{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:T4JEOGSXXV6YLCTV6BTOPKL7ZA","short_pith_number":"pith:T4JEOGSX","schema_version":"1.0","canonical_sha256":"9f12471a57bd7d858a75f066e7a97fc83e6a871ed3dba6c3bf61162f68002cb7","source":{"kind":"arxiv","id":"2009.12316","version":1},"attestation_state":"computed","paper":{"title":"ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG"],"primary_cat":"cs.IR","authors_text":"Eunyee Koh, Fan Du, Joel Chan, Ryan A. Rossi, Sana Malik, Sungchul Kim, Tak Yeon Lee, Xin Qian","submitted_at":"2020-09-25T16:13:29Z","abstract_excerpt":"Visualization recommendation seeks to generate, score, and recommend to users useful visualizations automatically, and are fundamentally important for exploring and gaining insights into a new or existing dataset quickly. In this work, we propose the first end-to-end ML-based visualization recommendation system that takes as input a large corpus of datasets and visualizations, learns a model based on this data. Then, given a new unseen dataset from an arbitrary user, the model automatically generates visualizations for that new dataset, derive scores for the visualizations, and output a list o"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2009.12316","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-09-25T16:13:29Z","cross_cats_sorted":["cs.HC","cs.LG"],"title_canon_sha256":"3334e60f443cea8ed025e1beebba157f50113916f871b0369e9c24289dee8ea6","abstract_canon_sha256":"808e1d94f30d2db24cac49ebd347a650f134239d1e65d6da52ac9b92a88c637e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:38:01.371722Z","signature_b64":"7sF41qgunJrSl4Xr5+fWQUnCmAJ0kLmY1Oi04c79JkK9SQgx6BPOlfzVakVI7O9vlfRVtC1s0iBIhMjRBbNcCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f12471a57bd7d858a75f066e7a97fc83e6a871ed3dba6c3bf61162f68002cb7","last_reissued_at":"2026-07-05T01:38:01.371300Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:38:01.371300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ML-based Visualization Recommendation: Learning to Recommend Visualizations from Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","cs.LG"],"primary_cat":"cs.IR","authors_text":"Eunyee Koh, Fan Du, Joel Chan, Ryan A. Rossi, Sana Malik, Sungchul Kim, Tak Yeon Lee, Xin Qian","submitted_at":"2020-09-25T16:13:29Z","abstract_excerpt":"Visualization recommendation seeks to generate, score, and recommend to users useful visualizations automatically, and are fundamentally important for exploring and gaining insights into a new or existing dataset quickly. In this work, we propose the first end-to-end ML-based visualization recommendation system that takes as input a large corpus of datasets and visualizations, learns a model based on this data. Then, given a new unseen dataset from an arbitrary user, the model automatically generates visualizations for that new dataset, derive scores for the visualizations, and output a list o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.12316","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2009.12316/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2009.12316","created_at":"2026-07-05T01:38:01.371373+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.12316v1","created_at":"2026-07-05T01:38:01.371373+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.12316","created_at":"2026-07-05T01:38:01.371373+00:00"},{"alias_kind":"pith_short_12","alias_value":"T4JEOGSXXV6Y","created_at":"2026-07-05T01:38:01.371373+00:00"},{"alias_kind":"pith_short_16","alias_value":"T4JEOGSXXV6YLCTV","created_at":"2026-07-05T01:38:01.371373+00:00"},{"alias_kind":"pith_short_8","alias_value":"T4JEOGSX","created_at":"2026-07-05T01:38:01.371373+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA","json":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA.json","graph_json":"https://pith.science/api/pith-number/T4JEOGSXXV6YLCTV6BTOPKL7ZA/graph.json","events_json":"https://pith.science/api/pith-number/T4JEOGSXXV6YLCTV6BTOPKL7ZA/events.json","paper":"https://pith.science/paper/T4JEOGSX"},"agent_actions":{"view_html":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA","download_json":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA.json","view_paper":"https://pith.science/paper/T4JEOGSX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.12316&json=true","fetch_graph":"https://pith.science/api/pith-number/T4JEOGSXXV6YLCTV6BTOPKL7ZA/graph.json","fetch_events":"https://pith.science/api/pith-number/T4JEOGSXXV6YLCTV6BTOPKL7ZA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA/action/storage_attestation","attest_author":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA/action/author_attestation","sign_citation":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA/action/citation_signature","submit_replication":"https://pith.science/pith/T4JEOGSXXV6YLCTV6BTOPKL7ZA/action/replication_record"}},"created_at":"2026-07-05T01:38:01.371373+00:00","updated_at":"2026-07-05T01:38:01.371373+00:00"}