirements for each program, and in some cases provides a sample plan of study. The catalog includes a complete listing and description of approved courses. It also provides information on academic policies, contact information for supporting offices, and a complete listing of faculty members. -- Page 3

University catalog, 2015-2016

The catalog is a comprehensive reference for your academic studies. It includes a list of all degree programs offered at MU, including bachelors, masters, specialists, doctorates, minors, certificates, and emphasis areas. It details the university wide requirements, the curricular requirements for each program, and in some cases provides a sample plan of study. The catalog includes a complete listing and description of approved courses. It also provides information on academic policies, contact information for supporting offices, and a complete listing of faculty members. -- Page 4

Virginia Commonwealth University Graduate Bulletin

Graduate bulletin for Virginia Commonwealth University for the academic year 2020-2021. It includes information on academic regulations, degree requirements, course offerings, faculty, academic calendar, and tuition and expenses for graduate programs