In this study, we built a personalized hybrid course recommendation system (PHCRS) that considers students’ interests, abilities and career development. To meet students’ individual needs, we adopted the five most widely used algorithms, including content-based filtering, popularity-based methods, item-based collaborative filtering, user-based collaborative filtering, and score-based methods, to build a PHCRS. First, we collected course syllabi and labeled each course (e.g., knowledge/skills taught, basic/advanced level). Next, we used course labels and students’ past course selections and grades to train five recommendation models. To evaluate the accuracy of the system, we performed experiments with students in the Department of Electrical and Computer Engineering, which provides 1794 courses for 925 students and utilizes the receiver operating characteristic curve (ROC) and normalized discounted cumulative gain (NDCG) as metrics. The results showed that our proposed system can achieve accuracies of 80% for ROC and 90% for NDCG. We invited 46 participants to test our system and complete a questionnaire. Overall, 60 to 70% of participants were interested in the recommended courses, while the course recommendation lists produced by content-based filtering were in line with 67.40% of students’ actual course preferences. This study also found that students were more interested in courses at the top of the recommendation lists, and more students were autonomously motivated than held extrinsic informational motivation across the five recommendation methods. These findings highlighted that the proposed course recommendation system can help students choose the courses that interest them most.

Wyze Rule Recommendation Dataset

  Dataset Summary

The Wyze Rule dataset is a new large-scale dataset designed specifically for smart home rule recommendation research. It contains over 1 million rules generated by 300,000 users from Wyze Labs, offering an extensive collection of real-world automation rules tailored to users' unique smart home setups. The goal of the Wyze Rule dataset is to advance research and development of personalized rule recommendation… See the full description on the dataset page: https://huggingface.co/datasets/wyzelabs/RuleRecommendation.

Dataset

This dataset was created by Tanuj dhiman

Contents

The Recommendation Engine market is projected to grow significantly from XXX million in 2023 to XXX million by 2033, at a CAGR of XX% during the forecast period. The increasing adoption of e-commerce and the growing popularity of personalized content are driving the demand for recommendation engines. Additionally, the growing use of artificial intelligence (AI) and machine learning (ML) is enabling the development of more sophisticated and accurate recommendation engines. Key players in the market include IBM, Google, AWS, Microsoft, and Salesforce. The market is segmented by application, type, and region. By application, the market is divided into e-commerce, media and entertainment, and others. By type, the market is divided into personalized content, cross-selling, and up-selling. By region, the market is divided into North America, South America, Europe, Middle East and Africa, and Asia Pacific. North America is expected to dominate the market throughout the forecast period, followed by Europe. The Asia Pacific region is expected to grow at the highest CAGR during the forecast period. The major drivers of the market growth in the Asia Pacific region are the increasing adoption of e-commerce and the growing popularity of personalized content.

Context

This Dataset is an updated version of the Amazon review dataset released in 2014. As in the previous version, this dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs). In addition, this version provides the following features:

• More reviews:

  • The total number of reviews is 233.1 million (142.8 million in 2014).

• New reviews:

  • Current data includes reviews in the range May 1996 - Oct 2018.

• Metadata: - We have added transaction metadata for each review shown on the review page.

  • Added more detailed metadata of the product landing page.

Acknowledgements

If you publish articles based on this dataset, please cite the following paper:

• Jianmo Ni, Jiacheng Li, Julian McAuley. Justifying recommendations using distantly-labeled reviews and fined-grained aspects. EMNLP, 2019.

Journal recommendations prepared on results from JANE and whichjournal.com based on 4 abstracts from the disciplines dentistry, psychology and aerosol chemistry.

The factsheets with data for each journal should help to decide for the best journal.

The data is provided as spreadsheet (xls) and factsheet (pdf).

Global Machine Learning Recommendation Algorithm Market Report 2023 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2023-2029. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

This dataset is crop recommendation dataset. This dataset is crop recommendation dataset. This dataset is crop recommendation dataset. This dataset is crop recommendation dataset. This dataset is crop recommendation dataset. Year The calendar year in which the data was recorded. Country The name of the country where the data was collected. Crop The type of crop (e.g., wheat, maize, rice) being measured. Area_Harvested The total area harvested for the crop, measured in hectares. Production The total production output of the crop, measured in metric tons. Yield The crop yield calculated as production per unit area (e.g., tons per hectare). Irrigation Indicates whether the crop was irrigated (Yes) or rainfed (No). Fertilizer_Used The amount of fertilizer applied, measured in kilograms per hectare. Pesticide_Used The amount of pesticides applied, measured in kilograms per hectare. Temperature The average temperature during the growing season, measured in degrees Celsius. Rainfall The total rainfall during the growing season, measured in millimeters.

