{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-movielens-1m","slug":"movielens-1m","name":"MovieLens-1M","type":"dataset","url":"https://grouplens.org/datasets/movielens/1m/","page_url":"https://unfragile.ai/movielens-1m","categories":["model-training"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-movielens-1m__cap_0","uri":"capability://data.processing.analysis.collaborative.filtering.model.training.with.user.item.interaction.matrix","name":"collaborative-filtering-model-training-with-user-item-interaction-matrix","description":"Enables training of collaborative filtering recommendation algorithms by providing a pre-structured user-item interaction matrix with 1,000,000 explicit ratings across 6,000 users and 4,000 movies. The dataset is organized as flat files (likely CSV/TSV format) containing user IDs, movie IDs, rating values, and timestamps, allowing direct ingestion into matrix factorization frameworks (SVD, NMF) and neighborhood-based CF algorithms without preprocessing. The 4.2% sparsity density is typical for rating matrices and sufficient for training algorithms that handle sparse interactions.","intents":["Train a matrix factorization model (SVD, NMF) for movie recommendations without building a custom dataset pipeline","Benchmark collaborative filtering algorithms against a stable, well-established baseline","Develop and validate user-based or item-based nearest-neighbor recommendation logic","Compare algorithm performance across multiple CF approaches using identical training data"],"best_for":["ML researchers and practitioners developing recommendation algorithms","Computer science students learning collaborative filtering fundamentals","Teams establishing baseline recommendation system performance before custom data collection"],"limitations":["Sparsity of 4.2% (1M ratings / 24M possible user-item pairs) limits certain algorithms' ability to find dense neighborhoods","No implicit feedback signals (clicks, views, dwell time) — only explicit 1-5 star ratings, which may not capture true user preference intensity","Dataset is 20+ years old (released 2003); user preferences and movie catalog no longer reflect current entertainment landscape, limiting real-world applicability","No mechanism for cold-start scenarios — new users or movies not in the training set cannot be handled without additional techniques"],"requires":["Python 3.6+ with pandas or numpy for data loading","ML framework supporting matrix operations (scikit-learn, TensorFlow, PyTorch, or Spark MLlib)","Minimum 500 MB RAM for in-memory loading of full dataset","CSV/TSV parsing capability (standard in all data science libraries)"],"input_types":["flat-file dataset (CSV or tab-delimited)","user ID (integer)","movie ID (integer)","rating value (integer, scale unknown but likely 1-5)","timestamp (Unix epoch or similar)"],"output_types":["user-item interaction matrix (sparse or dense)","user embeddings (latent factor vectors)","item embeddings (latent factor vectors)","predicted ratings (float values)","recommendation rankings (ordered movie lists per user)"],"categories":["data-processing-analysis","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-movielens-1m__cap_1","uri":"capability://data.processing.analysis.temporal.sequence.analysis.for.rating.behavior.modeling","name":"temporal-sequence-analysis-for-rating-behavior-modeling","description":"Enables time-series analysis of user rating behavior by including Unix timestamps for each rating event, allowing researchers to study how user preferences evolve, detect temporal patterns in rating activity, and develop time-aware recommendation algorithms. The dataset structure preserves the chronological order of ratings, supporting sequence-based models (RNNs, Transformers) and temporal collaborative filtering approaches that weight recent ratings more heavily than historical ones.","intents":["Analyze how individual user preferences change over time and identify preference drift patterns","Train time-aware recommendation models that weight recent user behavior more heavily","Study temporal dynamics of movie popularity and rating trends across the 2003 timeframe","Develop session-based or sequential recommendation algorithms using rating timestamps"],"best_for":["Researchers studying temporal recommendation systems and preference evolution","Teams building time-aware collaborative filtering models","Practitioners analyzing user engagement patterns and rating behavior trends"],"limitations":["Temporal data spans only the 2003 release period — insufficient for studying long-term preference drift or seasonal patterns","No session information — timestamps exist but no explicit session boundaries, requiring heuristic-based session detection","Timestamp