{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ZMIT6SHVNK7N6NIQBWCKLMTZSG","short_pith_number":"pith:ZMIT6SHV","canonical_record":{"source":{"id":"1904.04381","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-04-08T22:15:44Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"623a5978215971fd42cd8f88a46dce6d5a4c0fb1ec7b5ee7609f14cf6ac7063b","abstract_canon_sha256":"f83d159583f7656e4dc13536858ec13880a86cadfdfef62467fbc0b92d922698"},"schema_version":"1.0"},"canonical_sha256":"cb113f48f56abedf35100d84a5b279918f2e46e64a0770812407e2f5d58b38a2","source":{"kind":"arxiv","id":"1904.04381","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.04381","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"arxiv_version","alias_value":"1904.04381v2","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04381","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"pith_short_12","alias_value":"ZMIT6SHVNK7N","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZMIT6SHVNK7N6NIQ","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZMIT6SHV","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ZMIT6SHVNK7N6NIQBWCKLMTZSG","target":"record","payload":{"canonical_record":{"source":{"id":"1904.04381","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-04-08T22:15:44Z","cross_cats_sorted":["cs.IR","cs.LG"],"title_canon_sha256":"623a5978215971fd42cd8f88a46dce6d5a4c0fb1ec7b5ee7609f14cf6ac7063b","abstract_canon_sha256":"f83d159583f7656e4dc13536858ec13880a86cadfdfef62467fbc0b92d922698"},"schema_version":"1.0"},"canonical_sha256":"cb113f48f56abedf35100d84a5b279918f2e46e64a0770812407e2f5d58b38a2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:48.153279Z","signature_b64":"5AN4fPLLgot6ou1XSpzOxLJXbDHZp4RHHYNI21MYrRU6XS0+TtibH14i6Lecw8Ocf5BNqGGEBnjIKWPUbgUSBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb113f48f56abedf35100d84a5b279918f2e46e64a0770812407e2f5d58b38a2","last_reissued_at":"2026-05-17T23:48:48.152782Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:48.152782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.04381","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":"2026-05-17T23:48:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fDS1YEX/rX45fEAv7gnf+37iH2j9mTOKEQyhVZnDJs8FgcWcPihb0Y6iRCFv3U8XdMH/TI093hqJHGYfE+nSAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T10:38:39.343122Z"},"content_sha256":"91d456cda4122673472da8abf71798e913b4ec8c32866327e4f3439b0912c82f","schema_version":"1.0","event_id":"sha256:91d456cda4122673472da8abf71798e913b4ec8c32866327e4f3439b0912c82f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ZMIT6SHVNK7N6NIQBWCKLMTZSG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR","cs.LG"],"primary_cat":"cs.SI","authors_text":"Aditya Pal, Chuck Rosenberg, Jiaxuan You, Jure Leskovec, Pong Eksombatchai, Yichen Wang","submitted_at":"2019-04-08T22:15:44Z","abstract_excerpt":"Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches often use out-of-the-box sequence models which are limited by speed and memory consumption, are often infeasible for production environments, and usually do not incorporate cross-session information, which is crucial for effective recommendations. Here we propose Hierarchical Temporal Convolutional Networks (HierTCN), a hierarchical deep learning architecture that makes dynamic recommendations based on users' sequentia"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04381","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":""},"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":"2026-05-17T23:48:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GzTf1pCscdGkCpaaWwxKG55BjxdbEoE3hP2P7fRwb/rAvWJupfnCWvHsTlZiSP6TGcqX1k6yTs78Wmga1/XZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T10:38:39.343828Z"},"content_sha256":"f4d6a49962ada4cb6b17684edd21ad1a6365bcb69ce16aa9f9a336a78993764d","schema_version":"1.0","event_id":"sha256:f4d6a49962ada4cb6b17684edd21ad1a6365bcb69ce16aa9f9a336a78993764d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/bundle.json","state_url":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/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":"2026-05-22T10:38:39Z","links":{"resolver":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG","bundle":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/bundle.json","state":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ZMIT6SHVNK7N6NIQBWCKLMTZSG","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":"f83d159583f7656e4dc13536858ec13880a86cadfdfef62467fbc0b92d922698","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-04-08T22:15:44Z","title_canon_sha256":"623a5978215971fd42cd8f88a46dce6d5a4c0fb1ec7b5ee7609f14cf6ac7063b"},"schema_version":"1.0","source":{"id":"1904.04381","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.04381","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"arxiv_version","alias_value":"1904.04381v2","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04381","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"pith_short_12","alias_value":"ZMIT6SHVNK7N","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZMIT6SHVNK7N6NIQ","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZMIT6SHV","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:f4d6a49962ada4cb6b17684edd21ad1a6365bcb69ce16aa9f9a336a78993764d","target":"graph","created_at":"2026-05-17T23:48:48Z","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"},"paper":{"abstract_excerpt":"Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms. However, existing approaches often use out-of-the-box sequence models which are limited by speed and memory consumption, are often infeasible for production environments, and usually do not incorporate cross-session information, which is crucial for effective recommendations. Here we propose Hierarchical Temporal Convolutional Networks (HierTCN), a hierarchical deep learning architecture that makes dynamic recommendations based on users' sequentia","authors_text":"Aditya Pal, Chuck Rosenberg, Jiaxuan You, Jure Leskovec, Pong Eksombatchai, Yichen Wang","cross_cats":["cs.IR","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-04-08T22:15:44Z","title":"Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.04381","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":"sha256:91d456cda4122673472da8abf71798e913b4ec8c32866327e4f3439b0912c82f","target":"record","created_at":"2026-05-17T23:48:48Z","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":"f83d159583f7656e4dc13536858ec13880a86cadfdfef62467fbc0b92d922698","cross_cats_sorted":["cs.IR","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2019-04-08T22:15:44Z","title_canon_sha256":"623a5978215971fd42cd8f88a46dce6d5a4c0fb1ec7b5ee7609f14cf6ac7063b"},"schema_version":"1.0","source":{"id":"1904.04381","kind":"arxiv","version":2}},"canonical_sha256":"cb113f48f56abedf35100d84a5b279918f2e46e64a0770812407e2f5d58b38a2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cb113f48f56abedf35100d84a5b279918f2e46e64a0770812407e2f5d58b38a2","first_computed_at":"2026-05-17T23:48:48.152782Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:48.152782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5AN4fPLLgot6ou1XSpzOxLJXbDHZp4RHHYNI21MYrRU6XS0+TtibH14i6Lecw8Ocf5BNqGGEBnjIKWPUbgUSBg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:48.153279Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.04381","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:91d456cda4122673472da8abf71798e913b4ec8c32866327e4f3439b0912c82f","sha256:f4d6a49962ada4cb6b17684edd21ad1a6365bcb69ce16aa9f9a336a78993764d"],"state_sha256":"7417c52b49b7e4a0a8743e9bd654375484ef98dbcd978e4c99f6bc426718360a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s2FWT6GqrAb+k9vDFkuwvtnUku7zqJ1d2BY6fRlopPtN3ww1TW1TM98iY7Q6lZKzYFsfAd5UO/lMSap5I8NgAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T10:38:39.347341Z","bundle_sha256":"72ee95a8a9861fcfd5792d75e74b0dddd25e6c9a97edcbe01b53f930921fc8ab"}}