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)

Ecommerce Product Recommendation Dataset using Content Filtering

This dataset provides a comprehensive collection of features for building a content-based recommendation system in an ecommerce environment. Content filtering, which relies on users' interests and past activities, is a prevalent method for suggesting products tailored to individual preferences.

Each entry in the dataset represents a product along with various attributes that can be leveraged for recommendation purposes. Here's an overview of the features included:

1) Number of clicks on similar products: Indicates the popularity or engagement level of similar items. 2) Number of similar products purchased so far: Reflects the conversion rate of similar products. 3) Average rating given to similar products: Offers insight into the perceived quality of comparable items. 4) Gender: Allows for gender-specific recommendations. 5) Median purchasing price (in rupees): Provides pricing information for segmentation or pricing strategy analysis. 6) Rating of the product: The rating of the product itself, indicating its overall quality. 7)**Brand of the product**: Brand loyalty or preference can influence recommendations. 8) Customer review sentiment score (overall): Sentiment analysis of customer reviews, indicating overall satisfaction. 9) Price of the product: The actual price of the product. 10) Holiday: Seasonal or holiday-specific buying patterns. 11) Season: Seasonal preferences may influence product choices. 12) Geographical locations: Regional preferences or availability may impact recommendations. 13) Probability for the product to be recommended to the person: The likelihood of recommending the product to a specific user based on their profile and past behavior.

With this rich set of features, businesses can implement sophisticated recommendation algorithms to personalize the shopping experience for users, ultimately leading to increased customer satisfaction, engagement, and sales.

This dataset provides a detailed, transaction-level view of customer shopping behavior, including customer profiles, product details, session context, and the impact of personalized product recommendations. It is ideal for developing and evaluating recommendation systems, analyzing sales conversion, and understanding the effectiveness of targeted offers in retail environments.

🎬 Movie Recommendation Dataset 📖 Overview

This dataset is created to help researchers, students, and practitioners build movie recommendation systems. It contains 20,000 rows of realistic, user-movie interaction data, blending popular movie titles, genres, and user behavior metrics.

It is ideal for experimenting with collaborative filtering, content-based filtering, hybrid approaches, and deep learning-based recommenders.

📂 Dataset Structure

The dataset has the following columns:

movie_title 🎥 The name of the movie (e.g., The Shawshank Redemption, Inception, Titanic). Titles are based on realistic movie names from popular cinema.

user_id 👤 A unique identifier for each user (e.g., User_1023). Simulates multiple users interacting with various movies.

genres 🎭 One or more genres for each movie (e.g., Action, Comedy, Drama). Useful for content-based recommendation.

watch_time ⏱️ Time spent (in minutes) by the user watching the movie. Higher watch time can imply greater engagement.

imdb_score ⭐ A score between 1.0 and 10.0, inspired by IMDb ratings. Acts as a measure of quality or popularity.

movie_likes ❤️ The number of likes a movie has (0–10,000). Works as an implicit popularity measure.

📊 Use Cases

Building Recommendation Systems (Collaborative Filtering, Content-based, Hybrid, Deep Learning models)

Exploratory Data Analysis (EDA) on user preferences

Data visualization (genre distribution, rating patterns, engagement analysis)

Practicing data cleaning, preprocessing, and feature engineering

Testing machine learning algorithms for personalization tasks

🌍 Source

The dataset is synthetically generated but uses realistic movie titles and attributes.

Movie names are inspired by popular films on IMDb and general cinema culture.

User interactions (watch time, likes, ratings) were simulated with statistical distributions to mimic real-world behavior.

⚠️ Disclaimer: This dataset was not scraped from IMDb, Netflix, or any external service. It was created for educational and research purposes only.

📌 Column Summary Column Type Description movie_title String The movie name (popular real-world titles included) user_id String Unique identifier for each user (e.g., User_1234) genres String One or more genres, comma-separated watch_time Integer Time in minutes user watched the movie (10–180) imdb_score Float IMDb-like score between 1.0 and 10.0 movie_likes Integer Number of likes the movie received (0–10,000) 🙌 Acknowledgements

This dataset was created for the data science and ML community to:

Build and test recommendation systems

Explore movie analytics

Practice ML modeling on structured data

If you use this dataset, a citation or link back is appreciated. ❤️

✨ Happy analyzing & recommending movies! ✨

recommender systems, user-item graph, social recommendation, item recommendation, movie recommendation, collaborative filtering data sets, recommendation data, download recommendation data, eachmovie, movielens, github, yelp-user-business, amazon ratings data, epinions, libimseti

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

This datasets contain anonymized user borrowing transactions and associated metadata collected from a university and public library system. The University Library dataset contains 14,148 users, 8,369 library items, and 52,394 borrowing interactions, exhibiting an extreme sparsity level of 99.97%. The Public Library dataset contains 53,008 users and 54,563 items with similarly sparse interaction patterns of 99.98%.

