Recommendation Systems

The MovieLens datasets, first released in 1998, describe people’s expressed preferences for movies. These preferences take the form of tuples, each the result of a person expressing a preference (a 0-5 star rating) for a movie at a particular time. These preferences were entered by way of the MovieLens web site1 — a recommender system that asks its users to give movie ratings in order to receive personalized movie recommendations. Source: The MovieLens Datasets: History and Context Image Source: http://files.grouplens.org/papers/harper-tiis2015.pdf

Netflix Prize consists of about 100,000,000 ratings for 17,770 movies given by 480,189 users. Each rating in the training dataset consists of four entries: user, movie, date of grade, grade. Users and movies are represented with integer IDs, while ratings range from 1 to 5. Source: The Netflix Prize Image Source: https://www.netflixprize.com/

Gowalla is a location-based social networking website where users share their locations by checking-in. The friendship network is undirected and was collected using their public API, and consists of 196,591 nodes and 950,327 edges. We have collected a total of 6,442,890 check-ins of these users over the period of Feb. 2009 - Oct. 2010.

The Yelp2018 dataset is adopted from the 2018 edition of the yelp challenge. Wherein local businesses like restaurants and bars are viewed as items. We use the same 10-core setting in order to ensure data quality.

ReDial (Recommendation Dialogues) is an annotated dataset of dialogues, where users recommend movies to each other. The dataset consists of over 10,000 conversations centered around the theme of providing movie recommendations. Source: Towards Deep Conversational Recommendations

The Yelp Dataset is a valuable resource for academic research, teaching, and learning. It provides a rich collection of real-world data related to businesses, reviews, and user interactions. Here are the key details about the Yelp Dataset: Reviews: A whopping 6,990,280 reviews from users. Businesses: Information on 150,346 businesses. Pictures: A collection of 200,100 pictures. Metropolitan Areas: Data from 11 metropolitan areas. Tips: Over 908,915 tips provided by 1,987,897 users. Business Attributes: Details like hours, parking availability, and ambiance for more than 1.2 million businesses. Aggregated Check-ins: Historical check-in data for each of the 131,930 businesses.

We release Douban Conversation Corpus, comprising a training data set, a development set and a test set for retrieval based chatbot. The statistics of Douban Conversation Corpus are shown in the following table. | |Train|Val| Test | | ------------- |:-------------:|:-------------:|:-------------:| | session-response pairs | 1m|50k| 10k | | Avg. positive response per session | 1|1| 1.18 | | Fless Kappa | N\A|N\A|0.41 | | Min turn per session | 3|3| 3 | | Max ture per session | 98|91|45 | | Average turn per session | 6.69|6.75|5.95 | | Average Word per utterance | 18.56|18.50|20.74 | The test data contains 1000 dialogue context, and for each context we create 10 responses as candidates. We recruited three labelers to judge if a candidate is a proper response to the session. A proper response means the response can naturally reply to the message given the context. Each pair received three labels and the majority of the labels was taken as the final decision. <br> As far as we known, this is the first human-labeled test set for retrieval-based chatbots. The entire corpus link https://www.dropbox.com/s/90t0qtji9ow20ca/DoubanConversaionCorpus.zip?dl=0

This dataset contains 21,889 outfits from polyvore.com, in which 17,316 are for training, 1,497 for validation and 3,076 for testing. Source: GitHub Image Source: https://arxiv.org/pdf/1707.05691.pdf

The Epinions dataset is built form a who-trust-whom online social network of a general consumer review site Epinions.com. Members of the site can decide whether to ''trust'' each other. All the trust relationships interact and form the Web of Trust which is then combined with review ratings to determine which reviews are shown to the user. It contains 75,879 nodes and 50,8837 edges. Source: https://snap.stanford.edu/data/soc-Epinions1.html

KuaiRec is a real-world dataset collected from the recommendation logs of the video-sharing mobile app Kuaishou. For now, it is the first dataset that contains a fully observed user-item interaction matrix. For the term “fully observed”, we mean there are almost no missing values in the user-item matrix, i.e., each user has viewed each video and then left feedback.

The Foursquare dataset consists of check-in data for different cities. One subset contains check-ins in NYC and Tokyo collected for about 10 month (from 12 April 2012 to 16 February 2013). It contains 227,428 check-ins in New York city and 573,703 check-ins in Tokyo. Each check-in is associated with its time stamp, its GPS coordinates and its semantic meaning (represented by fine-grained venue-categories). Another subset contains long-term (about 18 months from April 2012 to September 2013) global-scale check-in data collected from Foursquare. It contains 33,278,683 checkins by 266,909 users on 3,680,126 venues (in 415 cities in 77 countries). Those 415 cities are the most checked 415 cities by Foursquare users in the world, each of which contains at least 10K check-ins. Source: https://sites.google.com/site/yangdingqi/home/foursquare-dataset

KuaiRand is an unbiased sequential recommendation dataset collected from the recommendation logs of the video-sharing mobile app, Kuaishou (快手). It is the first recommendation dataset with millions of intervened interactions of randomly exposed items inserted in the standard recommendation feeds!