granularity unknown — may be at day/hour/minute level, affecting ability to study rapid rating sequences","No contextual temporal features (day of week, holidays, movie release dates) — requires external data enrichment"],"requires":["Python 3.6+ with pandas for timestamp parsing and time-series operations","Unix timestamp parsing capability or datetime library","Optional: time-series ML frameworks (statsmodels, Prophet) for trend analysis"],"input_types":["user ID (integer)","movie ID (integer)","rating value (integer)","timestamp (Unix epoch or datetime format)"],"output_types":["time-indexed rating sequences per user","temporal embeddings (time-aware latent factors)","preference drift metrics (cosine similarity between user vectors at different time periods)","temporal popularity curves (rating counts over time per movie)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-movielens-1m__cap_2","uri":"capability://data.processing.analysis.demographic.based.user.segmentation.and.filtering","name":"demographic-based-user-segmentation-and-filtering","description":"Enables user segmentation and demographic-based recommendation filtering by including user demographic attributes (age, gender, occupation, zip code) alongside rating data. This allows researchers to build demographic-aware recommendation systems, study preference differences across demographic groups, and develop fairness-aware algorithms that account for demographic representation. The dataset structure links demographic attributes to user IDs, enabling stratified analysis and demographic-specific model training.","intents":["Build recommendation models that account for demographic differences in user preferences","Analyze whether recommendation algorithms exhibit demographic bias or fairness issues","Develop demographic-filtered recommendations (e.g., 'recommendations for users aged 18-25')","Study how movie preferences vary across age groups, genders, and occupations"],"best_for":["Researchers studying fairness and bias in recommendation systems","Teams building demographic-aware or demographic-filtered recommendation engines","Practitioners analyzing preference heterogeneity across user segments"],"limitations":["Demographic attributes are static and from 2003 — do not reflect current user populations or diversity","Demographic data completeness unknown — may have missing values or sparse representation in certain categories","No sensitive attribute protection — demographic data is included without anonymization guidance, raising privacy concerns","Limited demographic dimensions — likely includes only age, gender, occupation, zip code; no ethnicity, income, education, or other attributes"],"requires":["Python 3.6+ with pandas for demographic data filtering and grouping","Categorical data handling (one-hot encoding, label encoding) for ML frameworks","Optional: fairness libraries (AI Fairness 360, Fairlearn) for bias analysis"],"input_types":["user ID (integer)","age (integer, range unknown)","gender (categorical: likely 'M' or 'F')","occupation (categorical, specific values unknown)","zip code (string or integer)"],"output_types":["user demographic segments (stratified groups)","demographic-specific recommendation models (separate models per age group, gender, etc.)","fairness metrics (demographic parity, equalized odds, disparate impact)","preference distribution by demographic (aggregated statistics)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-movielens-1m__cap_3","uri":"capability://data.processing.analysis.movie.metadata.based.content.filtering.and.genre.analysis","name":"movie-metadata-based-content-filtering-and-genre-analysis","description":"Enables content-based and hybrid recommendation approaches by providing movie metadata including titles and genre classifications for 4,000 movies. This allows researchers to build content-based recommendation systems that match user preferences to movie attributes, develop hybrid algorithms combining collaborative and content-based filtering, and analyze genre-level preference patterns. The dataset structure links movie IDs to titles and genres, enabling feature-based similarity calculations and genre-aware recommendation logic.","intents":["Build content-based recommendation models that match user preferences to movie genres and titles","Develop hybrid recommendation systems combining collaborative filtering with content-based features","Analyze user preference patterns at the genre level (e.g., 'users who rate Action movies highly')","Create genre-specific recommendation models or genre-aware ranking systems"],"best_for":["Researchers developing hybrid recommendation systems combining CF and content-based approaches","Teams building genre-aware