Multibehavior Recommendation

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

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.

Data from the popular Chinese online shopping platform Taobao includes behaviors like buy, add-to-cart, add-to-favorite, and pageview. The buying behavior is considered the target behavior.