Analysis of ‘Crop Recommendation Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/siddharthss/crop-recommendation-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

THE INFORMATION IN THE DATASET IS PROVIDED TO THE BEST OF KNOWLEDGE OF ICAR. THE BELOW DATA CAN BE USED PUBLICALLY UNDER ALL PUBLIC AND PRIVATE UNDERTAKINGS

Context Precision agriculture is in trend nowadays. It helps the farmers to get informed decision about the farming strategy. Here, we present you a dataset which would allow the users to build a predictive model to recommend the most suitable crops to grow in a particular farm based on various parameters.**

Source This dataset was build by augmenting datasets of rainfall, climate and fertilizer data available for India. Gathered over the period by ICFA, India.

Data fields N - ratio of Nitrogen content in soil P - ratio of Phosphorous content in soil K - ratio of Potassium content in soil temperature - temperature in degree Celsius humidity - relative humidity in % ph - ph value of the soil rainfall - rainfall in mm

COPYRIGHT: Indian Chamber of Food and Agriculture https://www.icfa.org.in/ https://www.google.com/url?sa=i&url=https%3A%2F%2F10times.com%2Fcompany%2Findian-council-of-food-and-agriculture&psig=AOvVaw0S9UpuXsVmmje0SgSjybK5&ust=1622035121203000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCNie_uv15PACFQAAAAAdAAAAABAn" alt="">

--- Original source retains full ownership of the source dataset ---

the ideal cycle is 14-18).

Acknowledgments: This project has received funding from the European Union's Horizon 2020 Research and Innovation program under the Marie Sk\l{}dowska-Curie ENCASE project (GA No. 691025) and the CONCORDIA project (GA No. 830927), the US National Science Foundation (grants: 1942610, 2114407, 2114411, and 2046590), and the UK's National Research Centre on Privacy, Harm Reduction, and Adversarial Influence Online (UKRI grant: EP/V011189/1). This work reflects only the authors' views; the Agency and the Commission are not responsible for any use that may be made of the information it contains.

Multi-Cloud FinOps Recommendation Engine Market Outlook

According to our latest research, the global Multi-Cloud FinOps Recommendation Engine market size in 2024 stands at USD 1.94 billion, reflecting the rapid adoption of cloud cost optimization technologies worldwide. The market is experiencing robust momentum, driven by the increasing complexity of multi-cloud environments and the growing need for financial accountability in cloud spending. The market is projected to reach USD 8.73 billion by 2033, expanding at a remarkable CAGR of 18.2% during the forecast period. The primary growth factor fueling this expansion is the escalating demand for intelligent solutions that automate and optimize cloud financial operations, enabling enterprises to maximize value from their multi-cloud investments.

The growth trajectory of the Multi-Cloud FinOps Recommendation Engine market is underpinned by the exponential rise in cloud adoption across various industry verticals. As organizations increasingly leverage multiple cloud service providers to enhance agility, scalability, and innovation, managing cloud expenditure has become a critical business imperative. Traditional cost management tools are often inadequate for the dynamic and distributed nature of multi-cloud environments. This has led to the emergence of advanced FinOps recommendation engines that harness artificial intelligence and machine learning to deliver actionable insights, automate resource allocation, and ensure continuous cost optimization. The integration of these engines with existing cloud management platforms further accelerates their adoption, empowering enterprises to achieve financial transparency and operational efficiency.

Another significant growth driver for the Multi-Cloud FinOps Recommendation Engine market is the increasing regulatory scrutiny and compliance requirements in sectors such as BFSI, healthcare, and retail. With cloud environments becoming more complex, organizations face mounting challenges in ensuring compliance with data protection regulations and industry standards. FinOps recommendation engines provide automated compliance management capabilities, enabling enterprises to monitor, audit, and report on cloud usage and costs in real-time. This not only mitigates risks associated with non-compliance but also streamlines financial governance processes, further boosting the adoption of these solutions across regulated industries.

The proliferation of hybrid and multi-cloud strategies among large enterprises and SMEs alike is also propelling market growth. As businesses seek to avoid vendor lock-in and optimize workloads across public, private, and hybrid clouds, the need for intelligent financial management tools has become paramount. The ability of FinOps recommendation engines to provide granular visibility into cloud spending, predict future costs, and recommend cost-saving measures is driving their integration into enterprise IT ecosystems. Furthermore, the growing trend of digital transformation and the increasing reliance on cloud-native applications are expected to sustain the demand for these engines over the coming years.

