Collaborative Filtering

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

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.

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.

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Genre annotations for movies The file genre2movies.csv contains genre-movie tuples based on Wikidata annotations (https://www.wikidata.org/). Data Each line in genre2movies.csv represents one genre-movie tuple. The first entry is the genre. The second entry of each line is the movie name. There are 83,670 genre-movie tuples. Joining with the Movielens 20M dataset The movies considered are from the Movielens 20M corpus: https://grouplens.org/datasets/movielens/20m/ The movie names in genre2movies.csv match the movie 'titles' in Movielens 20M. Compositions The directory "compositions" contains movies assigned to compositions of genres. The compositions are of the form: "genre A and genre B", "genre A and not genre B", "genre A and genre B and genre C", "genre A and genre B and not genre C". These assignments have been automatically generated from genre2movies.csv. We try to generate genre-compositions that are useful, e.g., for a "genre A and genre B" composition we ensure that genre B is not a subgenre of genre A, because an interesection of a superset with a subset is identical to the subset and does not form a new concept.