As of March 2025, Google represented 79.1 percent of the global online search engine market on desktop devices. Despite being much ahead of its competitors, this represents the lowest share ever recorded by the search engine in these devices for over two decades. Meanwhile, its long-time competitor Bing accounted for 12.21 percent, as tools like Yahoo and Yandex held shares of over 2.9 percent each. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of 2.02 trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly 348.16 billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than 63 percent of internet users in Russia used Yandex, whereas Google users represented little over 33 percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over 21 percent of users in Mexico said they used Yahoo.

Search Engine Market Outlook 2032

The global search engine market size was USD 205.48 Billion in 2023 and is projected to reach USD 507.37 Billion by 2032, expanding at a CAGR of 11.0 % during 2024–2032. The market growth is attributed to the growing use of social media data across the globe.



Rising use of social media data is a key driver of the search engine market. The continuous engagement of users with diverse social media platforms results in the generation of a vast amount of data. This data offers invaluable insights into user behavior, preferences, and emerging trends. Search engines leverage this data to deliver personalized and relevant search results.



The analysis of a user's social media activity allows search engines to gain a deeper understanding of their interests, thereby enabling them to deliver search results that are closely aligned with these interests. This enhances the user's search experience and increases the likelihood of user engagement and conversion for businesses. Furthermore, integrating social media data into search algorithms improve ad targeting, leading to effective advertising campaigns. This trend, offers an opportunity for businesses to enhance their search engine strategies and gain a competitive edge.

Impact of Artificial Intelligence (AI) in Search Engine Market

Artificial Intelligence has a significantly impact on search engine market, creating personalized and efficient user experience. Through machine learning algorithms, search engines have become adept at understanding user intent, thereby delivering accurate and relevant results. AI's ability to process and analyze large volumes of data has enhanced search engine optimization strategies, enabling businesses to better understand user behavior and preferences.



AI has facilitated the development of voice search technology, which is rapidly gaining popularity due to its convenience and efficiency. This has necessitated a shift in SEO strategies, with a greater focus on natural language. Additionally, AI's predictive capabilities have improved ad targeting, leading to higher conversion rates. The integration of AI in the search engine market has improved user experience and revolutionized digital marketing strategies. Fo

In January 2025, Google remained by far the most popular search engine in the UK, holding a market share of ***** percent across all devices. That month, Bing had a market share of approximately **** percent in second place, followed by Yahoo! with approximately **** percent. The EU vs Google Despite Google’s dominance of the search engine market, maintaining its position at the top has not been a smooth ride. Google’s market share saw a decline in the summer of 2018, plummeting to an all-time-low in July. The search engine experienced a similar dip in June and July 2017. These two low points coincided with the European Commission’s antitrust charges against the company, both of which were unprecedented in the now decade-long duel between both parties. As skepticism towards search engine platforms grows in line with public concern regarding censorship and data privacy, alternative services like Duckduckgo offer users both information protection and unfiltered results. Despite this, it still held less than *** percent of the industry’s market share as of June 2021. Perception of fake news in the UK According to a questionnaire conducted in the United Kingdom in 2018, **** percent of respondents had come across inaccurate news on social media at least once before. Rising concerns over fake news, or information which has been manipulated to influence the public has been a hot topic in recent years. The younger generation however, remains skeptical with nearly **** of Generation Z claiming to be either unconcerned about fake news, or believed that it did not exist altogether.

Crawler Based Search Engine Market Outlook

The global crawler based search engine market size was estimated to be USD 25 billion in 2023 and is projected to reach USD 75 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. This growth is driven by the increasing need for sophisticated search engine solutions in various industries such as e-commerce, BFSI, and healthcare. The demand for efficient data retrieval and the rising importance of search engine optimization (SEO) are significant factors fueling market expansion.

One of the primary growth factors for the crawler based search engine market is the exponential growth of data generated across different platforms. With the advent of big data and the Internet of Things (IoT), the amount of structured and unstructured data has surged, necessitating advanced search solutions that can efficiently index and retrieve relevant information. This has led to the adoption of crawler-based search engines, which are capable of handling large volumes of data and providing accurate search results quickly. Furthermore, the increasing reliance on digital platforms for business operations and customer interactions is also pushing companies to invest in robust search engine technologies.

Another contributing factor to the marketÂ’s growth is the rising importance of personalized search experiences. Modern consumers expect search engines to understand their preferences and deliver highly relevant results. Crawler-based search engines utilize advanced algorithms and artificial intelligence (AI) techniques to analyze user behavior and preferences, thereby offering personalized search experiences. This not only enhances user satisfaction but also boosts engagement and retention rates, making these search engines an attractive investment for businesses across various sectors.

