{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:Z4BZCY3QKJAIZTZBDHAYGHHE7E","short_pith_number":"pith:Z4BZCY3Q","canonical_record":{"source":{"id":"2010.09225","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-19T05:00:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c76efb5e48eb183b035496e3568f34ce078060f403a440f1f832107db39be88f","abstract_canon_sha256":"8c758fabd29cae104f713971f0bc428ef29c9eadb1d075f87a387d1fd20cd557"},"schema_version":"1.0"},"canonical_sha256":"cf0391637052408ccf2119c1831ce4f9279e31fa0166d836445cb8ca892cf49b","source":{"kind":"arxiv","id":"2010.09225","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.09225","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"arxiv_version","alias_value":"2010.09225v2","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.09225","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"pith_short_12","alias_value":"Z4BZCY3QKJAI","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"pith_short_16","alias_value":"Z4BZCY3QKJAIZTZB","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"pith_short_8","alias_value":"Z4BZCY3Q","created_at":"2026-07-05T02:28:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:Z4BZCY3QKJAIZTZBDHAYGHHE7E","target":"record","payload":{"canonical_record":{"source":{"id":"2010.09225","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-19T05:00:40Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c76efb5e48eb183b035496e3568f34ce078060f403a440f1f832107db39be88f","abstract_canon_sha256":"8c758fabd29cae104f713971f0bc428ef29c9eadb1d075f87a387d1fd20cd557"},"schema_version":"1.0"},"canonical_sha256":"cf0391637052408ccf2119c1831ce4f9279e31fa0166d836445cb8ca892cf49b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:28:18.380162Z","signature_b64":"reO0yC2aXg5sSc3Y7n3Da9E3JWx5Q0M048Tp3kzBntnH5y69DVYur5UhKRiwHFBB6UkWYKt7+adC4SuRRbZNAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf0391637052408ccf2119c1831ce4f9279e31fa0166d836445cb8ca892cf49b","last_reissued_at":"2026-07-05T02:28:18.379642Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:28:18.379642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.09225","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:28:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FKodM8EIK5iFA6nA+4nBOp5EqQ8skUZdcX7J/caJcHaUF0PA8Mz0jufQ/6QGqWkQgko55LVC8AlYXjeG6pdhDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T06:15:04.647220Z"},"content_sha256":"d8760fdfb3445479365f2a8eb14f0a89823acfa837121368b83a724f0553b98e","schema_version":"1.0","event_id":"sha256:d8760fdfb3445479365f2a8eb14f0a89823acfa837121368b83a724f0553b98e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:Z4BZCY3QKJAIZTZBDHAYGHHE7E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Factorization Machines with Regularization for Sparse Feature Interactions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Kyohei Atarashi, Masahito Kurihara, Satoshi Oyama","submitted_at":"2020-10-19T05:00:40Z","abstract_excerpt":"Factorization machines (FMs) are machine learning predictive models based on second-order feature interactions and FMs with sparse regularization are called sparse FMs. Such regularizations enable feature selection, which selects the most relevant features for accurate prediction, and therefore they can contribute to the improvement of the model accuracy and interpretability. However, because FMs use second-order feature interactions, the selection of features often causes the loss of many relevant feature interactions in the resultant models. In such cases, FMs with regularization specially d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.09225","kind":"arxiv","version":2},"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/2010.09225/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:28:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9qUE3kdIIrTMFYAa0OmaMo81v/j7VN5pOtvCPwX5ER8CjUDxm2Wi3bbNV3A/OKOiS9AypvLjxbnGMpUXyQwqDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T06:15:04.647729Z"},"content_sha256":"b4b58ae088502cb66f0d3a1d852d881bc2eb1dff68354f0bae0f38e02b8a6ceb","schema_version":"1.0","event_id":"sha256:b4b58ae088502cb66f0d3a1d852d881bc2eb1dff68354f0bae0f38e02b8a6ceb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z4BZCY3QKJAIZTZBDHAYGHHE7E/bundle.json","state_url