The Pinterest dataset contains more than 1 million images associated to Pinterest users’ who have “pinned” them.

Pinterest dataset contains 109M records and 13 different attributes. Use our Pinterest profiles dataset (public data) to extract business and non-business insights from complete public profiles and filter by followers, boards amount, or locations. Tailor your dataset to suit your specific requirements, whether you need the complete dataset or a customized subset.

The Pinterest Fashion Compatibility dataset comprises images showcasing fashion products, each annotated with bounding boxes and associated with links directing to the corresponding products. This dataset facilitates the exploration of scene-based complementary product recommendation, aiming to complete the look presented in each scene by recommending compatible fashion items.

Basic Statistics: - Scenes: 47,739 - Products: 38,111 - Scene-Product Pairs: 93,274

Metadata: - Product IDs: Identifiers for the products featured in the images. - Bounding Boxes: Coordinates specifying the location of each product within the image.

Example (fashion.json): The dataset contains JSON entries where each entry associates a product with a scene, along with the bounding box coordinates for the product within the scene. json { "product": "0027e30879ce3d87f82f699f148bff7e", "scene": "cdab9160072dd1800038227960ff6467", "bbox": [ 0.434097, 0.859363, 0.560254, 1.0 ] }

Citation: If you utilize this dataset, please cite the following paper: Title: Complete the Look: Scene-based complementary product recommendation Authors: Wang-Cheng Kang, Eric Kim, Jure Leskovec, Charles Rosenberg, Julian McAuley Published in: CVPR, 2019 Link to paper

Code and Additional Resources: For additional resources, sample code, and instructions on how to collect the product images from Pinterest, you can visit the GitHub repository.

This dataset provides a rich ground for research and development in the domain of fashion-based image recognition, product recommendation, and the exploration of fashion styles and trends through machine learning and computer vision techniques.

The Pinterest Complete the Look dataset consists of over 1 million outfits and 4 million objects. It can be used to predict style compatibility between fashion items in order to recommend complementary items that complete an outfit.

Image-based platform Pinterest reached a record number of monthly active users (MAUs) in the most recently reported financial quarter. In the first quarter of 2025, Pinterest had 570 million monthly active users worldwide, up from 553 million users when compared to the previous quarter. The company Launched in March 2010, Pinterest was the fastest site in history to reach 10 million unique monthly visitors and high user engagement metrics; a significant feat especially when considering that during much of its initial launch and growth, the site was invitation-only. Despite many social networks going public within a few years post-launch (Twitter: six years; Snap Inc.: six years), Pinterest only filed for an IPO in February 2019, almost nine years after launch, and started trading on April 18, 2019. Pinterest usage The social site’s strong user engagement with popular topics and themes such as fashion, home, garden and DIY and subsequently, relevant brands and influencers, forms the basis for its leading position in the area of social marketing and commerce. In 2018, 600 million searches were performed using Pinterest Lens, a camera search that recommends ideas to try inspired by things seen offline.

Pinterest

## Overview

Pinterest is a dataset for object detection tasks - it contains Pinterest annotations for 867 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

zenai-org/headshot-pinterest dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains images (scenes) containing fashion products, which are labeled with bounding boxes and links to the corresponding products.

This dataset contains features extracted with EfficientNet-B3 from over 60000 images classified in 15 interests categories 4 of which are further analyzed in subcategories, features from the images of 12 users with hand-labeled Pinboards, the text extracted from the previous images and over 200000 features from the images of 422 Pinterest users along with their follower-followee relationships.

The number of Pinterest users in the United States was forecast to continuously increase between 2024 and 2028 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach ***** million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to *** countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Pinterest users in countries like Canada and Mexico.

This dataset was used for the experiments presented in the article "Reconstructive Classification for Age and Gender Identification in Social Networks" - IEEE Transactions on Computational Social Systems

The dataset contains text data from 548,761 pins corresponding to 264 users of Pinterest.

There are 7 files.

The first 5 files correspond to the extracted textual features from the pins that are aggregated per user: ats, emojis/emoticons, hashtags, links, and words.

There are 264 lines in each file (one per user), as the concatenation of the extracted features from all the pins corresponding to each user.

The last 2 files are the labels for the age and gender of the users. There are also 264 lines (one per user).

For age, there are 4 possible labels: 18-24, 25-34, 35-46, and 50+

For gender, there are 2 possible labels: F and M

Context

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fb984828c354c4d8e72965aa78d5503ee%2Fpinterest2.png?generation=1700270897504206&alt=media" alt="">

Based on their wikipedia page

Pinterest is an American image-sharing and social media service designed to enable saving and discovery of information (specifically "ideas") like recipes, home, style, motivation, and inspiration on the internet using images and, on a smaller scale, animated GIFs and videos, in the form of pinboards. The site was created by Ben Silbermann, Paul Sciarra, and Evan Sharp and it is operated by now Pinterest, Inc., and headquartered in San Francisco.

These reviews were extracted from its Google Store page.

Usage

This dataset should paint a good picture on what is the public's perception of the app over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of the app had the most positive feedback, the worst.
3. Use topic modeling to identify the pain points of the application.

(AND MANY MORE!)

Note

Images generated using Bing Image Generator

The Pinterest research & publication dataset, which was indexed by Scopus from 2011 to 2019. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, abstract, index keywords, references, Correspondence Address, editors, publisher, conference name, conference date, conference code, ISSN, language, document type, access type, and EID.

Pinterest stock-based compensation for the twelve months ending March 31, 2025 was $1.879B, a 16.87% increase year-over-year. Pinterest annual stock-based compensation for 2024 was $0.766B, a 18.2% increase from 2023. Pinterest annual stock-based compensation for 2023 was $0.648B, a 30.32% increase from 2022. Pinterest annual stock-based compensation for 2022 was $0.497B, a 19.68% increase from 2021.

Auto-generated structured data of Pinterest Ads Field Reference from table Fields

Pinterest total liabilities for the quarter ending March 31, 2025 were $0.594B, a 13.65% increase year-over-year. Pinterest total liabilities for 2024 were $0.592B, a 17.43% increase from 2023. Pinterest total liabilities for 2023 were $0.504B, a 13.31% decline from 2022. Pinterest total liabilities for 2022 were $0.581B, a 16.57% increase from 2021.

Pinterest's market share in Romania peaked at over **** percent at the beginning of 2020. By January 2025, Pinterest's market share was **** percent.

Pinterest basic shares outstanding for the twelve months ending March 31, 2025 were $2.717B, a 0.74% increase year-over-year. Pinterest annual basic shares outstanding for 2024 were $0.679B, a 0.62% increase from 2023. Pinterest annual basic shares outstanding for 2023 were $0.675B, a 1.34% increase from 2022. Pinterest annual basic shares outstanding for 2022 were $0.666B, a 4.02% increase from 2021.

Traffic analytics, rankings, and competitive metrics for pinterest.com as of May 2025

A complete list of live websites using the Wp Pinterest technology, compiled through global website indexing conducted by WebTechSurvey.