These datasets include ratings as well as social (or trust) relationships between users. Data are from LibraryThing (a book review website) and epinions (general consumer reviews).

Metadata includes

• reviews

• price paid (epinions)

• helpfulness votes (librarything)

• flags (librarything)

These datasets contain attributes about products sold on ModCloth and Amazon which may be sources of bias in recommendations (in particular, attributes about how the products are marketed). Data also includes user/item interactions for recommendation.

Metadata includes

• ratings

• product images

• user identities

• item sizes, user genders

Drug recomendation dataset based on disease

These datasets contain reviews from the Goodreads book review website, and a variety of attributes describing the items. Critically, these datasets have multiple levels of user interaction, raging from adding to a shelf, rating, and reading.

Metadata includes

• reviews

• add-to-shelf, read, review actions

• book attributes: title, isbn

• graph of similar books

Basic Statistics:

• Items: 1,561,465

• Users: 808,749

• Interactions: 225,394,930

Dataset

This dataset was created by Gaurav B.V

Contents

In the realm of global agriculture, the imperative of sustaining an ever-expanding population is met with challenges in optimizing crop production and judicious resource management. SmartzAgri heralds a groundbreaking approach to modern agriculture. This innovative system represents a convergence of machine learning algorithms and Internet of Things (IoT) technology, aimed at reshaping traditional paradigms of crop recommendation. At the core of SmartzAgri lies a meticulous process: IoT devices intricately designed collect soil data, focusing on key parameters like Nitrogen (N), Phosphorus (P), Potassium (K), pH levels, moisture, and temperature. This real-time data is collected using different sensors and seamlessly transmitted to a dedicated web platform fortified by cutting-edge machine learning algorithms including Random Forest, XG-Boost, Naive Bayes, and Support Vector Machine (SVM). This ensemble of algorithms facilitates an intelligent analysis, enabling the system to predict with precision the most suitable crops for a given soil composition. In essence, SmartzAgri emerges as a sophisticated solution, marrying data-driven insights and real-time analysis to offer farmers nuanced recommendations for crop selection. This holistic approach holds the promise of enhancing precision in crop management, ultimately contributing to the elevation of agricultural productivity in a technologically advanced and informed manner.

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Recommendation Engine Market Size 2024-2028

The recommendation engine market size is projected to increase by USD 1.66 billion, at a CAGR of 39.91% between 2023 and 2028.

The market growth is influenced by various factors, particularly the rise of digitalization across industries, which is catalyzing shifts in consumer behavior and operational paradigms. The proliferation of hybrid recommendation systems, which blend human expertise with machine algorithms, enhances personalized user experiences, thereby improving engagement and retention metrics. Additionally, a heightened focus on customer satisfaction has become a strategic imperative, fostering brand loyalty and advocacy in an increasingly competitive landscape. These interconnected dynamics highlight the transformative potential of technological innovation and customer-centric strategies, which are shaping market trajectories and introducing new paradigms in user engagement and service delivery.

Moreover, the market growth and forecasting report includes key player's detailed analyses of the competitive landscape of the market and information about 20 market companies, including Alphabet Inc., Coveo Solutions Inc., Curata Inc., Hewlett Packard Enterprise Co., Intel Corp., Kibo Software Inc., Mastercard Inc., Microsoft Corp., Muvi LLC, Nosto Solutions Oy, Oracle Corp., Outbrain Inc., Piano Software Inc., Recombee, Salesforce Inc., SAP SE, International Business Machines Corp., and Adobe Inc. . Additionally, Amazon.com Inc. company offers recommendation engine solutions through Vertex AI Search that recommend media content based on watch history and recommend products based on models users like.

What will be the Size of the Market During the Forecast Period?

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Market Dynamics

The market is thriving with advancements in collaborative filtering techniques, catering to diverse sectors like retail, information technology, and SMEs. Leveraging artificial intelligence and machine learning, these engines offer tailored product recommendations, enhancing customer delivery and proactive asset management. With the rise of smart technologies and internet penetration, brick-and-mortar retailers and OTT service providers are adopting automation solutions and self-service tools. Powered by AI-based cloud platforms, recommendation engines play a vital role in product planning and driving sales in various segments, while addressing challenges like incorrect labeling. Our researchers analyzed the market research and growth data with 2023 as the base year, along with the key market growth analysis, trends, and challenges. A holistic analysis of drivers, trends, and challenges will help companies refine their marketing strategies to gain a competitive advantage.