Regionally, North America dominates the Multi-Cloud FinOps Recommendation Engine market, accounting for the largest revenue share in 2024. The region's leadership is attributed to the early adoption of cloud technologies, a mature FinOps ecosystem, and the presence of major cloud service providers and technology vendors. However, Asia Pacific is emerging as the fastest-growing market, driven by rapid digitalization, expanding cloud infrastructure, and increasing awareness of cloud financial management best practices among enterprises. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, supported by rising cloud investments and the need for cost-effective cloud management solutions.

Component Analysis

The Component segment of the Multi-Cloud FinOps Recommendation Engine market is bifurcated into Software and Services. The software sub-segment dominates the market, accounting for a significant share of total revenue in 2024. This dominance is attributed to the increasing deployment of AI-driven analytics platforms that provide real-time recommendations for cloud cost optimization and resource allocation. These software solutions are designed to seamlessly integrate

All the movies are sorted by their degrees in a ascending order (horizontal ordinate). For a given movie , other movies are sorted by their similarities with in a ascending order (vertical ordinate) and the color depth denotes the value of similarity.

The importance of online recommender systems for drugs, medical professionals, and hospitals is growing. Today, the majority of people use online consultations for drug recommendations for all types of health issues. Emergencies such as pandemics, floods, or cyclones can be helped by the medical recommender system. In the era of machine learning (ML), recommender systems produce more accurate, quick, and reliable clinical predictions with minimal costs. As a result, these systems maintain better performance, integrity, and privacy of patient data in the decision-making process and provide precise information at any time. Therefore, we present drug recommender systems with a stacked artificial neural network (ANN) model to improve the fairness and safety of treatment for infectious diseases. To reduce side effects, drugs are recommended based on a patient’s previous health profile, lifestyle, and habits. The proposed system produced results with 97.5% accuracy. A system such as this could be useful in recommending safe medicines to patients, especially during health emergencies.

The basic statistics of using a PFNF algorithm on Douban datasets.

hese days, the volume of data is increasing at a faster rate, necessitating scalable, personalized, and effective recommendation systems that can also adapt to changing user context. The impact of contextual elements, such as location, time, and device kinds, is sometimes overlooked by classical recommendation systems, which primarily concentrate on heuristic data and user preferences. This paper proposes a scalable context-aware recommendation framework that leverages the Hadoop ecosystem in order to process and examine big data efficiently. The proposed framework provides more accurate and tailored recommendations across a variety of disciplines by integrating the mentioned described contextual information into the recommendation process. However, the Hadoop ecosystem, which consists of elements like MapReduce, Mahout, Hive, and Hadoop Distributed File Systems (HDFS), is used to handle massive databases that allow for high scalability and performance under heavy data loads. This framework is demonstrated to increase recommendation accuracy by up to 20% when compared to traditional methods through simulations, particularly the association of real-world problem-based databases. As a result, when scaling to databases with more than 10 million records, the processing time ratio is reduced by 30%. Furthermore, the computational efficiency of the suggested framework is demonstrated by the fact that it can process up to 2 Terabytes (TB) of data in less than 7200s. Because of this, the suggested solution can be used in e-commerce, healthcare, and entertainment, and it primarily has to provide real-time, context-sensitive recommendations. This is one of the instances that shows how big data analytics may enhance user experiences by providing recommendations that are both computationally scalable and contextually relevant.

Standardized Hudup dataset receives information from raw data, which is composed of ten units such as “hdp_config”, “hdp_account”, “hdp_attribute_map”, “hdp_nominal”, “hdp_user”, “hdp_item”, “hdp_rating”, “hdp_context_template”, “hdp_context”, and “hdp_sample”. Each unit has particular functions, which is described in the section of data description. Hudup dataset is meta-data which models any raw data with abstract level. The default raw data which is source of Hudup dataset here is Movielens 1M. It is possible to consider that Hudup dataset is secondary data whereas Movielens is primary data. The raw rating data Movielens (GroupLens, 1998) 1M has 1,000,209 ratings from 6,040 users on 3,900 movies (items), which is available at https://files.grouplens.org/datasets/movielens/ml-1m.zip.

Outfit Recommendation AI App Market Outlook

According to our latest research, the global Outfit Recommendation AI App market size reached USD 1.57 billion in 2024 and is expected to grow at a robust CAGR of 21.6% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 11.65 billion. This impressive growth trajectory is propelled by the increasing adoption of artificial intelligence in the fashion sector, rising consumer demand for personalized shopping experiences, and the integration of advanced technologies like computer vision and machine learning into mobile and web applications.