The dataset was developed to support research on recommender systems, particularly studies addressing the challenges of extreme interaction sparsity and cold-start conditions.

The collection consists of three interconnected data components: 1. User Data Anonymized user attributes including academic program, academic year, cumulative GPA, gender, and other demographic characteristics that can be used as auxiliary information for recommendation modeling.

1. Item Data Bibliographic metadata describing library resources, including title, author, publisher, publication year, ISBN, DDC classification, and location information. These attributes can support content-based recommendation and metadata-driven cold-start strategies.

2. Borrowing Transactions Historical borrowing records representing user–item interactions. These records can be transformed into user–item interaction matrices for collaborative filtering, hybrid recommendation systems, and interaction reconstruction research.

The dataset exhibits characteristics commonly observed in real-world recommendation environments, including: - Extreme interaction sparsity (>99%) - High proportions of cold-start users and cold-start items - Heterogeneous user and item metadata - Long-tail borrowing behavior

All personally identifiable information has been removed or anonymized prior to publication. The dataset is provided exclusively for research and educational purposes.

Methods: Data were extracted from the university library management system and subsequently anonymized. User identifiers were replaced with anonymous IDs, while personal information was removed to ensure privacy protection. Data preprocessing included: 1. feature selection by the domain expert. 2. Data cleansing, to addressing missing data and outlier, removed duplication, and standardization). 3. Feature transformation. The technique used is One-Hot Encoding, which is used to convert categorical data into a numeric representation so that it can be used by machine learning algorithms.
4. Metadata integration and validation

Keywords: Recommender Systems; Cold-Start Problem; Data Sparsity; Digital Library; Collaborative Filtering; Metadata-Based Recommendation; Matrix Densification; Library Analytics

The Sentiment-Based Product Recommendation System stands out as one of the most referenced examples in teaching recommendation systems at leading institutions like IITs (Indian Institutes of Technology), IISc, and top B-schools across India and worldwide. It’s widely used to demonstrate how sentiment from user reviews can elevate both User-Based Recommendation System and Item-Based Recommendation System models—by weaving emotional nuance into data-driven suggestions.

About the Sentiment-Based Product Recommendation System & Review Dataset 🛍️📝

What Makes the Dataset Popular?

• Real-World Scale: It comprises over 30,000+ product reviews spanning 200+ diverse product categories, submitted by more than 20,000+ users.
• Rich Content Mix: The dataset includes:
  • Structured fields like ratings, usernames, sentimental flags, products, brand, manufacturer and more.
  • Unstructured elements such as review titles and text content, ideal for sentiment modeling and explainable recommendations.
• Pedagogically Proven: Frequently adopted in courses and hackathons for teaching how to capture recommendation patterns—especially the differences between User-Based (e.g., "Users similar in taste") and Item-Based (e.g., "Items frequently bought together") recommender systems.

Why It’s a Go-To Teaching Tool

• Simple yet powerful: Combines numerical feedback (ratings) with semantic richness (text reviews), making it ideal for hands-on learning.
• Hybrid Learning Models: Perfect for experimenting with:
  1. User-Based Recommendation System — Recommendations based on users with similar sentiment profiles
  2. Item-Based Recommendation System — Suggestions derived from sentiment-filtered item similarities

Overview of dataset columns and their descriptions

Top Search-Engine Keywords for This Dataset

sentiment based product recommendation dataset
amazon reviews sentiment recommendation system
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user item based recommendation with sentiment
simplified sentiment product recommendation example

During a March 2024 survey among adults in the United States, around ** percent of respondents selected friends and family as a trustworthy product recommendation source. Recommendations from an expert reviewer followed with a share of ** percent, whereas artificial intelligence (AI) applications such as ChatGPT and Bard ranked fifth, chosen by less than ** percent of the interviewees.

Content-Based Recommendation System market was valued at $5.8 billion in 2025, projected to reach $18.6 billion by 2034, growing at 13.8% CAGR. Comprehensive analysis of software, services, e-commerce, media, and healthcare applications.

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.

Recommendation (Legislation). Legal source published by vLex ( part of Clio ).

AI Wine Recommendation Market Outlook

According to our latest research, the AI Wine Recommendation Market size reached USD 1.33 billion in 2025, 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 2026 to 2034, projecting a value of USD 6.21 billion by 2034. 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 globally.

