Learning from Seller Experiements in Online Markets

The internet has dramatically reduced the cost of varying prices, dis- plays and information provided to consumers, facilitating both active and passive experimentation. We document the prevalence of targeted pricing and auction design variation on eBay, and identify hundreds of thousands of experiments con- ducted by sellers across a wide array of retail products. We show how this type of data can be used to address questions about consumer behavior and market outcomes, and provide illustrative results on price dispersion, the frequency of over-bidding, the choice of reserve prices, ?buy now?options and other auction design parameters, and on consumer sensitivity to shipping fees. We argue that leveraging the experiments of market participants takes advantage of the scale and heterogeneity of online markets and can be a powerful approach for testing and measurement.

Large Scale Visual Recommendations From Street Fashion Images

We describe a completely automated large scale visual recommendation system for fashion. Our focus is to efficiently harness the availability of large quantities of online fashion images and their rich meta-data. Specifically, we propose four data driven models in the form of Complementary Nearest Neighbor Consensus, Gaussian Mixture Models, Texture Agnostic Retrieval and Markov Chain LDA for solving this problem. We analyze relative merits and pitfalls of these algorithms through extensive experimentation on a large-scale data set and baseline them against existing ideas from color science. We also illustrate key fashion insights learned through these experiments and show how they can be employed to design better recommendation systems. Finally, we also outline a large-scale annotated data set of fashion images (Fashion-136K) that can be exploited for future vision research

The rise of mobile devices has done little to change how we shop online, at least for now

In recent years, smartphones have changed the way that we interact with the web – and each other. But have they changed the way that we shop online? In a recent study of internet and mobile eBay transactions, Liran Einav, John Levin, Igor Popov and Neel Sundaresan, find that mobile adopters tended to be heavy eBay users, and tended to buy more using their mobiles, while their non-mobile purchases stayed largely the same. They also find that their mobile shopping behaviors in terms of prices and products are little different to those on eBay’s internet site

Consumer behavior in online shopping is affected by sales tax

Profits from the sales tax make up a large percentage of overall revenues in many states, making the estimated $10 billion lost each year to tax-free internet purchases particularly concerning. Liran Einav, Dan Knoepfle, Jonathan Levin, and Neel Sundaresan examine just how much the presence (or lack thereof) of a sales tax influences consumer behavior. He finds that online purchasing goes up by 1-2 percent for each percentage point increase in the state sales tax, and that an online sales tax does lead to a decline in purchases

Learning from Seller Experiments in Online Markets

The internet has dramatically reduced the cost of varying prices, displays and information provided to consumers, facilitating both active and passive experimentation. We document the prevalence of targeted pricing and auction design variation on eBay, and identify hundreds of thousands of experiments conducted by sellers across a wide array of retail products. We show how this type of data can be used to address questions about consumer behavior and market outcomes, and provide illustrative results on price dispersion, the frequency of over-bidding, the choice of reserve prices, "buy now" options and other auction design parameters, and on consumer sensitivity to shipping fees. We argue that leveraging the experiments of market participants takes advantage of the scale and heterogeneity of online markets and can be a powerful approach for testing and measurement.

Exploring and Evaluating Personalized Models for Code Generation

Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code. Transformers are usually pre-trained on large unsupervised corpora, learning token representations and transformations relevant to modeling generally available text, and are then fine-tuned on a particular downstream task of interest. While fine-tuning is a tried-and-true method for adapting a model to a new domain -- for example, question-answering on a given topic -- generalization remains an on-going challenge. In this paper, we explore and evaluate transformer model fine-tuning for personalization. In the context of generating unit tests for Java methods, we evaluate learning to personalize to a specific software project using several personalization techniques. We consider three key approaches: (i) custom fine-tuning, which allows all the model parameters to be tuned; (ii) lightweight fine-tuning, which freezes most of the model's parameters, allowing tuning of the token embeddings and softmax layer only or the final layer alone; (iii) prefix tuning, which keeps model parameters frozen, but optimizes a small project-specific prefix vector. Each of these techniques offers a trade-off in total compute cost and predictive performance, which we evaluate by code and task-specific metrics, training time, and total computational operations. We compare these fine-tuning strategies for code generation and discuss the potential generalization and cost benefits of each in various deployment scenarios.Comment: Accepted to the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022), Industry Track - Singapore, November 14-18, 2022, to appear 9 page