Reputation and feedback systems in online marketplaces are often biased, making it difficult to ascertain the quality of sellers. We use post-transaction, buyer-to-seller message traffic to detect signals of unsatisfactory transactions on eBay. We posit that a message sent after the item was paid for serves as a reliable indicator that the buyer may be unhappy with that purchase, particularly when the message included words associated with a negative experience. The fraction of a seller's message traffic that was negative predicts whether a buyer who transacts with this seller will stop purchasing on eBay, implying that platforms can use these messages as an additional signal of seller quality.
Leveraging the power of data and economic engineering to understand and advance e-commerce.
In proceedings of the Workshop on Log-based Personalization (the 4th WSCD workshop) at WSDM 2014
A Large Scale Query Logs Analysis for Assessing Personalization Opportunities in E-commerce Sites
Neel Sundaresan, Zitao Liu
Personalization offers the promise of improving online search and shopping experience. In this work, we perform a large scale analysis on the sample of eBay query logs, which involves 9.24 billion session data spanning 12 months (08/2012-07/2013) and address the following topics
(1) What user information is useful for personalization;
(2) Importance of per-query personalization
(3) Importance of recency in query prediction.
In this paper, we study these problems and provide some preliminary conclusions
Physician Incentives and Treatment Choices in Heart Attack Management
We estimate how physicians’ financial incentives affect their treatment choices in heart Attack management, using a large dataset of private health insurance claims. Different insurance plans pay physicians different amounts for the same services, generating the required variation in financial incentives.
We begin by presenting evidence that, unconditionally, plans that pay physicians more for more invasive treatments are associated with a considerably larger fraction of such treatments. To interpret this correlation as causal, we continue by showing that it survives conditioning on a rich set of diagnosis and provider-specific variables.
We perform a host of additional checks verifying that differences in unobservable patient or provider characteristics across plans are unlikely to be driving our results. We find that physicians’ treatment choices respond positively to the payments they receive, and that the response is quite large.
If physicians received bundled payments instead of fee-for-service incentives, for example, heart attack management would become considerably more conservative. Our estimates imply that 20 percent of patients would receive different treatments, physician costs would decrease by 27 percent, and social welfare would increase.
Tracking and recording users’ browsing behaviors on the web down to individual mouse clicks can create massive web session logs.While such web session data contain valuable information about user behaviors, the ever-increasing data size has placed a big challenge to analyzing and visualizing the data.
An efficient data analysis framework requires both powerful computational analysis and interactive visualization. Following the visual analytics mantra "Analyze first, show the important, zoom, filter and analyze further, details on demand", we introduce a two-tier visual analysis system, TrailExplorer2, to discover knowledge from massive log data.
The system supports a visual analysis process iterating between two steps: querying web sessions and visually analyzing the retrieved data. The query happens at the lower tier where terabytes of web session data are processed in a cluster.
At the upper tier, the extracted web sessions with much smaller scale are visualized on a personal computer for interactive exploration. Our system visualizes a sorted list of web sessions’ temporal patterns and enables data exploration at different levels of details.
The query visualization exploration process iterates until a satisfactory conclusion is achieved. We present two case studies of TrailExplorer2 using real world session data from eBay to demonstrate the system's effectiveness.
Web clickstream data are routinely collected to study how users browse the web or use a service. It is clear that the ability to recognize and summarize user behavior patterns from such data is valuable to e-commerce companies. In this paper, we introduce a visual analytics system to explore the various user behavior patterns reflected by distinct clickstream clusters.
In a practical analysis scenario, the system first presents an overview of clickstream clusters using a Self-Organizing Map with Markov chain models.
Then the analyst can interactively explore the clusters through an intuitive user interface. He can either obtain summarization of a selected group of data or further refine the clustering result. We evaluated our system using two different datasets from eBay.
Analysts who were working on the same data have confirmed the system’s effectiveness in extracting user behavior patterns from complex datasets and enhancing their ability to reason.