Moreover, the growing emphasis on search engine optimization (SEO) and digital marketing strategies has further bolstered the demand for crawler-based search engines. Businesses are increasingly leveraging these search engines to optimize their online presence and improve their search engine rankings. By crawling and indexing web pages efficiently, these search engines enable businesses to gain insights into their website performance and make data-driven decisions to enhance their SEO strategies. This, in turn, drives market growth as companies strive to stay competitive in the digital landscape.

Insight Engines are becoming increasingly vital in the realm of data management and retrieval. These engines are designed to provide users with deeper insights by analyzing large datasets and delivering contextual information. As businesses generate vast amounts of data, Insight Engines help in transforming this data into actionable insights, enabling organizations to make informed decisions. They leverage advanced technologies such as natural language processing and machine learning to understand user queries and provide precise answers. This capability is particularly beneficial for industries that rely heavily on data-driven strategies, as it enhances the ability to uncover hidden patterns and trends within data.

Regionally, North America holds a significant share of the crawler-based search engine market, primarily due to the presence of major technology companies and the rapid adoption of advanced search solutions in the region. The Asia Pacific region is also expected to witness substantial growth during the forecast period, driven by the increasing digitization efforts and the rising number of internet users in countries like China and India. Additionally, Europe and Latin America are anticipated to contribute to market growth, supported by the growing emphasis on digital transformation and data-driven decision-making in these regions.

Component Analysis

The crawler-based search engine market can be segmented by component into software, hardware, and services. The software segment dominates the market, driven by the continuous advancements in search engine algorithms and the integration of artificial intelligence (AI) and machine learning (ML) technologies. Search engines are becoming more sophisticated, capable of understanding natural language queries and providing more accurate and relevant search results. The demand for such advanced software solutions is increasing as businesses seek to enhance their search capabilities and deliver better user experiences.

Comprehensive SERP Data

This dataset contains comprehensive search engine ranking data collected from Google and Bing, along with extracted technical and content features for analyzing search engine ranking algorithms.

  📊 Dataset Overview

Total Records: 14,465 search results Search Engines: Google (5,895 results) and Bing (8,570 results) Keywords: 500 diverse search queries Features: 20 features including technical scores, content analysis, and ranking metadata… See the full description on the dataset page: https://huggingface.co/datasets/goker/comp-serp-data.

This is a test collection for passage and document retrieval, produced in the TREC 2023 Deep Learning track. The Deep Learning Track studies information retrieval in a large training data regime. This is the case where the number of training queries with at least one positive label is at least in the tens of thousands, if not hundreds of thousands or more. This corresponds to real-world scenarios such as training based on click logs and training based on labels from shallow pools (such as the pooling in the TREC Million Query Track or the evaluation of search engines based on early precision).Certain machine learning based methods, such as methods based on deep learning are known to require very large datasets for training. Lack of such large scale datasets has been a limitation for developing such methods for common information retrieval tasks, such as document ranking. The Deep Learning Track organized in the previous years aimed at providing large scale datasets to TREC, and create a focused research effort with a rigorous blind evaluation of ranker for the passage ranking and document ranking tasks.Similar to the previous years, one of the main goals of the track in 2022 is to study what methods work best when a large amount of training data is available. For example, do the same methods that work on small data also work on large data? How much do methods improve when given more training data? What external data and models can be brought in to bear in this scenario, and how useful is it to combine full supervision with other forms of supervision?The collection contains 12 million web pages, 138 million passages from those web pages, search queries, and relevance judgments for the queries.

AI in Search Engines Market Outlook

As per our latest research, the global AI in Search Engines market size reached USD 8.2 billion in 2024, demonstrating robust momentum driven by the rapid adoption of artificial intelligence technologies across digital platforms. The market is expected to expand at a CAGR of 23.5% from 2025 to 2033, with the market size projected to reach USD 65.7 billion by 2033. This remarkable growth is primarily attributed to the escalating demand for enhanced search experiences, intelligent content discovery, and the integration of AI-powered features such as natural language processing and machine learning in both consumer and enterprise environments.

The accelerating proliferation of digital content, coupled with the increasing complexity of user queries, is a significant growth factor propelling the adoption of AI in Search Engines. Businesses and consumers alike are seeking more accurate, context-aware, and personalized search results, which traditional keyword-based algorithms struggle to provide. AI-driven search engines leverage advanced techniques such as deep learning and natural language processing to understand user intent, semantic context, and deliver highly relevant results. This trend is further amplified by the rise of voice-activated assistants, mobile search, and the demand for multi-modal search capabilities, all of which require sophisticated AI algorithms to function effectively.

Another critical driver for the AI in Search Engines market is the increasing emphasis on user experience and engagement. Enterprises are investing in AI-powered search technologies to improve website navigation, enhance e-commerce product discovery, and boost customer satisfaction. AI enables search engines to offer predictive suggestions, auto-complete, and personalized recommendations, leading to higher conversion rates and deeper user engagement. Additionally, the integration of AI in enterprise search platforms is helping organizations unlock value from unstructured data, streamline knowledge management, and support data-driven decision-making processes.