The primary growth driver for the Outfit Recommendation AI App market is the surging consumer preference for personalized fashion solutions. With the proliferation of digital devices and mobile internet, consumers are seeking more tailored and convenient shopping experiences. AI-powered outfit recommendation apps leverage vast datasets, including user preferences, purchase history, body measurements, and even current fashion trends, to provide highly curated clothing suggestions. This not only enhances user satisfaction but also fosters brand loyalty and increases conversion rates for fashion retailers. The rapid evolution of AI algorithms and the ability to process and analyze complex data in real-time have made these applications more accurate and responsive, further fueling their adoption across individual and enterprise users.

Another significant factor contributing to the market’s growth is the strategic integration of AI outfit recommendation engines with retail and e-commerce platforms. As competition intensifies in the online fashion retail space, brands are constantly seeking innovative ways to differentiate themselves and engage customers. AI-driven apps are being embedded into retailer websites, mobile apps, and even social media platforms to offer seamless, interactive, and visually engaging shopping experiences. Features such as virtual fitting rooms, real-time style suggestions, and augmented reality try-ons are becoming mainstream, reducing return rates and improving customer retention. The synergy between AI outfit recommendation tools and digital retail infrastructure is unlocking new revenue streams and optimizing operational efficiencies for businesses.

Additionally, technological advancements in computer vision, natural language processing, and cloud computing are playing a pivotal role in shaping the Outfit Recommendation AI App market. These technologies enable apps to analyze clothing images, understand fashion semantics, and deliver contextually relevant suggestions. The rise of social commerce and influencer-driven marketing has further accelerated the integration of AI-powered styling tools into social media platforms, making fashion discovery more interactive and personalized. Startups and established tech companies alike are investing heavily in research and development to enhance the predictive accuracy and user experience of their recommendation engines, thus driving continuous innovation and market expansion.

From a regional perspective, North America currently leads the market, owing to a high concentration of tech-savvy consumers, established fashion e-commerce players, and significant investments in AI research. However, the Asia Pacific region is emerging as the fastest-growing market, driven by the rapid digitalization of retail, increasing smartphone penetration, and a burgeoning middle-class population with a keen interest in fashion and technology. Europe also holds a substantial share, supported by a strong fashion industry and growing adoption of omnichannel retail strategies. Regional market dynamics are influenced by factors such as cultural preferences, regulatory frameworks, and the pace of technological adoption, all of which are shaping the competitive landscape and future growth prospects of the Outfit Recommendation AI App market.

Component Analysis

The Outfit Recommendation AI App market is segmented by component into software and services, each playing a critical role in the overall ecosystem. The software segment dominates the market, accounting for the largest revenue share in 2024. This segment encompasses proprietary algorithms, mobile and web applications, integration APIs, and AI-powered analytics platforms. The demand for robust, scalable, and user-friendly software solutions is intensifying as fashion retailers and individual users seek to harness the p

AI Wine Recommendation Market Outlook

According to our latest research, the AI Wine Recommendation Market size reached USD 1.12 billion in 2024, demonstrating robust momentum driven by the rapid adoption of artificial intelligence in consumer-facing industries. The market is expected to exhibit a remarkable compound annual growth rate (CAGR) of 18.7% from 2025 to 2033, projecting a value of USD 6.06 billion by 2033. This impressive growth is primarily attributed to increasing digital transformation across the wine industry, rising consumer demand for personalized experiences, and the integration of AI-powered analytics in retail and hospitality sectors.

One of the primary growth factors propelling the AI Wine Recommendation Market is the escalating consumer expectation for tailored and seamless wine selection experiences. As wine consumers become more sophisticated, they seek recommendations that account for their unique taste profiles, dining occasions, and even health preferences. Artificial intelligence leverages vast data sets, including user preferences, ratings, purchase history, and even social media activity, to generate hyper-personalized wine suggestions. This level of personalization not only enhances customer satisfaction but also significantly increases conversion rates for retailers and e-commerce platforms. Furthermore, the integration of AI-driven systems into mobile apps and digital kiosks has made wine recommendations more accessible, thereby expanding the market’s reach beyond traditional sommeliers and wine experts.

Another significant driver is the digital transformation occurring within the wine industry, particularly among retailers, wineries, and hospitality providers. The adoption of AI-powered recommendation engines is revolutionizing inventory management, marketing strategies, and customer engagement. Retailers are now able to offer real-time, data-driven recommendations both online and in-store, optimizing their product offerings and reducing wastage. Wineries are leveraging AI to analyze consumer feedback and market trends, enabling them to craft wines that better align with evolving consumer preferences. In hospitality, AI wine recommendation tools are being integrated into restaurant management systems, allowing servers to provide expert-level suggestions, thus elevating the overall dining experience and boosting sales of premium wines.

