In 2025, leading marketplaces were the primary source for starting to search for products online worldwide, along with search engines. According to a survey, roughly ***percent of*online shoppers searched for products through these channels. Online supermarkets and grocers followed, with ***percent of respondents. Popularity contest Online shopping has become increasingly popular globally. In various countries, including the United Kingdom, the United States, Germany, and many others, consumers have stated that they prefer to shop online rather than in-store. On a weekly basis, however, in European countries, offline shopping is still more popular among consumers. Germany had the largest share of weekly online shoppers, with ** percent of consumers. The preference for online shopping also depends on the product category and shopping events occurring at the time. Over ** percent of consumers prefer to use the internet over in-store shopping to complete their holiday and entertainment purchases. It is a preference While marketplaces are the primary source for consumers to search for products online, they are also the leading source for online shopping inspiration in 2024. Around ** percent of global consumers expressed their preference for marketplaces over any other online channel as a source of inspiration for their upcoming purchases. Consumers in different regions in the world tend to prefer different marketplaces, with consumers in Europe, the United States, and the United Kingdom preferring to use Amazon. The most visited marketplace in China was Taobao, Alibaba's B2C e-commerce platform. In Latin America, consumers use the local online marketplace Mercado Libre.

In most cases, online product search doesn't start on Google. In 2022, only ** percent of Italian shoppers reported to have looked for a product on the search engine. The remaining ** percent browsed on Amazon. Likewise, ** percent of Spanish shoppers looked for products on Amazon website and ** percent of German respondents did the same.

trec-product-search/product-search-2023-queries dataset hosted on Hugging Face and contributed by the HF Datasets community

Across all product categories, Amazon was the place where online shoppers in the United States most often began searching for specific products in 2022. For household products, ** percent of shoppers reported beginning their searching on the e-commerce giant's platform. Additionally, ** percent started their household item searches on Walmart's online platform. Fashion e-commerce in the U.S. The internet, social media, and the proliferation of inexpensive clothing have opened doors to U.S. fashion e-commerce like never before. The U.S. apparel, footwear, and accessories retail e-commerce market is worth a remarkable *** billion U.S. dollars, according to 2021 estimates, and it is set to surpass the *** billion dollar mark by 2025. Millennials shaping the future of U.S. e-commerce In general, Millennials are hyper-connected and better educated than previous generations. Over the past decade, they have become the largest generation group in the U.S. Also known as Generation Y, Millennials are more tech-savvy consumers than their antecessors. In 2019, people born between 1983 and 1998 were found to be more influenced by bloggers when buying apparel than previous generations. Millennials also outrank Gen X-ers and baby boomers in digital buyer penetration in the United States, with over ** percent as of ********.

This dataset provides comprehensive access to product search results from Google Shopping in real-time. Search and compare products, offers, and reviews across multiple major retailers and sources. Perfect for e-commerce applications, price comparison tools, and product discovery platforms. The dataset is delivered in a JSON format via REST API.

If you are looking for a challenging data to work on, then good luck with this.

Data Collection

• We have scraped data from different online vendors, of different categories for our project.
• The data includes all the information found on the page for a product.
• Since the data has been scraped it needs some preprocessing.

Purpose

Main goal of this dataset is to bring a search engine and a recommendation system that clusters data from different vendors without any bias towards one vendor. Biasness happens because the data format of products from one vendor are much alike and therefore it becomes difficult to recommend products across different vendors.

Applications

This dataset can be used to create - Search Engine: that takes a user query or keywords and finds relevant products. - Search Engine with Filters: you can add filters of different specs. As the are no explicit specs in the dataset, rather they are in JSON formal in a column, it becomes a challenge to filter out with desired specs - Recommendation System: You can use content based filtering for recommendation but again you have to avoid bias towards one vendor, as it happens because of similarity of keywords intra vendors

The product search track focuses on IR tasks in the world of product search and discovery. This track seeks to understand what methods work best for product search, improve evaluation methodology, and provide a reusable dataset which allows easy benchmarking in a public forum.

