57 research outputs found
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.
AutoDev: Automated AI-Driven Development
The landscape of software development has witnessed a paradigm shift with the
advent of AI-powered assistants, exemplified by GitHub Copilot. However,
existing solutions are not leveraging all the potential capabilities available
in an IDE such as building, testing, executing code, git operations, etc.
Therefore, they are constrained by their limited capabilities, primarily
focusing on suggesting code snippets and file manipulation within a chat-based
interface. To fill this gap, we present AutoDev, a fully automated AI-driven
software development framework, designed for autonomous planning and execution
of intricate software engineering tasks. AutoDev enables users to define
complex software engineering objectives, which are assigned to AutoDev's
autonomous AI Agents to achieve. These AI agents can perform diverse operations
on a codebase, including file editing, retrieval, build processes, execution,
testing, and git operations. They also have access to files, compiler output,
build and testing logs, static analysis tools, and more. This enables the AI
Agents to execute tasks in a fully automated manner with a comprehensive
understanding of the contextual information required. Furthermore, AutoDev
establishes a secure development environment by confining all operations within
Docker containers. This framework incorporates guardrails to ensure user
privacy and file security, allowing users to define specific permitted or
restricted commands and operations within AutoDev. In our evaluation, we tested
AutoDev on the HumanEval dataset, obtaining promising results with 91.5% and
87.8% of Pass@1 for code generation and test generation respectively,
demonstrating its effectiveness in automating software engineering tasks while
maintaining a secure and user-controlled development environment
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