or content-filtered recommendation engines","Practitioners studying genre-level preference patterns and cross-genre recommendation"],"limitations":["Movie metadata is minimal — only titles and genres; no plot summaries, cast, crew, release date, or other content features","Genre representation unknown — number of genres per movie, genre taxonomy, and coverage unclear","No rich content features — cannot leverage modern NLP-based content analysis (embeddings from plot text, reviews, etc.)","Content features are static — movie metadata does not change over time, limiting ability to study how content perception evolves"],"requires":["Python 3.6+ with pandas for movie metadata loading and genre parsing","Optional: scikit-learn for TF-IDF or other content-based similarity calculations","Optional: NLP libraries if enriching with external movie metadata"],"input_types":["movie ID (integer)","movie title (string)","genres (categorical, likely pipe-delimited or comma-separated list)"],"output_types":["content-based user profiles (genre preference vectors)","movie similarity matrices (based on genre overlap)","genre-level recommendation rankings","hybrid recommendation scores (weighted combination of CF and content-based scores)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-movielens-1m__cap_4","uri":"capability://data.processing.analysis.benchmark.dataset.for.algorithm.comparison.and.reproducibility","name":"benchmark-dataset-for-algorithm-comparison-and-reproducibility","description":"Provides a stable, fixed-size benchmark dataset enabling reproducible algorithm comparisons and performance validation across recommendation systems research. The dataset's 20-year history in academic literature means thousands of published results use it as a baseline, allowing new algorithms to be positioned against established performance metrics. The flat-file distribution model and well-documented structure (via GroupLens documentation) enable consistent train/test splits and cross-validation workflows across different research teams and implementations.","intents":["Establish baseline recommendation algorithm performance for comparison with new approaches","Reproduce published results from recommendation systems papers using MovieLens-1M","Validate algorithm implementations against known performance metrics from literature","Compare multiple recommendation algorithms on identical data to isolate algorithmic differences"],"best_for":["Academic researchers publishing recommendation systems papers","ML practitioners validating algorithm implementations against published baselines","Teams establishing performance benchmarks before deploying custom recommendation systems"],"limitations":["Dataset is 20+ years old — performance metrics from 2003-era algorithms may not translate to modern deep learning approaches or current user preferences","Scale is small by modern standards — 1M ratings is orders of magnitude smaller than Netflix (100M+) or YouTube (billions), limiting insights about scalability","No standard train/test split defined — different papers may use different splits, reducing comparability across published results","Limited to movie domain — cannot generalize benchmark insights to other recommendation domains (music, books, products, etc.)"],"requires":["Python 3.6+ with pandas and scikit-learn for standard train/test split and cross-validation","Optional: published papers using MovieLens-1M for reference implementations and expected performance metrics","Reproducibility tools (random seed management, version control) for consistent results"],"input_types":["full dataset (1M ratings across 6K users and 4K movies)","train/test split ratio (typically 80/20 or 90/10, but not standardized)"],"output_types":["algorithm performance metrics (RMSE, MAE, NDCG, Precision@K, Recall@K)","comparison tables (algorithm A vs B vs C on identical data)","reproducibility artifacts (trained models, predictions, evaluation logs)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-movielens-1m__cap_5","uri":"capability://data.processing.analysis.educational.dataset.for.recommendation.systems.learning","name":"educational-dataset-for-recommendation-systems-learning","description":"Serves as an accessible, well-documented learning resource for students and practitioners new to recommendation systems by providing a manageable dataset size (1M ratings, 6K users, 4K movies) that fits in memory and can be processed on commodity hardware without distributed computing infrastructure. The dataset's long history in academic literature means extensive tutorials, reference implementations, and educational materials are available online, reducing the learning curve for understanding collaborative filtering, content-based