This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. 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).

The Memetracker corpus contains articles from mainstream media and blogs from August 1 to October 31, 2008 with about 1 million documents per day. It has 10,967 hyperlink cascades among 600 media sites. Source: Marked Temporal Dynamics Modeling based on Recurrent Neural Network Image Source: http://blog.fabric.ch/index.php?/archives/292-Memetracker-Tracking-News-Phrases-over-the-Web.html

The General Video Game AI (GVGAI) framework is widely used in research which features a corpus of over 100 single-player games and 60 two-player games. These are fairly small games, each focusing on specific mechanics or skills the players should be able to demonstrate, including clones of classic arcade games such as Space Invaders, puzzle games like Sokoban, adventure games like Zelda or game-theory problems such as the Iterative Prisoners Dilemma. All games are real-time and require players to make decisions in only 40ms at every game tick, although not all games explicitly reward or require fast reactions; in fact, some of the best game-playing approaches add up the time in the beginning of the game to run Breadth-First Search in puzzle games in order to find an accurate solution. However, given the large variety of games (many of which are stochastic and difficult to predict accurately), scoring systems and termination conditions, all unknown to the players, highly-adaptive general methods are needed to tackle the diverse challenges proposed. Source: Rolling Horizon Evolutionary Algorithms for General Video Game Playing Image Source: http://www.gvgai.net/

Amazon Review is a dataset to tackle the task of identifying whether the sentiment of a product review is positive or negative. This dataset includes reviews from four different merchandise categories: Books (B) (2834 samples), DVDs (D) (1199 samples), Electronics (E) (1883 samples), and Kitchen and housewares (K) (1755 samples).

The Ciao dataset contains rating information of users given to items, and also contain item category information. The data comes from the Epinions dataset. Source: Collaborative Translational Metric Learning

The Pinterest dataset contains more than 1 million images associated to Pinterest users’ who have “pinned” them. Source: https://openaccess.thecvf.com/content_iccv_2015/papers/Geng_Learning_Image_and_ICCV_2015_paper.pdf

A human-to-human Chinese dialog dataset (about 10k dialogs, 156k utterances), which contains multiple sequential dialogs for every pair of a recommendation seeker (user) and a recommender (bot). Source: Towards Conversational Recommendation over Multi-Type Dialogs

TG-ReDial is a a topic-guided conversational recommendation dataset for research on conversational/interactive recommender systems. Source: https://github.com/RUCAIBox/TG-ReDial Image Source: https://github.com/RUCAIBox/TG-ReDial

The Yahoo! Learning to Rank Challenge dataset consists of 709,877 documents encoded in 700 features and sampled from query logs of the Yahoo! search engine, spanning 29,921 queries. Source: Ranking for Relevance and Display Preferencesin Complex Presentation Layouts

Amazon Sports (Amazon Sports 5-core)

Amazon Clothing (Amazon Clothing 5-core)

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).

N/A

The MMD (MultiModal Dialogs) dataset is a dataset for multimodal domain-aware conversations. It consists of over 150K conversation sessions between shoppers and sales agents, annotated by a group of in-house annotators using a semi-automated manually intense iterative process. Source: Towards Building Large Scale Multimodal Domain-Aware Conversation Systems

BeerAdvocate is a dataset that consists of beer reviews from beeradvocate. The data span a period of more than 10 years, including all ~1.5 million reviews up to November 2011. Each review includes ratings in terms of five "aspects": appearance, aroma, palate, taste, and overall impression. Reviews include product and user information, followed by each of these five ratings, and a plaintext review. Source: https://snap.stanford.edu/data/web-BeerAdvocate.html

A dataset containing 404,683 shop photos collected from 25 different online retailers and 20,357 street photos, providing a total of 39,479 clothing item matches between street and shop photos. Source: Where to Buy It: Matching Street Clothing Photos in Online Shops

This datasets is a subset of the Amazon reviews dataset which contain Men related products

Amazon-Sports is a sub-category of the Amazon dataset, which contains a series of product reviews crawled from Amazon.com.

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).

The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. The core of the dataset is the feature analysis and metadata for one million songs, provided by The Echo Nest. The dataset does not include any audio, only the derived features. Note, however, that sample audio can be fetched from services like 7digital, using code provided by the authors. Source: http://millionsongdataset.com/ Paper: The Million Song Dataset

This datasets is a subset of the Amazon reviews dataset which contain Fashion related products

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).

The WeChat dataset for fake news detection contains more than 20k news labelled as fake news or not.

CITE is a crowd-sourced resource for multimodal discourse: this resource characterises inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations. Source: CITE: A Corpus of Image-Text Discourse Relations