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With the prevalence of e-commence websites and the ease of online shopping, consumers are embracing huge amounts of various options in products. Undeniably, shopping is one of the most essential activities in our society and studying…
Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. However, such items are not necessarily the ones that maximize…
From social networks to supply chains, more and more aspects of how humans, firms and organizations interact is mediated by artificial learning agents. As the influence of machine learning systems grows, it is paramount that we study how to…
Since the public release of ChatGPT in November 2022, the AI landscape is undergoing a rapid transformation. Currently, the use of AI chatbots by consumers has largely been limited to image generation or question-answering language models.…
Lately, personalized marketing has become important for retail/e-retail firms due to significant rise in online shopping and market competition. Increase in online shopping and high market competition has led to an increase in promotional…
Thick two-sided matching platforms, such as the room-rental market, face the challenge of showing relevant objects to users to reduce search costs. Many platforms use ranking algorithms to determine the order in which alternatives are shown…
Consumers value keeping some information about them private from potential marketers. E-commerce dramatically increases the potential for marketers to accumulate otherwise private information about potential customers. Online marketers…
Many existing industrial recommender systems are sensitive to the patterns of user-item engagement. Light users, who interact less frequently, correspond to a data sparsity problem, making it difficult for the system to accurately learn and…
Housing scholars stress the importance of the information environment in shaping housing search behavior and outcomes. Rental listings have increasingly moved online over the past two decades and, in turn, online platforms like Craigslist…
We examine problems of ``intermediated implementation,'' in which a single principal can only regulate limited aspects of the consumption bundles traded between intermediaries and agents with hidden characteristics. An example is sales, in…
Recommendation system is a type of information filtering systems that recommend various objects from a vast variety and quantity of items which are of the user interest. This results in guiding an individual in personalized way to…
Second-hand markets have expanded rapidly with the growth of online consumer-to-consumer (C2C) platforms. A key feature of C2C markets is that sellers are typically non-professionals and often face uncertainty about the quality of the goods…
In the world of big data, many people find it difficult to access the information they need quickly and accurately. In order to overcome this, research on the system that recommends information accurately to users is continuously conducted.…
When agents are acting together, they may need a simple mechanism to decide on joint actions. One possibility is to have the agents express their preferences in the form of a ballot and use a voting rule to decide the winning action(s).…
Modern recommendation systems rely on the wisdom of the crowd to learn the optimal course of action. This induces an inherent mis-alignment of incentives between the system's objective to learn (explore) and the individual users' objective…
Interactions between pieces of information (entities) play a substantial role in the way an individual acts on them: adoption of a product, the spread of news, strategy choice, etc. However, the underlying interaction mechanisms are often…
Product personalization opens the door to price discrimination. A rich product line allows firms to better tailor products to consumers' tastes, but the mere choice of a product carries valuable information about consumers that can be…
Recommender systems face a critical challenge in the item cold-start problem, which limits content diversity and exacerbates popularity bias by struggling to recommend new items. While existing solutions often rely on auxiliary data, but…
When deployed, AI agents will encounter problems that are beyond their autonomous problem-solving capabilities. Leveraging human assistance can help agents overcome their inherent limitations and robustly cope with unfamiliar situations. We…
We study market interactions in which buyers are allowed to credibly reveal partial information about their types to the seller. Previous recent work has studied the special case of one buyer and one good, showing that such communication…