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 grow more sophisticated, they seek recommendations that account for their unique taste profiles, dining occasions, and even dietary preferences. Artificial intelligence leverages vast data sets, including user preferences, ratings, purchase history, and 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, expanding the market's reach well beyond traditional sommeliers and wine experts. The convergence of digital wine pairing technology with broader AI recommendation platforms is accelerating this trend in 2025 and beyond.

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 can now 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 preferences. In hospitality, AI wine recommendation tools are being integrated into restaurant management systems, allowing servers to provide expert-level suggestions and elevate the overall dining experience. The growing deployment of AI solutions in restaurants is creating a robust pipeline of adoption for wine recommendation platforms across full-service and fine-dining segments.

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 guide users through extensive catalogs, filter options based on taste, budget, and occasion, and educate consumers through interactive content. This digital shift is particularly pronounced in regions with high internet penetration and a tech-savvy consumer base. Additionally, the acceleration of contactless and digital commerce since the early 2020s has deeply embedded AI wine recommendation tools into the modern consumer journey, with no signs of reversal heading into the 2026-2034 forecast window.

The rise of the AI sommelier concept is revolutionizing the wine industry by offering a unique blend of technology and tradition. Advanced AI systems now mimic the expertise of a human sommelier, providing wine enthusiasts with personalized recommendations based on taste preferences, food pairings, and dining occasion. By utilizing machine learning and vast databases of

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.

The AI-Based Recommendation Engine Market was valued at USD 4.25 Billion in 2025 and is projected to grow to USD 15 Billion by 2035, at a CAGR of 13.4%. Ai Based Recommendation Engine Market Overview: The AI-Based Recommendation Engine Market Size was valued at 3,750 USD Million in 2024. The AI-Based Recommendation Engine Market is expected to grow from 4,250 USD Million in 2025 to 15 USD Billion by 2035. The AI-Based Recommendation Engine Market CAGR (growth rate) is expected to be around 13.4% during the forecast period (2025 - 2035). Key Ai Based Recommendation Engine Market Trends Highlighted The Global AI-Based Recommendation Engine Market is witnessing significant growth due to the rising demand for personalized experiences among consumers across various sectors. Businesses are increasingly focusing on enhancing customer satisfaction and engagement, which serves as a key market driver. The integration of AI technologies enables companies to analyze user behavior and preferences, leading to more accurate recommendations. This trend is not limited to e-commerce; industries such as entertainment, travel, and finance are also leveraging recommendation engines to tailor their offerings and improve decision-making. Opportunities to be explored include the expansion of AI-based solutions to small and medium enterprises that have traditionally lagged in adopting such technologies.As cloud computing continues to grow globally, it provides a cost-effective platform for these businesses to implement AI-driven tools. This trend opens up a vast market potential, allowing smaller players to enhance their service delivery and remain competitive against larger corporations. In recent times, the focus has also shifted towards improving the transparency and explainability of AI recommendation systems. With increasing concerns over data privacy and algorithmic bias, companies are striving to build trust with consumers by ensuring that their systems are robust and fair. Additionally, advancements in natural language processing and machine learning are enhancing the effectiveness of recommendation engines, making them more adaptable to changing consumer preferences.Overall, the Global AI-Based Recommendation Engine Market is positioned to experience substantial growth, driven by these trends and the increasing adoption of AI technologies worldwide. As organizations continue to recognize the value of personalized engagement, the demand for sophisticated recommendation solutions is set to rise, contributing to a projected market size of 15.0 billion in USD by 2035. Source: Primary Research, Secondary Research, WGR Database and Analyst Review Ai Based Recommendation Engine Market Segment Insights: Ai Based Recommendation Engine Market Regional Insights In 2024, the North America segment, valued at 2 USD Billion, dominates the Global AI-Based Recommendation Engine Market, with expectations to grow to 7 USD Billion by 2035. This region holds a majority share due to its advanced technological infrastructure and strong adoption of AI technologies across various industries. Europe is experiencing steady expansion as businesses leverage AI for enhanced customer experiences and improved decision-making, contributing significantly to the market landscape. The APAC region showcases robust growth as emerging economies invest in digitalization and AI-driven solutions, effectively increasing their market presence.South America shows moderate increases driven by growing internet penetration and digital transformation initiatives within organizations. Moreover, the MEA region reflects gradual growth as companies begin to recognize the value of AI in driving efficiency and customer engagement. The Global AI-Based Recommendation Engine Market statistics indicate a favorable outlook driven by a rise in personalization trends, increasing data analytics demands, and a shift towards digital solutions across all industries. These trends underscore the burgeoning opportunities and challenges facing the industry as it adapts to evolving market dynamics. Source: Primary Research, Secondary Research, WGR Database and A

Recommender System is a system that seeks to predict or filter preferences according to the user’s choices. Recommender systems are utilized in a variety of areas including movies, music, news, books, research articles, search queries, social tags, and products in general.