Key Market Driver

The increase in the use of hybrid systems is the key factor driving the market. A recommendation engine is a filtering system used by companies to generate and provide appropriate recommendations to users about a product or content. Recommendation filtering systems, which companies use to enhance user experience and customer base, can be classified into three categories such as the collaborative filtering, content-based filtering, and hybrid filtering

Additionally, hybrid filtering incorporates both recommendation systems, collaborative filtering, and content-based filtering. Thus, it increases the chances of providing accurate recommendations. It enables users' flexibility to create a set of recommendations using different technologies, such as AI and ML. Netflix and Amazon are among the major companies utilizing the hybrid recommendation system to provide an enhanced user experience. Furthermore, the integration of hybrid filtering techniques is increasingly relevant in higher education testing and assessment. These techniques enhance the accuracy and personalization of educational recommendations and assessments, leveraging AI and ML to adapt to individual learning needs. As educational institutions adopt advanced technologies to improve learning outcomes, the application of hybrid filtering in higher education testing and assessment is poised to drive market growth during the forecast period.

Significant Market Trends

The implementation of AI is the primary trend shaping the market. The recommendation engine is becoming an important tool among end-users, primarily online retailers and online content providers. This can be substantiated by the fact that more than 35% of Amazon. Com's revenue is generated by its recommendation engine. To provide more advanced search engine capabilities to enhance the user experience, major market players are integrating advanced technologies, such as AI and ML, in recommendation engines.

Moreover, AI technology enables recommendation engines to understand customer behavior and provide relevant content in real

Dataset

This dataset was created by Raihan Sikdar

Contents

Report Overview

The Global AI-Based Recommendation System Market size is expected to be worth around USD 34.4 Billion by 2033, from USD 2.8 Billion in 2023, growing at a CAGR of 28.5% during the forecast period from 2024 to 2033.

The AI-Based Recommendation System Market refers to the industry segment focused on developing and deploying systems that use artificial intelligence to provide personalized suggestions to users. These systems analyze large datasets, including user behavior, preferences, and past interactions, to recommend products, services, or content.

This market is driven by the growing need for personalized customer experiences. Businesses are increasingly adopting AI recommendation systems to enhance customer satisfaction, boost sales, and improve engagement. The rise of big data and advancements in machine learning developments also propel this market.

https://market.us/wp-content/uploads/2024/09/AI-Based-Recommendation-System-Market-By-Size.jpg" alt="AI-Based Recommendation System Market By Size" width="785" height="465">

The AI-based recommendation system market is experiencing significant growth, driven by the increasing demand for personalized customer experiences across various industries. In the e-commerce sector, AI-powered recommendations are particularly impactful.

Companies like Amazon attribute up to 35% of their sales to these systems, which analyze user behavior, purchase history, and browsing patterns to suggest relevant products. This not only enhances the likelihood of conversion but also increases average order values by 20-30% through effective cross-selling and upselling strategies.

Consumer engagement is heavily influenced by personalization, with 91% of consumers more likely to interact with brands that offer tailored recommendations. This trend is not confined to e-commerce platforms; it is reflected across industries, with 60% of customers expressing a willingness to use AI-driven tools for enhanced shopping experiences.

The effectiveness of these systems in creating a personalized experience is also evident in their impact on customer acquisition and retention. AI-driven personalization can reduce customer acquisition costs by up to 50%, while also improving marketing efficiency by 10-30%. These benefits make AI-based recommendation systems an essential tool for businesses aiming to stay competitive in today’s market.

Government initiatives are also leveraging AI recommendation systems to improve public service delivery. For instance, Brazil’s gov.br portal uses AI to recommend public services based on a citizen’s browsing history and previous interactions.

This system has proven highly effective, with 25% of service requests on the portal now originating from AI-driven recommendations. The portal has been viewed over 51 million times, demonstrating its success in enhancing service accessibility and efficiency.

