In the fast-paced digital world, recommender systems have become invaluable tools for enhancing user experiences, whether it’s finding the next binge-worthy web series or discovering the perfect online purchase. These systems utilize advanced algorithms to predict and estimate user preferences, making recommendations that cater to individual tastes. Major online platforms like Facebook, Netflix, and Myntra have harnessed the power of recommender systems to optimize user satisfaction. In this article, we will explore ten crucial datasets that are instrumental in building robust recommender systems.
#1 | MovieLens 25M Dataset
About: The MovieLens 25M Dataset is an extensive collection of movie ratings gathered from the MovieLens website. Comprising 250,000,095 ratings and 1,093,360 tag applications across 62,423 movies, this dataset offers a wealth of information for building recommender systems. Created by 162,541 users over a span of nearly 25 years, from January 9, 1995, to November 21, 2019, it provides valuable insights into user preferences and trends.
Click here to access the MovieLens 25M Dataset.
#2 | Social Network Influencer
About: Peerindex presents the Social Network Influencer dataset, designed for pairwise preference learning. Each datapoint in this dataset characterizes two individuals based on pre-computed, standardized features derived from their Twitter activity. These features include the volume of interactions, the number of followers, and more. Leveraging this dataset, one can develop machine learning models with high accuracy to identify influential individuals in social networks.
Click here to access the Social Network Influencer dataset.
#3 | Million Song Dataset
About: The Million Song Dataset offers a vast collection of audio features and metadata for a million contemporary popular music tracks. Powered by Echo Nest, this dataset primarily focuses on feature analysis and metadata for songs. It serves multiple purposes, including fostering research on scalable algorithms, providing a benchmark for evaluating new methodologies, facilitating the entry of new researchers into the field of Music Information Retrieval (MIR), and much more.
Click here to access the Million Song Dataset.
#4 | Free Music Archive
About: The Free Music Archive (FMA) is a treasure trove of high-quality, legally downloadable audio files tailored for music analysis. Spanning an impressive 917 gigabytes and 343 days of Creative Commons-licensed audio, the FMA dataset comprises 106,574 tracks contributed by 16,341 artists across 14,854 albums. It also includes a hierarchical taxonomy of 161 genres, track- and user-level metadata, tags, and free-form text such as artist biographies. This dataset proves invaluable for various tasks in Music Information Retrieval (MIR).
Click here to access the Free Music Archive dataset.
#5 | Netflix Prize Dataset
About: The Netflix Prize dataset represents a multivariate, time-series dataset used in the renowned Netflix Prize competition. With approximately 100 million movie ratings and over 480,000 customers identified by unique integer IDs, this dataset serves as a fertile ground for predicting missing entries in movie-user rating matrices. Analyzing this dataset helps in improving the accuracy of recommender systems and enhancing personalized recommendations.
Click here to access the Netflix Prize Dataset.
#6 | Book-Crossing Dataset
About: The Book-Crossing Dataset captures the dynamics of the Book-Crossing community over a four-week crawl. It encompasses anonymized information about 278,858 users, including demographic data, and provides 1,149,780 explicit and implicit ratings for 271,379 books. This dataset offers valuable insights into user preferences for books, making it an essential resource for building book recommendation systems.
Click here to access the Book-Crossing Dataset.
#7 | Amazon Review Data
About: The Amazon Review Data is a comprehensive collection of reviews, ratings, and product metadata from Amazon. This dataset encompasses a staggering 233.1 million reviews and offers valuable information such as helpfulness votes, product descriptions, category details, pricing, brand information, image features, and more. Researchers can leverage this dataset to gain deep insights into customer preferences, sentiment analysis, and recommender system evaluations.
Click here to access the Amazon Review Data.
#8 | Yahoo! Music User Ratings
About: The Yahoo! Music User Ratings dataset represents the preferences of the Yahoo! Music community for various musical artists. With over 10 million artist ratings provided by Yahoo! Music users, this dataset proves useful for validating recommender systems and collaborative filtering algorithms. Moreover, it serves as a testbed for matrix and graph algorithms, including Principal Component Analysis (PCA) and clustering algorithms.
Click here to access the Yahoo! Music User Ratings dataset.
#9 | LastFM
About: The LastFM dataset encompasses social networking, tagging, and music artist listening information from a group of 2,000 users of the Last.fm online music system. It includes a collection of 17,632 music artists listened to and tagged by the users, making it an excellent resource for studying music preferences, user behavior, and personalized music recommendations.
Click here to access the LastFM dataset.
#10 | Steam Video Games
About: The Steam Video Games dataset offers insights into user behaviors related to the popular PC gaming platform, Steam. With columns including user ID, game title, behavior name, and value indicating the degree of a particular behavior, this dataset sheds light on user preferences, purchase patterns, and gameplay activities. It serves as a valuable resource for understanding gaming trends and developing personalized game recommendations.
Click here to access the Steam Video Games dataset.
By utilizing a combination of these datasets in different orders and employing relevant links for each, one can create a powerful recommender system tailored to specific needs. These datasets provide a rich source of information, allowing developers to understand user preferences, enhance recommendation accuracy, and create personalized experiences.