Skimrelated

Skimrelated is a term used in information retrieval and content recommendation to describe methods that estimate item relatedness using skim representations of content. A skim is a lightweight summary that captures the essential ideas or themes of a document, video, or other item, typically produced by extractive summarization, keyword extraction, or compact embeddings. Skimrelated systems compute similarity between the skim of a query item and the skims of candidate items to rank related content, allowing fast processing on large catalogs.

Origin and scope: The approach gained prominence with advances in fast text representations and on-device or edge computing, enabling scalable matching based on brief content signals rather than full-text processing.

Methodology: Skimrelated generally involves a two-stage pipeline. First, a skim of each item is created, using methods such as short summaries, topic vectors, or sentence embeddings. Second, a similarity or relatedness score is computed between the query skim and each candidate skim, often combining multiple signals (semantic similarity, metadata, popularity) and producing a ranked list.

Variants and usage: Some systems emphasize purely semantic similarity of skim vectors; others blend skim signals with user interaction data. Skimrelated is commonly used in news aggregators, library search, video recommendations, and educational platforms to improve latency and scalability while maintaining relevance.

Limitations and evaluation: The quality of results depends on the quality of the skim representations, which may overlook nuanced connections. Evaluation typically uses offline metrics (precision, recall, ranking measures) and user studies or A/B tests to measure engagement.

See also: content-based filtering, semantic similarity, summarization, embeddings, information retrieval.