As of the second quarter of 2023, globally, the channel in which consumers started their online shopping journey was search engines. A total ** percent of shoppers worldwide used search engines such as Google to begin searching for products, while ** percent opted to start the shopping journey using a specific online store.

This Data set is comprised of product titles and names scraped from various sites. It is just to have a basic idea of how we can make a search system without string matching. This is for learning and for my own reference but I encourage if anybody wants to do something with it or want to improve any of my notebook or data can go ahead.

Dataset

This dataset was created by Abhishek Mungoli

Contents

trec-product-search/Product-Search-Triples dataset hosted on Hugging Face and contributed by the HF Datasets community

trec-product-search/product-search-corpus dataset hosted on Hugging Face and contributed by the HF Datasets community

trec-product-search/product-search-2024-queries dataset hosted on Hugging Face and contributed by the HF Datasets community

Real-time product search trends and consumer demand analytics across Amazon's marketplace with category and geographic granularity for institutional investment research.

trec-product-search/product-search-2025-test-queries dataset hosted on Hugging Face and contributed by the HF Datasets community

TREC 2025 Product Recommendation Data

This is the data for the recommendation task for the TREC 2025 Product Search and Recommendation Task. The initial directory contains the initial corpus and training data release. This may be updated as we get further along in the timeline.

[!NOTE] This data is derived from the Amazon ESCI and M2 data sets, each under the Apache license (version 2.0).

Large Walmart Products Dataset is an essential resource for businesses, analysts, and developers seeking detailed insights into Walmart’s vast product catalog. This dataset includes extensive information on Walmart products, such as product names, descriptions, prices, categories, brand information, ratings, and customer reviews.

With Walmart being one of the largest retailers globally, this dataset provides a unique opportunity to study consumer trends, perform competitive pricing analysis, and develop e-commerce solutions. For startups and established businesses, the dataset is ideal for market research, inventory management insights, and enhancing product discovery mechanisms.

AI and machine learning practitioners can use this dataset to build recommendation systems, predictive pricing algorithms, and sentiment analysis models. Its structured format ensures smooth integration with Python, R, and other data analytics tools, making it user-friendly for data visualization and predictive modeling.

Walmart Products Dataset is also an invaluable resource for retail analysts and e-commerce marketers aiming to optimize product positioning or analyze buying behaviors. Its broad coverage across categories like groceries, electronics, fashion, and home essentials provides a holistic view of Walmart’s inventory.

Key Features:

• Extensive Product Information: Details on pricing, discounts, availability, and ratings.
• Diverse Applications: Suitable for AI models, trend analysis, and market research.
• Retail Insights: Explore consumer preferences and popular product trends.

Whether you're developing an AI-driven product search engine or conducting a pricing strategy study, the Large Walmart Products Dataset equips you with the data you need to succeed in a competitive market.

BigBox API provides reliable, real-time Home Depot product, category, reviews, and offers data. All data includes comprehensive coverage of each of the search results in a cleanly structured output.

You can originate your request from any zip code (US) to see results as they would appear to customers in the specified location i.e. shipping info. BigBox APIs high-capacity, global infrastructure assures you the highest level of performance and reliability. For easy integration with your Home Depot data apps and services, data is delivered in JSON or CSV format.

Data is retrieved by search term, search results page URL, or for single products, by the Home Depot item ID or by global identifiers such as GTIN, ISBN, UPC and EAN. GTIN-based requests work by looking up the GTIN/ISBN/UPC on Home Depot first, then retrieving the product details for the first matching item ID.

So what's in the data from BigBox API?