Recommender systems produce a list of recommendations in any of the two ways:

1. Collaborative filtering: Collaborative filtering approaches build a model from the user’s past behavior (i.e. items purchased or searched by the user) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that users may have an interest in. 2. Content-based filtering: Content-based filtering approaches uses a series of discrete characteristics of an item in order to recommend additional items with similar properties. Content-based filtering methods are totally based on a description of the item and a profile of the user’s preferences. It recommends items based on the user’s past preferences.

Let’s develop a basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies. It just tells what movies/items are most similar to the user’s movie choice.

According to Market Research Intellect, the Content Recommendation Engine Market stood at USD 1.41 Billion in 2025 and is forecast to reach USD 4.57 Billion by 2035, progressing at a CAGR of 12.5%.

South America Machine Learning Recommendation Algorithm Market is segmented by Type (Collaborative Filtering, Content-Based Filtering, Hybrid Filtering, Context-Aware Filtering, Deep Learning-Based), Application (E-commerce, Media & Entertainment, Healthcare, Banking & Finance, Retail), Deployment Model (Cloud-based, On-premise, Hybrid), End User Industry (Retailers, Financial Institutions, Healthcare Providers, Media Companies, Telecommunications), and Geography (Brazil, Argentina, Chile, Peru, Colombia, Rest of South America)

This is a good first issue (GFI) recommendation dataset created from the GFI-Bot project (https://github.com/osslab-pku/gfi-bot). For more information about the GFI recommendation problem and GFI-Bot, please check our publications:

• Wenxin Xiao, Hao He, Weiwei Xu, Xin Tan, Jinhao Dong, and Minghui Zhou. 2022. Recommending Good First Issues in GitHub OSS Projects. In Proceedings of the 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 21–29, 2022. ACM. https://hehao98.github.io/files/2022-recgfi.pdf
• Hao He, Haonan Su, Wenxin Xiao, Runzhi He, and Minghui Zhou. 2022. GFI-Bot: Automated Good First Issue Recommendation on GitHub. Currently Under Review at the ESEC/FSE 2022 Demonstration Track. https://hehao98.github.io/files/2022-gfibot.pdf

The dataset is a MongoDB dump and needs to be restored to a MongoDB instance before use. This can be done via the official mongorestore tool by running a command like this in the dataset/ folder:

mongorestore --uri={{ your mongodb url }} --gzip

In the gfibot.dataset collection, each document describes the state of an issue at a certain time (either at the time of issue creation or at the time of issue resolution). The resolver_commit_num is the ground truth label (i.e., # of commits the issue resolver has made in the repository before issue resolution, excluding commits for resolving the issue itself; resolver_commit_num = 0 means the resolver is someone completely new to the repository). The remaining fields can be used as features or further analyzed to derive new features.

This dataset can be used to evaluate new GFI recommendation approaches. We hope it will be helpful in advancing GFI recommendation research and other future studies on open-source software onboarding.

Overview

This dataset contains structured user profiles and interaction feedback designed for building and evaluating profile matching and recommendation systems.

It includes detailed profile information such as demographic attributes, professional background, personality type, and descriptive text fields, along with simulated user interaction records indicating acceptance or rejection of recommended matches. These datasets support machine learning workflows involving similarity analysis, compatibility scoring, classification, and recommendation modeling.

Key Applications

The dataset is suitable for developing systems such as:

• Profile matching platforms
• Recommendation engines
• Professional networking systems
• User compatibility analysis tools
• Intelligent matching applications

It can be used for tasks involving:

• Natural Language Processing (NLP)
• Ranking and similarity algorithms
• Supervised learning models
• Recommendation system development
• Model evaluation and experimentation

Dataset Contents

users.csv
Contains structured user profile data including:

• User identifiers
• Demographic information
• Professional summaries
• Personality types
• Interest descriptions
• Text fields for similarity analysis

feedback.csv
Contains simulated interaction data including:

• User interactions with recommended profiles
• Acceptance or rejection responses
• Behavioral feedback records
• Timestamped activity data

Data Source and Creation

All data in this dataset was independently created to simulate realistic user behavior and structured profile information. The records were generated using consistent templates and controlled logic to ensure data quality, usability, and reproducibility for machine learning experiments and system development.

This dataset is intended for:

• Machine learning research
• Recommendation system development
• Similarity and matching analysis
• Educational and experimental purposes
• Prototype and application testing