User-Generated Content (UGC) on social media platforms is changing the way consumers shop for goods. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star...

Learning to Rank (LeToR) is an important class of machine learning problems that focuses on finding an optimal sequence of documents as a function of some user query. In this project we consider hotel search and click-through data provided by the popular travel website Expedia.com, with the goal of developing a model that will provide a list of hotels ranked by highest likelihood of customer pu...

This workshop brings together philosophers and psychologists and fo-cuses on investigating conditionals from formal and empirical points of views. The topics (listed in alphabetical order) include but are not restricted to: • causality and conditionals • conditional structures • experimental paradigms for conditionals • learning conditionals (i.e., conditionalizing on conditionals) • probabilis...

Information seeking in an online shopping environment is different from classical relevance-based information retrieval. In this paper, we focus on understanding how humans seek information and make economic decisions, when interacting with an array of choices in an online shopping environment. Our study is instantiated on a unique dataset of US hotel reservations from Travelocity.com. Current ...

Mobile commerce has changed the decision environment for users who intend to reserve a preferred hotel. This study aims investigate factors that affect dynamic click-through (CTD) in mobile online travel agency (OTA) search engines. We propose Bayesian inference framework model individual-level users’ CTDs and examine effects of item position, price, cost, use refinement tools. The uses real-wo...

I this paper, we study the effects of three different kinds of search engine rankings on consumer behavior and search engine revenues: direct ranking effect, interaction effect between ranking and product ratings, and personalized ranking effect. We combine a hierarchical Bayesian model estimated on approximately one million online sessions from Travelocity, together with randomized experiments...

In this paper, we examine how different ranking and personalization mechanisms on product search engines influence consumer online search and purchase behavior. To investigate these effects, we combine archival data analysis with randomized field experiments. Our archival data analysis is based on a unique dataset containing approximately 1 million online sessions from Travelocity over a 3-mont...

Differences in culture, history, economy, and political and management systems may lead to differences in employee job attribute preferences across countries. To the extent that this is true, managers and designers of motivation systems must understand the preferences of local employees. This study provides information on the job attribute preferences of Chinese employees at a major internation...

Increasingly, user-generated product reviews, images and tags serve as a valuable source of information for customers making product choices online. An extant stream of work has looked at the economic impact of reviews. Typically, the impact of product reviews has been incorporated by numeric variables representing the valence and volume of reviews. In this paper, we posit that the information ...

In this paper, we consider a classification-based approach to the recommendation of user-generated product reviews. In particular, we develop review ranking techniques that allow the most helpful reviews for a particular product to be recommended, thereby facilitating users to readily asses the quality of the product in question. We apply a supervised machine learning approach to this task and ...