":"https://pith.science/pith/Z4BZCY3QKJAIZTZBDHAYGHHE7E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z4BZCY3QKJAIZTZBDHAYGHHE7E/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-22T06:15:04Z","links":{"resolver":"https://pith.science/pith/Z4BZCY3QKJAIZTZBDHAYGHHE7E","bundle":"https://pith.science/pith/Z4BZCY3QKJAIZTZBDHAYGHHE7E/bundle.json","state":"https://pith.science/pith/Z4BZCY3QKJAIZTZBDHAYGHHE7E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z4BZCY3QKJAIZTZBDHAYGHHE7E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:Z4BZCY3QKJAIZTZBDHAYGHHE7E","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8c758fabd29cae104f713971f0bc428ef29c9eadb1d075f87a387d1fd20cd557","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-19T05:00:40Z","title_canon_sha256":"c76efb5e48eb183b035496e3568f34ce078060f403a440f1f832107db39be88f"},"schema_version":"1.0","source":{"id":"2010.09225","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.09225","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"arxiv_version","alias_value":"2010.09225v2","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.09225","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"pith_short_12","alias_value":"Z4BZCY3QKJAI","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"pith_short_16","alias_value":"Z4BZCY3QKJAIZTZB","created_at":"2026-07-05T02:28:18Z"},{"alias_kind":"pith_short_8","alias_value":"Z4BZCY3Q","created_at":"2026-07-05T02:28:18Z"}],"graph_snapshots":[{"event_id":"sha256:b4b58ae088502cb66f0d3a1d852d881bc2eb1dff68354f0bae0f38e02b8a6ceb","target":"graph","created_at":"2026-07-05T02:28:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2010.09225/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Factorization machines (FMs) are machine learning predictive models based on second-order feature interactions and FMs with sparse regularization are called sparse FMs. Such regularizations enable feature selection, which selects the most relevant features for accurate prediction, and therefore they can contribute to the improvement of the model accuracy and interpretability. However, because FMs use second-order feature interactions, the selection of features often causes the loss of many relevant feature interactions in the resultant models. In such cases, FMs with regularization specially d","authors_text":"Kyohei Atarashi, Masahito Kurihara, Satoshi Oyama","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-19T05:00:40Z","title":"Factorization Machines with Regularization for Sparse Feature Interactions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.09225","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d8760fdfb3445479365f2a8eb14f0a89823acfa837121368b83a724f0553b98e","target":"record","created_at":"2026-07-05T02:28:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8c758fabd29cae104f713971f0bc428ef29c9eadb1d075f87a387d1fd20cd557","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-10-19T05:00:40Z","title_canon_sha256":"c76efb5e48eb183b035496e3568f34ce078060f403a440f1f832107db39be88f"},"schema_version":"1.0","source":{"id":"2010.09225","kind":"arxiv","version":2}},"canonical_sha256":"cf0391637052408ccf2119c1831ce4f9279e31fa0166d836445cb8ca892cf49b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cf0391637052408ccf2119c1831ce4f9279e31fa0166d836445cb8ca892cf49b","first_computed_at":"2026-07-05T02:28:18.379642Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:28:18.379642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"reO0yC2aXg5sSc3Y7n3Da9E3JWx5Q0M048Tp3kzBntnH5y69DVYur5UhKRiwHFBB6UkWYKt7+adC4SuRRbZNAA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:28:18.380162Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.09225","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d8760fdfb3445479365f2a8eb14f0a89823acfa837121368b83a724f0553b98e","sha256:b4b58ae088502cb66f0d3a1d852d881bc2eb1dff68354f0bae0f38e02b8a6ceb"],"state_sha256":"85e44166f85b43de0bf870e7f41788470951df57490cf4d9563e064167e8de40"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rZsB4H/EeYmAHJyc+XBZpGm+LYeLrexBIkv5QBMp7CcyQVqNijzgsKfFdoxsG70EvJ2nugHRi6JkyDzwuQrrBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T06:15:04.651373Z","bundle_sha256":"08e57803edf7ced5274a93076cae2293aed493b4b02d0c3bb65f188a664c9a1e"}}