The rapid advancements in AI technologies, particularly in machine learning, deep learning, and computer vision, are significantly expanding the capabilities of modern search engines. These technologies enable the processing of diverse data types, including text, images, audio, and video, thus supporting a wide range of search applications beyond traditional web search. Furthermore, the growing investments in AI research and the increasing availability of cloud-based AI services are lowering the barriers to adoption, enabling even small and medium enterprises to harness the power of AI in their search solutions. This democratization of AI technology is expected to further fuel market growth in the coming years.

From a regional perspective, North America remains the largest market for AI in Search Engines, accounting for the highest revenue share in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology companies, advanced digital infrastructure, and early adoption of AI technologies are key factors driving market growth in these regions. Meanwhile, Asia Pacific is emerging as the fastest-growing market, supported by rapid digitization, increasing internet penetration, and substantial investments in AI research and development. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions gradually embrace AI-driven digital transformation.

Component Analysis

The AI in Search Engines market is segmented by component into Software, Hardware, and Services. The software segment dominates the market, accounting for the largest share in 2024. This dominance is attributed to the critical role of AI algorithms, frameworks, and platforms in powering intelligent search functionalities. AI software solutions encompass a wide range of technologies, including natural language processing engines, machine learning models, and deep learning frameworks, which are essential for understanding complex user queries, extracting insights from vast datasets, and delivering relevant search results. The continuous evolution of AI software, with frequent updates and innovations, ensures that search engines remain at the forefront of digital transformation.

The hardware segment, while smaller in comparison,

Based on a survey conducted in 2019 among internet users in the United States, the majority of adults (36 percent) admitted they would switch search engines if it meant getting better quality results. Furthermore, 33.7 percent stated that knowing their data was not being collected by a platform would also encourage them to make the switch. Other factors listed included 'having fewer ads' and a well designed interface. Overall, there was a noticeable lean toward search result quality and data privacy when it came to search engine selection.

Google leads despite user preference for increased privacy

Despite a strong consumer call for data protection, Google topped the list when it came to search engines with 93 percent of Americans surveyed reporting to having used the popular search giant at some point during the past 4 weeks. In comparison, the second most popular platform Yahoo! had only been used by 31 percent of those surveyed. Meanwhile DuckDuckGo, the search engine most known for protecting user data and search history had only been used by 8 percent. Mobile search figures lean even more in Google's favor. Here, a similar share (93 percent) of the market as of January 2021 belonged to Google, while approximately 3 percent was held by DuckDuckGo.

Growth expected for search advertising

With search engines playing a significant role in internet use be it on desktop or mobile, companies and search platforms alike are seeing an increased opportunity in the field of search engine advertising. Nationwide spend in the industry reached an impressive 58.2 billion U.S. dollars in 2020, and was forecast to further rise to 66.2 billion within the following year.

Search Engine Market Outlook

The search engine market size was valued at approximately USD 124 billion in 2023 and is projected to reach USD 258 billion by 2032, witnessing a robust CAGR of 8.5% during the forecast period. This growth is largely attributed to the increasing reliance on digital platforms and the internet across various sectors, which has necessitated the use of search engines for data retrieval and information dissemination. With the proliferation of smartphones and the expansion of internet access globally, search engines have become indispensable tools for both businesses and consumers, driving the market's upward trajectory. The integration of artificial intelligence and machine learning technologies into search engines is transforming the way search engines operate, offering more personalized and efficient search results, thereby further propelling market growth.

One of the primary growth factors in the search engine market is the ever-increasing digitalization across industries. As businesses continue to transition from traditional modes of operation to digital platforms, the need for search engines to navigate and manage data becomes paramount. This shift is particularly evident in industries such as retail, BFSI, and healthcare, where vast amounts of data are generated and require efficient management and retrieval systems. The integration of AI and machine learning into search engine algorithms has enhanced their ability to process and interpret large datasets, thereby improving the accuracy and relevance of search results. This technological advancement not only improves user experience but also enhances the competitive edge of businesses, further fueling market growth.

Another significant growth factor is the expanding e-commerce sector, which relies heavily on search engines to connect consumers with products and services. With the rise of e-commerce giants and online marketplaces, consumers are increasingly using search engines to find the best prices, reviews, and availability of products, leading to a surge in search engine usage. Additionally, the implementation of voice search technology and the growing popularity of smart home devices have introduced new dynamics to search engine functionality. Consumers are now able to conduct searches verbally, which has necessitated the adaptation of search engines to incorporate natural language processing capabilities, further driving market growth.

