What are the common datasets used to evaluate recommender systems?

Recommender systems are typically evaluated using datasets that capture user-item interactions, often accompanied by metadata. Common examples include MovieLens, Amazon Product Data, Netflix Prize, and Last.fm. These datasets vary in size, domain, and structure, enabling developers to test algorithms under different conditions. For instance, MovieLens provides movie ratings, while Amazon’s dataset includes product reviews and purchase histories. These datasets are widely adopted because they offer realistic scenarios, such as sparse interactions or cold-start problems, which are critical for assessing a recommender’s robustness.

MovieLens is a benchmark dataset for collaborative filtering, available in sizes ranging from 100,000 to 25 million ratings. It includes user ratings (1-5 stars) for movies, along with genre and timestamp data. The Amazon Product Dataset contains product reviews, metadata (e.g., product categories), and user-item graphs, making it suitable for testing hybrid models that combine collaborative and content-based filtering. The Netflix Prize dataset, though no longer publicly available, was a large-scale collection of movie ratings used in a 2006 competition, and it remains a reference for evaluating scalability. Last.fm focuses on music recommendations, providing implicit feedback (e.g., play counts) and social network data, which is useful for testing models that handle non-explicit user behavior.

When choosing a dataset, developers should consider the problem’s requirements. For example, MovieLens is ideal for explicit feedback scenarios (e.g., predicting ratings), while Last.fm suits implicit feedback tasks (e.g., predicting user engagement). Datasets like Amazon’s are valuable for testing recommendations in e-commerce, where metadata and temporal dynamics matter. Preprocessing steps, such as filtering sparse interactions or splitting data into train/test sets, are often necessary. Metrics like RMSE (for rating prediction) or precision@k (for top-N recommendations) are applied based on the dataset’s structure. Publicly available splits (e.g., Netflix’s test set) help standardize comparisons, but custom splits may be needed for domain-specific evaluations.