As AI technology continues to advance, the market for AI-based recommendation systems is expected to grow further. Businesses that adopt these systems can expect to see significant improvements in customer engagement, sales conversion rates, and operational efficiency.

The CDEI has been tasked with researching the ways in which algorithmically driven recommendation systems have impacted music consumption, including how creators are being affected (see Recommendation 18 in the government’s response to the economics of music streaming Committee’s Second Report). The CDEI will be carrying out a survey to take the views of creators into consideration as part of our research, as well as begin to understand if and how algorithmically driven recommendation systems affect different categories of creators, creators across different genres, and whether there are any apparent differences in their effect by region, age, gender identity, or ethnic group. This privacy notice explains who the CDEI are, the personal data the CDEI collects, how the CDEI uses it, who the CDEI shares it with, and what your legal rights are.

The global recommendation engine market size has the potential to grow by USD 3.57 billion during 2020-2024, and the market’s growth momentum will accelerate during the forecast period.

This report provides a detailed analysis of the market by geography (APAC, Europe, MEA, North America, and South America) and end-user (media and entertainment, retail, travel and tourism, and others). Also, the report analyzes the market’s competitive landscape and offers information on several market vendors, including Adobe Inc., Amazon Web Services Inc., Dynamic Yield Inc., Evergage Inc., Google LLC, International Business Machines Corp., Kibo Software Inc., Qubit Digital Ltd., Salesforce.com Inc., and SAP SE.

Market Overview

Market Competitive Analysis

The market is fragmented, and the degree of fragmentation will remain the same during the forecast period. The key players in the market are focusing on various growth strategies, including M&A and strategic partnerships, to expand the geographical reach and gain significant market shares and revenue. Qubit Digital Ltd., Salesforce.com Inc., and SAP SE. are some of the major market participants. Although the accelerating growth momentum will offer immense growth opportunities, issues related to accuracy in data prediction will challenge the growth of the market participants. To make the most of the possibilities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

To help clients improve their market position, this recommendation engine market forecast report provides a detailed analysis of the market leaders and offers information on the competencies and capacities of these companies. The report also covers details on the market’s competitive landscape and provides information on the products offered by various companies. Moreover, this report also includes information on the upcoming, recommendation engine market trends and challenges that will influence market growth. This will help companies create strategies to make the most of future growth opportunities.

This recommendation engine market analysis report provides information on the production, sustainability, and prospects of several leading recommendation engine companies, including:

Adobe Inc.
Amazon Web Services, Inc.
Dynamic Yield, Inc.
Evergage Inc.
Google LLC
International Business Machines Corp.
Kibo Software Inc.
Qubit Digital Ltd.
Salesforce.com Inc.
SAP SE

Recommendation Engine Market: Segmentation by Region

North America was the largest recommendation engine market in 2019, and the region will continue to offer the maximum growth opportunities to market vendors during the forecast period. The increasing adoption of OTT services, including both video-on-demand and audio-on-demand and the presence of significant e-commerce websites in the region, will significantly influence the growth of recommendation engine market size.

Over 38% of the market’s growth will originate from North America during the forecast period. The US and Canada are the key markets for recommendation engines in North America. Market growth in this region will be slower than the growth of the market in APAC and South America.

Recommendation Engine Market: Segmentation by End-user

Recommendation engines are highly preferred in the media and entertainment segment as they provide accurate and relevant recommendations about music and video to users. Moreover, several companies are also integrating advanced technologies, such as AI and ML, to enhance the capabilities of recommendation engines.

Market growth in this segment will be slower than the growth of the market in the retail segment. This report provides an accurate prediction of the contribution of all the segments to the growth of the recommendation engine market size.

Recommendation Engine Market: Key Drivers and Trends

Recommendation filtering systems are used by companies to enhance the user experience, and customer base and are also classified into collaborative, content-based, and hybrid filtering. Moreover, as hybrid filtering incorporates both recommendation systems, it increases the chances of providing accurate recommendations. Furthermore, it helps users create a set of recommendations using technologies, such as AI and ML. Netflix and Amazon are among the significant companies utilizing the hybrid recommendation system to provide an enhanced user experience. Netflix uses these systems to provide recommendations related to movies and TV shows, as well as similar high rated content to its subscribers. The rising use of hybrid recommendation systems will significantly influence the global recommendation engine market growth.

