problemsimilar

problemsimilar is a concept used in information retrieval and education platforms to quantify the similarity between programming contest problems. It seeks to identify related problems by comparing statements, constraints, inputs and outputs, and examples, facilitating recommendations, deduplication, and coherent problem sets.

Key aspects of problemsimilar include textual similarity of problem statements, structural similarity of input/output specifications and constraints, and topical similarity based on tags or topics. Multilingual statements and paraphrasing add complexity, as do variations in problem presentation across sources.

Common methods combine multiple signals: textual representations such as TF-IDF or neural embeddings; structural features from problem templates; metadata such as tags, difficulty, and sources; and exemplar-based signals from sample tests and editorial notes. Hybrid models often weight these signals to produce a similarity score.

Applications include automatic suggestion of related problems to learners, curated problem sets, detection of duplicate or near-duplicate problems, and clustering of large repositories to improve search and navigation. These capabilities support curriculum design, progress tracking, and more efficient problem management for platforms hosting large collections of challenges.

Evaluation typically relies on correlation with human judgments, using metrics such as cosine similarity or ranking measures, and may employ benchmark datasets where experts label relatedness. Challenges include language variation, ambiguous statements, differing constraints, and the dynamic growth of problem catalogs, which can complicate consistent similarity judgments. Related topics include information retrieval, text similarity, and recommender systems.