The advertising and marketing sectors are also contributing significantly to the growth of the search engine market. Businesses are leveraging search engines as a primary tool for online advertising, given their wide reach and ability to target specific audiences. Pay-per-click advertising and search engine optimization strategies have become integral components of digital marketing campaigns, enabling businesses to enhance their visibility and engagement with potential customers. The measurable nature of these advertising techniques allows businesses to assess the effectiveness of their campaigns and make data-driven decisions, thereby increasing their reliance on search engines and contributing to overall market growth.

The evolution of search engines is closely tied to the development of Ai Enterprise Search, which is revolutionizing how businesses access and utilize information. Ai Enterprise Search leverages artificial intelligence to provide more accurate and contextually relevant search results, making it an invaluable tool for organizations that manage large volumes of data. By understanding user intent and learning from past interactions, Ai Enterprise Search systems can deliver personalized experiences that enhance productivity and decision-making. This capability is particularly beneficial in sectors such as finance and healthcare, where quick access to precise information is crucial. As businesses continue to digitize and data volumes grow, the demand for Ai Enterprise Search solutions is expected to increase, further driving the growth of the search engine market.

Regionally, North America holds a significant share of the search engine market, driven by the presence of major technology companies and a well-established digital infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This growth can be attributed to the rapid digital transformation in emerging economies such as China and India, where increasing internet penetration and smartphone adoption are driving demand for search engines. Additionally, government initiatives to

Enterprise Search Engine Market Outlook

The global enterprise search engine market size was valued at USD 4.5 billion in 2023, and it is expected to reach USD 12.4 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12.1% during the forecast period. The rapid growth of this market can be attributed to various factors, including the increasing need for data management and the rising importance of enhancing the efficiency and productivity of organizations through efficient data retrieval systems.

One of the primary growth factors driving the enterprise search engine market is the exponential growth of data across various sectors. With organizations generating vast amounts of data daily, the need for effective data management and retrieval systems has become paramount. Enterprise search engines enable organizations to locate and retrieve information quickly and accurately, which is crucial for informed decision-making and maintaining competitive advantage. Furthermore, these systems can integrate with other enterprise applications, providing a seamless user experience.

Another significant growth driver is the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in enterprise search engines. AI and ML algorithms enhance the search capabilities of these systems by providing more accurate and relevant search results. They also offer predictive search functionalities, which can drastically reduce the time spent on searching for information. The advancements in natural language processing (NLP) are also enhancing the ability of search engines to understand and process human language, further boosting their efficiency.

The growing trend of cloud-based deployment is also playing a crucial role in the expansion of the enterprise search engine market. Cloud-based solutions offer several benefits, such as scalability, reduced operational costs, and ease of integration with other cloud services. This has led to an increasing preference for cloud-based enterprise search engines, especially among small and medium-sized enterprises (SMEs) that may lack the resources to invest in on-premises infrastructure. The increasing adoption of cloud services across various industries is thus providing a significant boost to the market.

Regionally, North America is anticipated to hold the largest market share throughout the forecast period. This is due to the presence of a large number of established players and the early adoption of advanced technologies in the region. The Asia Pacific region is expected to witness the highest growth rate, driven by the rapid digital transformation and increasing investments in IT infrastructure in countries like China and India. Europe is also expected to show significant growth due to stringent data governance regulations and the increasing need for data management solutions across various sectors.

Component Analysis

The enterprise search engine market is segmented into two primary components: software and services. The software segment encompasses the actual search engine platforms and technologies that organizations deploy to manage and retrieve data. Within this segment, advancements in AI and machine learning are significantly enhancing the capabilities of search engines. Modern enterprise search software incorporates AI algorithms to provide more accurate, relevant, and context-aware search results. These advancements are crucial for organizations that deal with vast and complex datasets, enabling them to streamline their operations and make data-driven decisions more effectively.

The services segment includes various professional services such as consulting, system integration, and support and maintenance. These services are essential for the successful deployment and operation of enterprise search engines. Consulting services help organizations identify their specific search requirements and design customized solutions. System integration services ensure that the search engine seamlessly integrates with the existing IT infrastructure and enterprise applications. Support and maintenance services are crucial for the ongoing performance and reliability of the search engine, ensuring that it continues to meet the organization's needs over time.

The software segment currently holds the largest market share, driven by the increasing demand for advanced search capabilities and the continuous development of new software solutions. The integration of AI and machine learning technologies into enterprise search software is a key trend, enhancing the ability to

Dataset from Visual Product Recognition Challenge 2023 served on AI-Crowd platform https://www.aicrowd.com/challenges/visual-product-recognition-challenge-2023

Organizers provided just testing datasets. As training, you could use any dataset, e.g.. Product 10 K or your own. You can get all the information about the dataset on the same page. Here is the main description

Testset format

Test set contains 2 files: gallery.csv and queries.csv.

gallery.csv defines the database of images from marketplaces. Each row contains the following information: - product_id - unique int32 identifier of product image that is used in result ranking NumPy array; - img_path - path to the product image in the "data" folder.

queries.csv defines a set of user images that will be used as queries to search the database. Each row contains the following information: - product_id - unique int32 identifier of user image that is used in result ranking NumPy array; - img_path - path to user image in "data" folder; - bbox_x, bbox_y, bbox_w, bbox_h - bounding box coordinates of the product in the user image.