filtering, and hybrid approaches.","intents":["Learn collaborative filtering fundamentals using a real-world dataset without building custom data pipelines","Understand recommendation system evaluation metrics (RMSE, Precision@K, Recall@K) on a standard benchmark","Implement and compare multiple recommendation algorithms (SVD, KNN, NMF) on identical data","Study the impact of sparsity, cold-start problems, and demographic bias on recommendation quality"],"best_for":["Computer science students learning recommendation systems in academic courses","Self-taught practitioners building their first recommendation system","Teams onboarding new engineers to recommendation systems concepts"],"limitations":["Dataset is small enough to fit in memory but large enough to expose real-world challenges (sparsity, cold-start), creating a 'Goldilocks' zone that may not prepare for production-scale systems","20-year-old data and outdated user preferences may confuse learners about what modern recommendation systems should optimize for","Limited documentation in the dataset itself — README.txt referenced but not provided, requiring external sources for format clarification","No built-in train/test split or cross-validation guidance — learners must design their own evaluation methodology"],"requires":["Python 3.6+ with pandas, numpy, and scikit-learn","Jupyter Notebook or similar interactive environment for exploratory analysis","Basic understanding of linear algebra and statistics","Access to online tutorials and reference implementations (widely available)"],"input_types":["flat-file dataset (CSV or TSV format)","user ID, movie ID, rating, timestamp"],"output_types":["exploratory data analysis visualizations (rating distributions, user activity, genre popularity)","trained recommendation models","evaluation metrics and performance comparisons","recommendation lists for sample users"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":21,"verified":false,"data_access_risk":"high","permissions":["Python 3.6+ with pandas or numpy for data loading","ML framework supporting matrix operations (scikit-learn, TensorFlow, PyTorch, or Spark MLlib)","Minimum 500 MB RAM for in-memory loading of full dataset","CSV/TSV parsing capability (standard in all data science libraries)","Python 3.6+ with pandas for timestamp parsing and time-series operations","Unix timestamp parsing capability or datetime library","Optional: time-series ML frameworks (statsmodels, Prophet) for trend analysis","Python 3.6+ with pandas for demographic data filtering and grouping","Categorical data handling (one-hot encoding, label encoding) for ML frameworks","Optional: fairness libraries (AI Fairness 360, Fairlearn) for bias analysis"],"failure_modes":["Sparsity of 4.2% (1M ratings / 24M possible user-item pairs) limits certain algorithms' ability to find dense neighborhoods","No implicit feedback signals (clicks, views, dwell time) — only explicit 1-5 star ratings, which may not capture true user preference intensity","Dataset is 20+ years old (released 2003); user preferences and movie catalog no longer reflect current entertainment landscape, limiting real-world applicability","No mechanism for cold-start scenarios — new users or movies not in the training set cannot be handled without additional techniques","Temporal data spans only the 2003 release period — insufficient for studying long-term preference drift or seasonal patterns","No session information — timestamps exist but no explicit session boundaries, requiring heuristic-based session detection","Timestamp granularity unknown — may be at day/hour/minute level, affecting ability to study rapid rating sequences","No contextual temporal features (day of week, holidays, movie release dates) — requires external data enrichment","Demographic attributes are static and from 2003 — do not reflect current user populations or diversity","Demographic data completeness unknown — may have missing values or sparse representation in certain categories","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.22,"ecosystem":0.25,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.25,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.578Z","last_scraped_at":"2026-05-03T14:00:10.321Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=movielens-1m","compare_url":"https://unfragile.ai/compare?artifact=movielens-1m"}},"signature":"rip+aCcB5wfdA4mqEcddOHSayFbrCV9NEMXuNqhNPBgvAVyOfyrgJP4oKmEazKSQaGj7yjVq7OitkZz4lVSYBQ==","signedAt":"2026-06-19T17:00:42.132Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/movielens-1m","artifact":"https://unfragile.ai/movielens-1m","verify":"https://unfragile.ai/api/v1/verify?slug=movielens-1m","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}