The internet offers a massive repository of unstructured information, but it's a significant challenge to convert this into a structured format. At Pinterest, the ability to accurately extract structured product data from e-commerce websites is essential to enhance user experiences and improve content distribution. In this paper, we present Pinterest's system for attribute extraction, which achieves remarkable accuracy and scalability at a manageable cost. Our approach leverages a novel webpage representation that combines structural, visual, and text modalities into a compact form, optimizing it for small model learning. This representation captures each visible HTML node with its text, style and layout information. We show how this allows simple models such as eXtreme Gradient Boosting (XGBoost) to extract attributes more accurately than much more complex Large Language Models (LLMs) such as Generative Pre-trained Transformer (GPT). Our results demonstrate a system that is highly scalable, processing over 1,000 URLs per second, while being 1000 times more cost-effective than the cheapest GPT alternatives.

Few maintenance tasks are as tedious and time-consuming as cleaning your swimming pool. The good news is that robots can now perform that job, leaving you more leisure time to actually swim in your pool. We've spotted some great deals ahead of Amazon's Prime Day sale next week, and we'll add more as they become available. The recently reviewed Airrobo PC10 is a mid-range robotic pool cleaner that did a very good job on our test pool, and Amazon has knocked 42% off its list price, taking it down to 350. While we haven't had an opportunity to review the Wybot C1 robotic pool cleaner, we have obtained good results with some of the company's other products.

If you're looking for an affordable Echo speaker to add Alexa to another room in your home, this Prime Day deal on the Echo Dot will be hard to beat. The Echo Dot (5th gen) has dropped to 23 for Prime Day, which is cheaper than it was during the July sales event. This tiny smart speaker has improved audio that competes with more expensive rivals like the HomePod mini. This Echo Dot model launched in 2022 with clearer vocals, deeper bass and more vibrant overall sound than previous generations. Save big on the 2022 Echo Dot.

Since 2022, Amazon has held a second Prime Day of sorts in October and that sale event is coming back this year, too. Prime Big Deal Days returns on October 8 and 9, but we're already starting to see some decent deals pop up across Amazon's site. As per usual, most of the deals we expected to see on October Prime Day will be exclusively for Prime members -- and some of the early Prime Day deals we're seeing now have followed suit. If you don't have a Prime membership, don't fret too much -- there are always a few discounts available for all shoppers. However, if you pay the 139 annual fee for Prime, now's the time to put it to even better use.

We knew Amazon would revive its Prime Big Deal Days sale event this fall, but we didn't know the exact dates until today. The online retailer announced that the sale event will return this year on October 8 and 9, giving us all the more reason to call it October Prime Day as we have done in years past. This is the third iteration of the fall sale event that Amazon has used as its (un)official kickoff to the holiday shopping season, and there are already discounts to be had as we see early October Prime Day deals pop up. Prime Day in July remains Amazon's marquee sale event for Prime members, but ever since its debut in 2022, October Prime Day provides subscribers with thousands of exclusive deals to shop during the two-day window. In turn, it also provides Amazon a way to boost sales during the same time period and, arguably more importantly, increase the number of overall Prime subscribers.

We knew Amazon would revive its Prime Big Deal Days sale event this fall, but we didn't know the exact dates until today. The online retailer announced that the sale event will return this year on October 8 and 9, giving us all the more reason to call it October Prime Day as we have done in years past. This is the third iteration of the fall sale event that Amazon has used as its (un)official kickoff to the holiday shopping season. Prime Day in July remains Amazon's marquee sale event for Prime members, but ever since its debut in 2022, October Prime Day provides subscribers with thousands of exclusive deals to shop during the two-day window. In turn, it also provides Amazon a way to boost sales during the same time period and, arguably more importantly, increase the number of overall Prime subscribers.

Non-parametric machine learning models, such as random forests and gradient boosted trees, are frequently used to estimate house prices due to their predictive accuracy, but such methods are often limited in their ability to quantify prediction uncertainty. Conformal Prediction (CP) is a model-agnostic framework for constructing confidence sets around machine learning prediction models with minimal assumptions. However, due to the spatial dependencies observed in house prices, direct application of CP leads to confidence sets that are not calibrated everywhere, i.e., too large of confidence sets in certain geographical regions and too small in others. We survey various approaches to adjust the CP confidence set to account for this and demonstrate their performance on a data set from the housing market in Oslo, Norway. Our findings indicate that calibrating the confidence sets on a \textit{locally weighted} version of the non-conformity scores makes the coverage more consistently calibrated in different geographical regions. We also perform a simulation study on synthetically generated sale prices to empirically explore the performance of CP on housing market data under idealized conditions with known data-generating mechanisms.

Leveraging an established exercise in negotiation education, we build a novel dataset for studying how the use of language shapes bilateral bargaining. Our dataset extends existing work in two ways: 1) we recruit participants via behavioral labs instead of crowdsourcing platforms and allow participants to negotiate through audio, enabling more naturalistic interactions; 2) we add a control setting where participants negotiate only through alternating, written numeric offers.Despite the two contrasting forms of communication, we find that the average agreed prices of the two treatments are identical. But when subjects can talk, fewer offers are exchanged, negotiations finish faster, the likelihood of reaching agreement rises, and the variance of prices at which subjects agree drops substantially. We further propose a taxonomy of speech acts in negotiation and enrich the dataset with annotated speech acts. We set up prediction tasks to predict negotiation success and find that being reactive to the arguments of the other party is advantageous over driving the negotiation.

Non-Fungible Tokens (NFTs) are non-interchangeable assets, usually digital art, which are stored on the blockchain. Preliminary studies find that female and darker-skinned NFTs are valued less than their male and lighter-skinned counterparts. However, these studies analyze only the CryptoPunks collection. We test the statistical significance of race and gender biases in the prices of CryptoPunks and present the first study of gender bias in the broader NFT market. We find evidence of racial bias but not gender bias. Our work also introduces a dataset of gender-labeled NFT collections to advance the broader study of social equity in this emerging market.

With the abundance of available data, many enterprises seek to implement data-driven prescriptive analytics to help them make informed decisions. These prescriptive policies need to satisfy operational constraints, and proactively eliminate rule conflicts, both of which are ubiquitous in practice. It is also desirable for them to be simple and interpretable, so they can be easily verified and implemented. Existing approaches from the literature center around constructing variants of prescriptive decision trees to generate interpretable policies. However, none of the existing methods are able to handle constraints. In this paper, we propose a scalable method that solves the constrained prescriptive policy generation problem. We introduce a novel path-based mixed-integer program (MIP) formulation which identifies a (near) optimal policy efficiently via column generation. The policy generated can be represented as a multiway-split tree which is more interpretable and informative than a binary-split tree due to its shorter rules. We demonstrate the efficacy of our method with extensive experiments on both synthetic and real datasets.