The Enterprise SEO Platforms market is experiencing robust growth, driven by the increasing reliance of businesses on digital channels for customer acquisition and brand building. The market size in 2025 is estimated at $5 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected from 2025 to 2033. This growth is fueled by several key factors. Firstly, the ever-increasing complexity of search engine algorithms necessitates sophisticated SEO tools capable of managing large-scale website optimization efforts. Secondly, the rise of mobile-first indexing and the growing importance of voice search are forcing companies to adopt advanced SEO strategies, which in turn fuels demand for robust enterprise-grade platforms. Finally, the need for precise measurement and reporting of SEO performance is driving adoption of platforms that offer comprehensive analytics and data visualization capabilities. The market is segmented by platform type (navigational, transactional, informational) and user application (SMEs, large enterprises), with large enterprises representing a significant portion of the market due to their higher budgets and more complex SEO needs. The competitive landscape is highly dynamic, with established players like SEMrush and Ahrefs alongside emerging specialized platforms continuously innovating to meet evolving market demands. Geographic distribution shows a strong concentration in North America and Europe, reflecting the higher digital maturity and adoption rates in these regions. However, Asia-Pacific is expected to witness significant growth in the coming years as businesses in this region increasingly invest in digital marketing strategies. The future of the Enterprise SEO Platforms market appears promising, with continued growth driven by technological advancements and evolving search engine landscapes. The increasing demand for data-driven decision-making in SEO will further propel the adoption of advanced analytical tools. Furthermore, integration with other marketing platforms, such as CRM and marketing automation systems, will become increasingly crucial for seamless workflow and data-driven campaign optimization. This necessitates the development of more integrated and user-friendly platforms, placing further pressure on vendors to innovate and provide comprehensive, value-added services. Despite these positive trends, challenges remain, including the need for greater transparency in SEO reporting and the ongoing evolution of search engine algorithms, requiring constant adaptation and platform updates.

Introduction

In 2024, the Global AI Search Engine Market was valued at USD 17.3 billion and is projected to reach nearly USD 73.7 billion by 2034, expanding at a CAGR of 15.6% during 2025–2034. The growth is driven by the increasing integration of generative AI, natural language processing, and voice-enabled search technologies across industries. Organizations are investing heavily in AI-based platforms to enhance search relevance, improve personalization, and support multilingual accessibility, which is accelerating adoption worldwide.

One of the top driving factors behind this market’s growth is the explosion of digital data and the need for intelligent processing to sort through vast volumes efficiently. Users now want search results that are tailored to their unique preferences and context, which AI technologies like deep learning enable. Another important driver is the increased adoption of cloud computing, providing the infrastructure needed to support scalable AI search solutions. The rise of voice assistants and visual search methods also expands how users interact with these search engines, contributing further to demand.

Demand for AI search engines shows significant growth not only among consumers but also enterprises. Consumers rely on AI search for everyday needs such as finding local services or translating languages, while enterprises use AI-powered search tools to quickly access complex data across departments, improving operational efficiency and decision-making. Large enterprises lead adoption due to their need for handling immense data, but smaller companies are catching up through affordable, cloud-based options that streamline customer support and research.

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In 2024, about 45% of U.S. adults used AI-powered search engines at least once a month, showing that mainstream adoption has already begun. Perplexity.ai emerged as a strong example, growing from 10 million monthly active users in mid-2023 to over 30 million by Q1 2025, reflecting the pace of adoption. Globally, around 27% of enterprises integrated AI search tools into their internal systems, while 51% of Gen Z turned to AI platforms for academic queries. In India, more than 60% of AI search traffic came from mobile, and 38% of users trusted AI search results over traditional engines.

The marketing landscape has also been reshaped by AI-driven search adoption. According to Gitnux, 61% of marketers saw increases in organic traffic due to AI, while 72% considered it central to future strategies. Nearly 95% of customer interactions are expected to be managed without humans by 2025, and by 2024 about 80% of search queries were projected to be handled by AI-powered agents. This signals a structural shift in how companies approach engagement, customer support, and visibility online.

Among smaller businesses, adoption momentum is accelerating, with 55% planning to expand AI investment in search marketing. AI-powered personalization is playing a key role in this trend, helping improve customer targeting and increasing conversion rates by up to 20%. Together, these figures illustrate how AI-driven search is not only transforming consumer behavior but also redefining enterprise strategies, marking a rapid global shift toward automated and intelligent engagement.

