{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ZMIT6SHVNK7N6NIQBWCKLMTZSG","short_pith_number":"pith:ZMIT6SHV","schema_version":"1.0","canonical_sha256":"cb113f48f56abedf35100d84a5b279918f2e46e64a0770812407e2f5d58b38a2","source":{"kind":"arxiv","id":"1904.04381","version":2},"attestation_state":"computed","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"},"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":"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1904.04381","created_at":"2026-05-17T23:48:48.152861+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.04381v2","created_at":"2026-05-17T23:48:48.152861+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.04381","created_at":"2026-05-17T23:48:48.152861+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZMIT6SHVNK7N","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZMIT6SHVNK7N6NIQ","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZMIT6SHV","created_at":"2026-05-18T12:33:33.725879+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/ZMIT6SHVNK7N6NIQBWCKLMTZSG","json":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG.json","graph_json":"https://pith.science/api/pith-number/ZMIT6SHVNK7N6NIQBWCKLMTZSG/graph.json","events_json":"https://pith.science/api/pith-number/ZMIT6SHVNK7N6NIQBWCKLMTZSG/events.json","paper":"https://pith.science/paper/ZMIT6SHV"},"agent_actions":{"view_html":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG","download_json":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG.json","view_paper":"https://pith.science/paper/ZMIT6SHV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.04381&json=true","fetch_graph":"https://pith.science/api/pith-number/ZMIT6SHVNK7N6NIQBWCKLMTZSG/graph.json","fetch_events":"https://pith.science/api/pith-number/ZMIT6SHVNK7N6NIQBWCKLMTZSG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/action/storage_attestation","attest_author":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/action/author_attestation","sign_citation":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/action/citation_signature","submit_replication":"https://pith.science/pith/ZMIT6SHVNK7N6NIQBWCKLMTZSG/action/replication_record"}},"created_at":"2026-05-17T23:48:48.152861+00:00","updated_at":"2026-05-17T23:48:48.152861+00:00"}