Product matching aims to identify identical or similar products sold on different platforms. By building knowledge graphs (KGs), the product matching problem can be converted to the Entity Alignment (EA) task, which aims to discover the equivalent entities from diverse KGs. The existing EA methods inadequately utilize both attribute triples and relation triples simultaneously, especially the interactions between them. This paper introduces a two-stage pipeline consisting of rough filter and fine filter to match products from eBay and Amazon. For fine filtering, a new framework for Entity Alignment, Relation-aware and Attribute-aware Graph Attention Networks for Entity Alignment (RAEA), is employed. RAEA focuses on the interactions between attribute triples and relation triples, where the entity representation aggregates the alignment signals from attributes and relations with Attribute-aware Entity Encoder and Relation-aware Graph Attention Networks. The experimental results indicate that the RAEA model achieves significant improvements over 12 baselines on EA task in the cross-lingual dataset DBP15K (6.59% on average Hits@1) and delivers competitive results in the monolingual dataset DWY100K. The source code for experiments on DBP15K and DWY100K is available at github (https://github.com/Mockingjay-liu/RAEA-model-for-Entity-Alignment).

Large Language Models (LLMs) have shown promise in structured prediction tasks, including regression, but existing approaches primarily focus on point estimates and lack systematic comparison across different methods. We investigate probabilistic regression using LLMs for unstructured inputs, addressing challenging text-to-distribution prediction tasks such as price estimation where both nuanced text understanding and uncertainty quantification are critical. We propose a novel quantile regression approach that enables LLMs to produce full predictive distributions, improving upon traditional point estimates. Through extensive experiments across three diverse price prediction datasets, we demonstrate that a Mistral-7B model fine-tuned with quantile heads significantly outperforms traditional approaches for both point and distributional estimations, as measured by three established metrics each for prediction accuracy and distributional calibration. Our systematic comparison of LLM approaches, model architectures, training approaches, and data scaling reveals that Mistral-7B consistently outperforms encoder architectures, embedding-based methods, and few-shot learning methods. Our experiments also reveal the effectiveness of LLM-assisted label correction in achieving human-level accuracy without systematic bias. Our curated datasets are made available at https://github.com/vnik18/llm-price-quantile-reg/ to support future research.

Here's a fun thought experiment; picture the amount of personal data you think tech companies keep on you. Now, realize it's actually way more than that (hmm, maybe this isn't that fun). Even as privacy and security become more talked about in consumer tech, the companies behind our favorite products are collecting more and more of our data. Well, if you want to know the information, say, Amazon has on you, there is a way to find out. To be clear, data collection is far from an Amazon-specific problem; it's pretty much par for the course when it comes to tech companies.

Share this post In this post, we are implementing a real-time application of Natural Language Processing. We are going to implement the Amazon review sentiment analysis project using NLTK Library and Machine Learning in the python programming language. After reading this post, you can able to learn how amazon figures out negative, positive, and neutral response and their percentages as shown at the end of every product in Amazon. I recommend that before going in deep with the project, first go to a product in amazon and see how the reviews are classified, and how the performance measured for a product. Amazon Product - Adidas Men Shoes Table of Contents What is the Sentiment Analysis?

Last week, we featured sales on gear from outdoor retailers who were hoping to get a jump on the creeping monolith that is Black Friday. Many other manufacturers and retailers are hoping to entice you into checking out their wares before you fall into a post-turkey stupor. If you're in the mood--or if you have better things to do on your holiday weekend than shop--we've rounded up more than a dozen of our favorite deals to check out. It may not be as beautiful as the Nest, but the Ecobee4 was our smart home thermostat of choice when we reviewed it last year. Temperature sensors ensure that the far reaches of your house remain unbaked (and unfrozen).

USA Today's Jefferson Graham suggests tips on the best way to shop through Amazon's home assistant, Alexa. LOS ANGELES -- With seven different choices in its Amazon Echo family, consumers are naturally confused about which smart speaker, if any, to buy. The entry-level Dot is the most affordable and Amazon's best seller, but has the worst sound. The video versions of Echo haven't caught on, but are now costing about the same as the regular, full-featured Echo. USA TODAY is here to help, with a guide to all seven speakers, pros and cons on each, and a summation at the end. Amazon's Monday/Tuesday Prime Day sale is expected to have the best deals on Amazon's own products, with especially good discounts on the Echo speakers.

The Echo was the first but there are a bunch of Alexa enabled Amazon products to choose from these days. So which one is the best for you? A link has been posted to your Facebook feed. The Echo was the first but there are a bunch of Alexa enabled Amazon products to choose from these days. So which one is the best for you?

Seeking to catch up to rivals in the realm of voice-controlled services, Apple soon will allow software developers to connect their apps to its Siri digital assistant for the first time, according to a report on Tuesday. Apple also may offer a device similar to the Amazon Echo voice-controlled speaker, according to the report on The Information tech news web site. The report, citing anonymous sources, said Apple will soon release a software development kit allowing other companies to integrate their apps and services with Siri, while the company is still developing the "smart" speaker project. Amazon, by contrast, has partnered with numerous other companies so that owners of its Echo speaker can use the device to call an Uber car, order a Domino's pizza or play music from Spotify. Google last week announced it too would offer a voice-controlled speaker device, dubbed Google Home, that would also be open to third party services.