MMSearch 🔥: Benchmarking the Potential of Large Models as Multi-modal Search Engines

Official repository for the paper "MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines". 🌟 For more details, please refer to the project page with dataset exploration and visualization tools: https://mmsearch.github.io/. [🌐 Webpage] [📖 Paper] [🤗 Huggingface Dataset] [🏆 Leaderboard] [🔍 Visualization]

  💥 News

[2024.09.25] 🌟 The evaluation code now… See the full description on the dataset page: https://huggingface.co/datasets/CaraJ/MMSearch.

The confluence of Search and Recommendation (S&R) services is a vital aspect of online content platforms like Kuaishou and TikTok. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within the academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in this field. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 300 million daily active users. Previous research in this field has predominantly employed publicly available datasets that are semi-synthetic and simulated, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR records genuine user behaviors, the occurrence of each interaction within either search or recommendation service, and the users’ transitions between the two services. This work aids in joint modeling of S&R, and the utilization of search data for recommenders (and recommendation data for search engines). Additionally, due to the diverse feedback labels of user-video interactions, KuaiSAR also supports a wide range of other tasks, including intent recommendation, multi-task learning, and long sequential multi-behavior modeling etc. We believe this dataset will facilitate innovative research and enrich our understanding of S&R services integration in real-world applications.

AI SEO Tools Market Outlook

The global AI SEO tools market size was estimated at $1.5 billion in 2023 and is projected to reach $5.2 billion by 2032, growing at a CAGR of 14.8%. The substantial growth factor for this market is the increasing adoption of AI technologies in digital marketing and SEO practices, which is driven by the need for businesses to enhance their online presence and streamline their marketing strategies. The integration of AI in SEO tools helps in automating tedious tasks, improving accuracy, and providing deeper insights, thus driving market growth.

One of the primary growth factors contributing to the AI SEO tools market is the exponential increase in internet penetration and the subsequent growth in online content. As more businesses move online, the competition to appear on the first page of search engine results intensifies. AI-powered SEO tools assist businesses in optimizing their websites more effectively by analyzing vast amounts of data, understanding search engine algorithms, and predicting future trends. Moreover, the ability of AI to process natural language and understand user intent helps in creating more relevant content, further boosting the market.

Another significant growth factor is the rising demand for personalized user experiences. AI SEO tools enable businesses to tailor their content and marketing strategies based on user behavior and preferences. By leveraging machine learning and data analytics, these tools can provide insights into what users are searching for, their engagement patterns, and their purchasing behavior. This enables businesses to optimize their content and improve user engagement, leading to higher conversion rates. Additionally, the integration of voice search optimization, which is becoming increasingly popular, further drives the adoption of AI SEO tools.

The increasing focus on data-driven decision-making also contributes to the growth of the AI SEO tools market. With the vast amount of data generated daily, businesses require advanced tools to analyze and extract meaningful insights. AI SEO tools help businesses make informed decisions by providing real-time data analysis, identifying trends, and predicting future outcomes. This not only enhances the efficiency of SEO strategies but also enables businesses to stay ahead of the competition. Furthermore, the cost-effectiveness of AI SEO tools compared to traditional methods makes them an attractive option for businesses of all sizes.

From a regional perspective, North America dominates the AI SEO tools market due to the presence of major technology companies and early adoption of advanced technologies in this region. The high level of internet penetration, along with the significant investments in AI and machine learning, further propels the market. Europe follows closely, with a growing emphasis on digital transformation and data privacy regulations that compel businesses to adopt advanced SEO tools. The Asia Pacific region is expected to witness the highest growth rate, driven by the rapid digitalization, increasing internet users, and the rising number of small and medium enterprises (SMEs) adopting AI technologies.

Search Engine Optimization Services have become a cornerstone for businesses aiming to enhance their digital footprint. With the increasing complexity of search engine algorithms, businesses are turning to specialized services to navigate these changes effectively. These services offer a comprehensive approach, combining technical expertise with strategic insights to optimize a website's performance in search results. By leveraging these services, businesses can ensure that their websites are not only visible but also relevant to their target audience. This relevance is crucial in attracting and retaining customers in a competitive online marketplace. Furthermore, SEO services provide ongoing support and updates, helping businesses adapt to the ever-evolving digital landscape.

Software Analysis

The software segment is the backbone of the AI SEO tools market, comprising various tools and platforms that assist in different aspects of SEO. These software solutions use machine learning algorithms to analyze large datasets, predict search engine ranking factors, and automate repetitive tasks. The rapid advancements in AI technology have led to the development of mo