The recommendation engine is an essential tool among end-users, online retailers and online content providers.
Major vendors are integrating technologies,

Dataset

This dataset was created by Vyshnavi Katta

Contents

This data set deals with Maintenance Action Recommendations

This dataset compiles the results of a systematic literature review on user intent modeling in Natural Language Processing (NLP), with a focus on its application in conversational recommender systems. Over 13,000 papers from the past decade have been analyzed to provide a thorough understanding of the prevalent AI models used in this area. The dataset includes detailed examinations of various machine learning models such as SVM, LDA, Naive Bayes, BERT, Word2vec, and MLP, highlighting their advantages, limitations, and suitability for different scenarios in recommender systems.

Additionally, the dataset encompasses a wide range of applications of user intent modeling across sectors such as e-commerce, healthcare, education, social media, and virtual assistants. It sheds light on how these models aid in delivering personalized recommendations, detecting fake reviews, providing health interventions, tailoring educational content, and enhancing user experience on social media.

A key component of the dataset is a decision model, derived from the literature review, designed to assist researchers and developers in selecting the most appropriate machine learning model for specific user intent modeling tasks in recommender systems. This model addresses the challenge posed by the variety of available models and the lack of a clear classification scheme.

Furthermore, the dataset includes the outcomes of two academic case studies conducted to assess the utility of the decision model. These case studies follow Yin's guidelines and provide practical insights into the application of the decision model in real-world scenarios.

Researchers, developers, and practitioners in the field of NLP, AI, and recommender systems will find this dataset invaluable for navigating the complex landscape of user intent modeling. It not only synthesizes scattered research but also provides a practical tool for model selection, thereby contributing significantly to the advancement of personalized user experiences in various domains.

Keywords: User Intent Modeling, NLP, Conversational Recommender Systems, Machine Learning, Systematic Literature Review, Decision Model

The global recommendation engine market size reached US$ 4.8 Billion in 2023. Looking forward, IMARC Group expects the market to reach US$ 59.1 Billion by 2032, exhibiting a growth rate (CAGR) of 31.2% during 2024-2032.

IMARC Group provides an analysis of the key trends in each sub-segment of the global recommendation engine market report, along with forecasts at the global, regional and country level from 2024-2032. Our report has categorized the market based on type, technology, deployment mode, application and end user.

This dataset contains ratings on news about the Brazilian 2018 presidential elections. This news was recommended by different recommender algorithms including diversification strategies. So, we registered the algorithms responsible for each recommendation and this information can be used by other researchers who are focusing on diversification of recommendations, especially in the news domain. Eeah news item contains the raw text that can be used for text mining techniques in order to discover features for diversification.

In 2023, over half of consumers used tools based on generative AI for product or service recommendations worldwide. Millennials were the most familiar with this type of technology, as 56 percent of them replaced traditional search engines with gen-AI tools.

Get the sample copy of Recommendation Engine Market Report 2024 (Global Edition) which includes data such as Market Size, Share, Growth, CAGR, Forecast, Revenue, list of Recommendation Engine Companies (IBM, Google, AWS, Microsoft, Salesforce, Sentient Technologies, HPE, Oracle, Intel, SAP), Market Segmented by Type (Collaborative filtering, Content based filtering, Hybrid recommendation), by Application (Manufacturing, Healthcare, BFSI, Media and entertainment, Transportation, Others)

Dataset

This dataset was created by Noor Saeed

Contents

Green food is well-known for its health benefits, environmental-friendliness, and safety. Current recommender systems used by e-commerce websites usually recommend products based on products' popularity or customers' ratings. However, users' reviews could be more representative of consumers' preferences. In addition, users' review time is not utilized. To reduce the recommendation bias, this study proposes a hybrid recommendation algorithm based on green food reviews and review time. The proposed algorithm combines a content-based recommendation algorithm with a user-based collaborative filtering approach, where affective values of reviews replace ratings and a time impact factor is considered. With the two classical evaluation indices of F1 and Mean Absolute Error (MAE), the experiments show that considering both reviews’ sentiments and dynamic changes of individuals’ preferences could improve recommendation effectiveness over three other algorithms, which provides a new reference direction for improving existing recommender systems on green food.