Product: - Item & parent ID - UPC - Store SKU - In-store bay &/or aisle - Product specifications - Description - Imagery - Product videos - Buy Box winner: price and fulfillment info - Rating & reviews count - Descriptive attributes

Search results: - Product details per search result: - Position - Related queries - Pagination - Facets

How can BigBox API be used? - Product listing management - Price monitoring - Category & product trends monitoring - Market research & competitor intelligence - Location-specific shipping data - Rank tracking on Home Depot

...and more, depending on your request parameters or the search result.

Who uses BigBox API? This data is leveraged by software developers, marketers & business owners, sales & business development teams, researchers, and data analysts & engineers, in ecommerce, other retail business, agencies and SaaS platforms.

Anyone in your organization who works with your digital presence can develop business intelligence and strategy using this advanced product data.

In 2025, three in ten global consumers preferred to search for luxury items via online marketplaces. Around ** percent of respondents used search engines, and roughly ** percent admitted to preferring brand websites to find luxury products.

Search and Product Discovery Market Outlook

According to our latest research, the global Search and Product Discovery market size reached USD 7.8 billion in 2024, driven by the rapid digital transformation across sectors and the increasing importance of personalized customer experiences. The market is projected to grow at a robust CAGR of 13.2% from 2025 to 2033, reaching an estimated USD 23.1 billion by 2033. This growth is fueled by advancements in artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), which are revolutionizing how consumers interact with digital platforms and discover products online. As per our latest research, major growth drivers include the proliferation of e-commerce, the demand for enhanced customer engagement, and the rising adoption of cloud-based solutions.

A primary factor propelling the growth of the Search and Product Discovery market is the exponential increase in online shopping and digital commerce activities worldwide. Retailers and brands are prioritizing seamless search functionalities and intuitive product discovery tools to improve customer retention and conversion rates. The integration of AI-powered recommendation engines and personalized search algorithms has enabled businesses to deliver tailored product suggestions, thereby enhancing the overall user experience and maximizing revenue opportunities. Additionally, the surge in mobile commerce and voice-assisted shopping is pushing organizations to invest in advanced search technologies that cater to evolving consumer preferences and behaviors.

Another significant growth factor is the ongoing evolution of data analytics and machine learning capabilities. Companies are leveraging vast amounts of customer data to gain actionable insights, enabling them to refine their search and recommendation systems continuously. The implementation of NLP allows users to interact with platforms using natural language queries, making product discovery more intuitive and user-friendly. Furthermore, the adoption of advanced analytics helps businesses identify emerging trends, optimize inventory management, and streamline supply chains, all of which contribute to the expansion of the Search and Product Discovery market.

The increasing focus on omnichannel retail strategies is also driving market expansion. As consumers expect consistent and personalized experiences across online and offline touchpoints, businesses are integrating search and product discovery solutions across their digital and physical channels. This approach not only improves customer satisfaction but also provides retailers with a holistic view of consumer behavior. The rise of social commerce and the convergence of entertainment and shopping experiences are further accelerating the adoption of sophisticated search technologies, positioning the market for sustained growth in the coming years.

From a regional perspective, North America currently leads the Search and Product Discovery market due to its mature e-commerce ecosystem and high adoption of advanced technologies. However, Asia Pacific is witnessing the fastest growth, fueled by the rapid digitalization of retail, increasing internet penetration, and the emergence of tech-savvy consumer bases in countries like China and India. Europe is also experiencing steady growth, supported by strong investments in digital infrastructure and a robust presence of leading retail brands. Latin America and the Middle East & Africa are gradually catching up, with increasing investments in e-commerce platforms and digital transformation initiatives.

As the market continues to evolve, the emergence of Next Generation Search Engines is set to redefine the landscape of digital discovery. These advanced search engines leverage cutting-edge technologies such as AI, ML, and NLP to deliver highly personalized and context-aware search experiences. By understanding user intent and preferences, these engines can provide more accurate and relevant results, enhancing user satisfaction and engagement. The integration of visual and voice search capabilities further enriches the user experience, allowing for more intuitive interactions. As businesses strive to differentiate themselves in a competitive market, the adoption of next-generation search technologies will be cruci