United States agricultural researchers have many options for making their data available online. This dataset aggregates the primary sources of ag-related data and determines where researchers are likely to deposit their agricultural data. These data serve as both a current landscape analysis and also as a baseline for future studies of ag research data. Purpose As sources of agricultural data become more numerous and disparate, and collaboration and open data become more expected if not required, this research provides a landscape inventory of online sources of open agricultural data. An inventory of current agricultural data sharing options will help assess how the Ag Data Commons, a platform for USDA-funded data cataloging and publication, can best support data-intensive and multi-disciplinary research. It will also help agricultural librarians assist their researchers in data management and publication. The goals of this study were to establish where agricultural researchers in the United States-- land grant and USDA researchers, primarily ARS, NRCS, USFS and other agencies -- currently publish their data, including general research data repositories, domain-specific databases, and the top journals compare how much data is in institutional vs. domain-specific vs. federal platforms determine which repositories are recommended by top journals that require or recommend the publication of supporting data ascertain where researchers not affiliated with funding or initiatives possessing a designated open data repository can publish data Approach The National Agricultural Library team focused on Agricultural Research Service (ARS), Natural Resources Conservation Service (NRCS), and United States Forest Service (USFS) style research data, rather than ag economics, statistics, and social sciences data. To find domain-specific, general, institutional, and federal agency repositories and databases that are open to US research submissions and have some amount of ag data, resources including re3data, libguides, and ARS lists were analysed. Primarily environmental or public health databases were not included, but places where ag grantees would publish data were considered. Search methods We first compiled a list of known domain specific USDA / ARS datasets / databases that are represented in the Ag Data Commons, including ARS Image Gallery, ARS Nutrition Databases (sub-components), SoyBase, PeanutBase, National Fungus Collection, i5K Workspace @ NAL, and GRIN. We then searched using search engines such as Bing and Google for non-USDA / federal ag databases, using Boolean variations of “agricultural data” /“ag data” / “scientific data” + NOT + USDA (to filter out the federal / USDA results). Most of these results were domain specific, though some contained a mix of data subjects. We then used search engines such as Bing and Google to find top agricultural university repositories using variations of “agriculture”, “ag data” and “university” to find schools with agriculture programs. Using that list of universities, we searched each university web site to see if their institution had a repository for their unique, independent research data if not apparent in the initial web browser search. We found both ag specific university repositories and general university repositories that housed a portion of agricultural data. Ag specific university repositories are included in the list of domain-specific repositories. Results included Columbia University – International Research Institute for Climate and Society, UC Davis – Cover Crops Database, etc. If a general university repository existed, we determined whether that repository could filter to include only data results after our chosen ag search terms were applied. General university databases that contain ag data included Colorado State University Digital Collections, University of Michigan ICPSR (Inter-university Consortium for Political and Social Research), and University of Minnesota DRUM (Digital Repository of the University of Minnesota). We then split out NCBI (National Center for Biotechnology Information) repositories. Next we searched the internet for open general data repositories using a variety of search engines, and repositories containing a mix of data, journals, books, and other types of records were tested to determine whether that repository could filter for data results after search terms were applied. General subject data repositories include Figshare, Open Science Framework, PANGEA, Protein Data Bank, and Zenodo. Finally, we compared scholarly journal suggestions for data repositories against our list to fill in any missing repositories that might contain agricultural data. Extensive lists of journals were compiled, in which USDA published in 2012 and 2016, combining search results in ARIS, Scopus, and the Forest Service's TreeSearch, plus the USDA web sites Economic Research Service (ERS), National Agricultural Statistics Service (NASS), Natural Resources and Conservation Service (NRCS), Food and Nutrition Service (FNS), Rural Development (RD), and Agricultural Marketing Service (AMS). The top 50 journals' author instructions were consulted to see if they (a) ask or require submitters to provide supplemental data, or (b) require submitters to submit data to open repositories. Data are provided for Journals based on a 2012 and 2016 study of where USDA employees publish their research studies, ranked by number of articles, including 2015/2016 Impact Factor, Author guidelines, Supplemental Data?, Supplemental Data reviewed?, Open Data (Supplemental or in Repository) Required? and Recommended data repositories, as provided in the online author guidelines for each the top 50 journals. Evaluation We ran a series of searches on all resulting general subject databases with the designated search terms. From the results, we noted the total number of datasets in the repository, type of resource searched (datasets, data, images, components, etc.), percentage of the total database that each term comprised, any dataset with a search term that comprised at least 1% and 5% of the total collection, and any search term that returned greater than 100 and greater than 500 results. We compared domain-specific databases and repositories based on parent organization, type of institution, and whether data submissions were dependent on conditions such as funding or affiliation of some kind. Results A summary of the major findings from our data review: Over half of the top 50 ag-related journals from our profile require or encourage open data for their published authors. There are few general repositories that are both large AND contain a significant portion of ag data in their collection. GBIF (Global Biodiversity Information Facility), ICPSR, and ORNL DAAC were among those that had over 500 datasets returned with at least one ag search term and had that result comprise at least 5% of the total collection. Not even one quarter of the domain-specific repositories and datasets reviewed allow open submission by any researcher regardless of funding or affiliation. See included README file for descriptions of each individual data file in this dataset. Resources in this dataset:Resource Title: Journals. File Name: Journals.csvResource Title: Journals - Recommended repositories. File Name: Repos_from_journals.csvResource Title: TDWG presentation. File Name: TDWG_Presentation.pptxResource Title: Domain Specific ag data sources. File Name: domain_specific_ag_databases.csvResource Title: Data Dictionary for Ag Data Repository Inventory. File Name: Ag_Data_Repo_DD.csvResource Title: General repositories containing ag data. File Name: general_repos_1.csvResource Title: README and file inventory. File Name: README_InventoryPublicDBandREepAgData.txt