The proliferation of e-commerce and digital retail channels is further catalyzing growth in the AI Wine Recommendation Market. As consumers increasingly turn to online platforms for wine purchases, the need for intuitive and intelligent recommendation systems becomes more pronounced. AI-powered platforms are capable of guiding users through extensive catalogs, filtering options based on taste, budget, and occasion, and even educating consumers through interactive content. This digital shift is particularly pronounced in regions with high internet penetration and a tech-savvy consumer base. Additionally, the COVID-19 pandemic has accelerated the adoption of contactless and digital solutions, further embedding AI wine recommendation tools into the consumer journey.

Regionally, North America currently leads the market, driven by early technology adoption, a mature wine culture, and strong investments in AI innovation. Europe follows closely, benefiting from its rich wine heritage and the integration of advanced technologies in retail and hospitality sectors. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by a burgeoning middle class, rising disposable incomes, and increasing openness to digital experiences. Latin America and the Middle East & Africa are emerging markets, gradually embracing AI-driven solutions as digital infrastructure improves and consumer awareness rises. The interplay of these regional dynamics underscores the global potential of the AI Wine Recommendation Market as it continues to evolve through 2033.

Component Analysis

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AI Size Recommendation Widget Market Outlook

According to our latest research and analysis, the AI Size Recommendation Widget market size reached USD 1.42 billion globally in 2024, reflecting the rapid adoption of artificial intelligence in retail and e-commerce environments. With a robust compound annual growth rate (CAGR) of 18.7% projected from 2025 to 2033, the market is expected to expand to USD 6.91 billion by 2033. This substantial growth is primarily driven by the increasing demand for personalized shopping experiences and the necessity for retailers to reduce product returns and enhance customer satisfaction.

One of the core growth factors for the AI Size Recommendation Widget market is the ongoing digital transformation within the retail sector. Retailers and brands are under constant pressure to improve the customer journey and minimize friction points, particularly in online shopping where size and fit issues are the leading causes of product returns. The integration of AI-powered size recommendation widgets addresses these challenges by leveraging advanced algorithms, machine learning, and data analytics to provide accurate size suggestions based on user data, previous purchases, and even social media activity. This not only reduces return rates but also boosts consumer confidence, leading to higher conversion rates and increased brand loyalty. As more retailers recognize the direct correlation between AI-driven personalization and revenue growth, investment in these widgets is accelerating.

Another significant driver is the proliferation of e-commerce platforms and the corresponding rise in cross-border online shopping. With consumers purchasing apparel, footwear, and accessories from international brands, sizing inconsistencies across regions have become a notable pain point. AI Size Recommendation Widgets can bridge this gap by standardizing size recommendations and adapting to local sizing charts, thus ensuring a seamless shopping experience for global customers. The adoption of cloud-based solutions further enhances scalability and accessibility, making it easier for small and medium enterprises (SMEs) as well as large enterprises to deploy these widgets without heavy upfront investments in infrastructure. The trend toward mobile commerce and omnichannel retailing is also propelling the market forward, as AI widgets can be seamlessly integrated into mobile apps and physical stores alike.

The market is also benefiting from advancements in computer vision and deep learning technologies, which are enabling more sophisticated and accurate body measurement techniques. Solutions now incorporate features such as virtual try-ons, 3D body scans, and AR-powered fitting rooms, all powered by AI engines that learn and improve over time. These innovations not only enhance the accuracy of size recommendations but also create unique, engaging shopping experiences that differentiate brands in a crowded marketplace. The growing availability of high-quality data, combined with the falling cost of AI technologies, is lowering barriers to entry and fostering innovation across the ecosystem. As a result, the competitive landscape is becoming more dynamic, with established players and startups alike racing to capture market share.

From a regional perspective, North America continues to dominate the AI Size Recommendation Widget market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States is at the forefront due to its advanced retail infrastructure, high e-commerce penetration, and early adoption of AI technologies. However, Asia Pacific is witnessing the fastest growth, driven by the explosive rise of online shopping in countries such as China, India, and Southeast Asian nations. The region's burgeoning middle class, increasing smartphone penetration, and tech-savvy consumers are creating fertile ground for AI-based retail innovations. Meanwhile, Europe is also experiencing steady growth, particularly in markets like the UK, Germany, and France, where fashion and apparel e-commerce is highly developed. Latin America and the Middle East & Africa are emerging markets, with significant potential as digital infrastructure matures and consumer preferences shift toward online shopping.

Component Analysis

The Component segment of the AI Size Recommendation Widget market is categorized into Software, Hardware, and Services. Sof