As a representative information retrieval task, site recommendation, which aims at predicting the optimal sites for a brand or an institution to open new branches in an automatic data-driven way, is beneficial and crucial for brand development in modern business. However, there is no publicly available dataset so far and most existing approaches are limited to an extremely small scope of brands, which seriously hinders the research on site recommendation. Therefore, we collect, construct and release an open comprehensive dataset, namely OpenSiteRec, to facilitate and promote the research on site recommendation. Specifically, OpenSiteRec leverages a heterogeneous graph schema to represent various types of real-world entities and relations in four international metropolises. To evaluate the performance of the existing general methods on the site recommendation task, we conduct benchmarking experiments of several representative recommendation models on OpenSiteRec. Furthermore, we also highlight the potential application directions to demonstrate the wide applicability of OpenSiteRec. We believe that our OpenSiteRec dataset is significant and anticipated to encourage the development of advanced methods for site recommendation. OpenSiteRec is available online at https://OpenSiteRec.github.io/.

Amazon has announced that Prime Day 2023 will begin on July 11th, but you don't have to wait until then to get a good deal. The company has started to roll out a few early Prime Day deals before the two-day shopping event officially commences, including, as expected, several discounts on its own devices and services. We've rounded up the best early access Prime Day deals we can find below. Remember that you'll need to subscribe to Prime to take advantage of many (but not all) of the offers, and that there's always a chance that prices drop lower during the event itself. For those with no interest in Prime, we've also included a few of the best tech deals from this week that aren't explicitly tied to the event.

The United States Federal Trade Commission has accused Amazon.com of enrolling millions of consumers into its paid subscription Amazon Prime service without their consent and making it hard for them to cancel, the latest action by the agency against the e-commerce giant in recent weeks. The FTC sued in Amazon in federal court in Seattle on Wednesday, alleging that "Amazon has knowingly duped millions of consumers into unknowingly enrolling in Amazon Prime." The FTC said Amazon used "manipulative, coercive or deceptive user-interface designs known as'dark patterns' to trick consumers into enrolling in automatically renewing Prime subscriptions." The lawsuit is one of several actions taken by President Joe Biden's administration intended to rein in the outsized market power of Big Tech firms as it tries to increase competition to create greater consumer protection. The FTC said Amazon Prime is the world's largest subscription programme, generating $25bn in revenue annually.

Amazon has announced the dates for its next annual shopping event. Prime Day 2023 will be on July 11th and 12th this year, beginning at 12AM PT/ 3AM ET on Tuesday, July 11th, and concluding at the end of Wednesday, July 12th. Prime Day isn't necessarily a perk of Amazon's subscription service, like access to Prime Video content, but most deals on Amazon during the two-day event are exclusively available to Prime members. The cost of Prime has increased quite a bit since its launch in 2005, and even in the past few years. An annual membership will set you back $139 right now, $20 more than its previous price.

We introduce and define the novel problem of multi-distribution information retrieval (IR) where given a query, systems need to retrieve passages from within multiple collections, each drawn from a different distribution. Some of these collections and distributions might not be available at training time. To evaluate methods for multi-distribution retrieval, we design three benchmarks for this task from existing single-distribution datasets, namely, a dataset based on question answering and two based on entity matching. We propose simple methods for this task which allocate the fixed retrieval budget (top-k passages) strategically across domains to prevent the known domains from consuming most of the budget. We show that our methods lead to an average of 3.8+ and up to 8.0 points improvements in Recall@100 across the datasets and that improvements are consistent when fine-tuning different base retrieval models. Our benchmarks are made publicly available.

Almost immediately after Jeff Bezos handed Amazon to Andy Jassy in July 2021, the trouble began. The company had been soaring post-pandemic, bolstered by overwhelming interest in e-commerce and the cloud, but a return to in-person life and a broad tech drawback changed it all fast. As Jassy took over, Amazon's share price plunged, customers started buying less, easy corporate deals became hard, and hard deals fell apart. While Bezos lived the good life, his anointed CEO dealt with the fallout. Next month, Jassy will reach the two-year point in his bumpy run as CEO. He's helped bring some stability to the company, but his record's been marked by approximately 27,000 layoffs, a rush to streamline operations, slowing cloud growth, and questions about the company's focus.

Summer can be a sleepy time for deals, but there was actually a fair amount of savings to be found on tech this week. Amazon's Prime Day is probably about a month away, but the company looked like they were getting a head start with discounts on Kindles, two Echo speakers, Fire TV devices and Blink mini cameras. Those prices may go lower during the event, but the savings are still good if you can't wait. Our favorite Sony headphones dropped back down to $348 and a few different Beats earbuds, including the Powerbeats Pro saw discounts of up to 36 percent. Apple's latest laptop, the 15-inch MacBook Air is already $100 off and last year's XPS 15 from Dell is currently $800 off. Here are the best deals from this week that you can still get today. Pair a smart speaker with a smart plug and you have the underpinnings of a smart home setup.

Since Google I/O in May, the company has heavily promoted its generative text and image AI tools to help people do everything from draft essays to create art. However, its core business model is selling ads and products. Today the company unveiled a new shopping tool that may help do exactly that. Now, customers in the United States can virtually "try on" women's tops. The company uses images of real models ranging from XXS to 3XL to wear AI-generated versions of clothes from hundreds of brands sold across Google, like Anthropologie, Everlane, and H&M.

Retail sales and price projections are typically based on time series forecasting. For some product categories, the accuracy of demand forecasts achieved is low, negatively impacting inventory, transport, and replenishment planning. This paper presents our findings based on a proactive pilot exercise to explore ways to help retailers to improve forecast accuracy for such product categories. We evaluated opportunities for algorithmic interventions to improve forecast accuracy based on a sample product category, Knitwear. The Knitwear product category has a current demand forecast accuracy from non-AI models in the range of 60%. We explored how to improve the forecast accuracy using a rack approach. To generate forecasts, our decision model dynamically selects the best algorithm from an algorithm rack based on performance for a given state and context. Outcomes from our AI/ML forecasting model built using advanced feature engineering show an increase in the accuracy of demand forecast for Knitwear product category by 20%, taking the overall accuracy to 80%. Because our rack comprises algorithms that cater to a range of customer data sets, the forecasting model can be easily tailored for specific customer contexts.

As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans. Our innate grasping system is prompt, accurate, flexible, and continuous across spatial and temporal domains. Few existing methods cover all these properties for robot grasping. In this paper, we propose AnyGrasp for grasp perception to enable robots these abilities using a parallel gripper. Specifically, we develop a dense supervision strategy with real perception and analytic labels in the spatial-temporal domain. Additional awareness of objects' center-of-mass is incorporated into the learning process to help improve grasping stability. Utilization of grasp correspondence across observations enables dynamic grasp tracking. Our model can efficiently generate accurate, 7-DoF, dense, and temporally-smooth grasp poses and works robustly against large depth-sensing noise. Using AnyGrasp, we achieve a 93.3% success rate when clearing bins with over 300 unseen objects, which is on par with human subjects under controlled conditions. Over 900 mean-picks-per-hour is reported on a single-arm system. For dynamic grasping, we demonstrate catching swimming robot fish in the water. Our project page is at https://graspnet.net/anygrasp.html