The enterprise search engine market is poised for substantial growth, projected to reach an impressive $186,500 million by 2025, exhibiting a robust Compound Annual Growth Rate (CAGR) of 11% throughout the forecast period (2025-2033). This expansion is significantly fueled by the escalating need for efficient information retrieval and knowledge management across organizations. Large enterprises, in particular, are driving demand due to the sheer volume of data they manage and the critical importance of swift access to this information for decision-making, operational efficiency, and competitive advantage. The increasing adoption of cloud-based solutions and the burgeoning integration of artificial intelligence (AI) and machine learning (ML) into search functionalities are also key enablers. These technologies are transforming traditional search engines into intelligent platforms capable of understanding context, providing personalized results, and proactively surfacing relevant information, thereby enhancing productivity and user experience. The market landscape is further shaped by the evolving nature of enterprise data, which is increasingly unstructured and resides across diverse platforms. This necessitates sophisticated search solutions that can index and analyze a wide array of content formats, from documents and emails to internal databases and social media feeds. Consequently, meta search engines, which aggregate results from multiple sources, are gaining traction, alongside advanced crawler search engines that offer deeper insights and broader coverage. While the market's growth trajectory is strong, it is important to acknowledge potential restraints such as data security and privacy concerns, the cost of implementing and maintaining advanced search solutions, and the need for skilled personnel to manage these complex systems. Nevertheless, the overarching trend points towards a future where enterprise search engines are indispensable tools for unlocking the full potential of an organization's knowledge base. This comprehensive report offers an in-depth analysis of the global Enterprise Search Engine market, spanning the Study Period: 2019-2033, with a Base Year: 2025 and an Estimated Year: 2025. The Forecast Period: 2025-2033 will witness significant evolution, building upon the Historical Period: 2019-2024. The market is projected to reach billions in value by 2025, with projections indicating a substantial growth trajectory throughout the forecast period.

BackgroundAIDS is a worrying public health issue in China and lacks timely and effective surveillance. With the diffusion and adoption of the Internet, the ‘big data’ aggregated from Internet search engines, which contain users’ information on the concern or reality of their health status, provide a new opportunity for AIDS surveillance. This paper uses search engine data to monitor and forecast AIDS in China.MethodsA machine learning method, artificial neural networks (ANNs), is used to forecast AIDS incidences and deaths. Search trend data related to AIDS from the largest Chinese search engine, Baidu.com, are collected and selected as the input variables of ANNs, and officially reported actual AIDS incidences and deaths are used as the output variable. Three criteria, the mean absolute percentage error, the root mean squared percentage error, and the index of agreement, are used to test the forecasting performance of the ANN method.ResultsBased on the monthly time series data from January 2011 to June 2017, this article finds that, under the three criteria, the ANN method can lead to satisfactory forecasting of AIDS incidences and deaths, regardless of the change in the number of search queries.ConclusionsDespite the inability to self-detect HIV/AIDS through online searching, Internet-based data should be adopted as a timely, cost-effective complement to a traditional AIDS surveillance system.

This dataset gathers the most crucial SEO statistics for the year, providing an overview of the dominant trends and best practices in the field of search engine optimization. Aimed at digital marketing professionals, site owners, and SEO analysts, this collection of information serves as a guide to navigate the evolving SEO landscape with confidence and accuracy. Mode of Data Production: The statistics have been carefully selected and compiled from a variety of credible and recognized sources in the SEO industry, including research reports, web traffic data analytics, and consumer and marketing professional surveys. Each statistic was checked for reliability and relevance to current trends. Categories Included: User search behaviour: Statistics on the evolution of search modes, including voice and mobile search. Mobile Optimisation: Data on the importance of site optimization for mobile devices. Importance of Backlinks: Insights on the role of backlinks in SEO ranking and the need to prioritize quality. Content quality: Statistics highlighting the importance of relevant and engaging content for SEO. Search engine algorithms: Information on the impact of algorithm updates on SEO strategies. Usefulness of the Data: This dataset is designed to help users quickly understand current SEO dynamics and apply that knowledge in optimizing their digital marketing strategies. It provides a solid foundation for benchmarking, strategic planning, and informed decision-making in the field of SEO. Update and Accessibility: To ensure relevance and timeliness, the dataset will be regularly updated with new information and emerging trends in the SEO world.