{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SSTJWGC2WBCN5JOO6TXVINPQF6","short_pith_number":"pith:SSTJWGC2","schema_version":"1.0","canonical_sha256":"94a69b185ab044dea5cef4ef5435f02f81af330a05930ac450c8607c7fb3aba9","source":{"kind":"arxiv","id":"2403.06372","version":3},"attestation_state":"computed","paper":{"title":"Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Enneng Yang, Guibing Guo, Jianzhe Zhao, Linying Jiang, Xingwei Wang, Yizhou Dang, Yuting Liu","submitted_at":"2024-03-11T01:50:41Z","abstract_excerpt":"Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences; 2) Batching-based training needs to ensure that the sequences in each batch have the same length. The special value \\emph{0} is usually used as the padding content, which does not contain the actual information and is ignored in the model calculations. This common-sense padding strategy leads us to a problem that has"},"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":"2403.06372","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-03-11T01:50:41Z","cross_cats_sorted":[],"title_canon_sha256":"af9428cada3857685f8bdd4c33672772ef15e2d6301347bea69c06169950d73d","abstract_canon_sha256":"86289a1f8797254856f94df94fa837b5d59a1f26c5d636838bb270baa0ca3f0a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:37:08.621576Z","signature_b64":"pTnt2K+P+GpXwq6amUW3Lpjf6Q/SieqaouiWRHggvSQ1WJa4yNmOCa0p2vWgg9RoF7OqUzqPvGI/BXBhRy9+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94a69b185ab044dea5cef4ef5435f02f81af330a05930ac450c8607c7fb3aba9","last_reissued_at":"2026-07-05T11:37:08.620863Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:37:08.620863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Repeated Padding+: Simple yet Effective Data Augmentation Plugin for Sequential Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Enneng Yang, Guibing Guo, Jianzhe Zhao, Linying Jiang, Xingwei Wang, Yizhou Dang, Yuting Liu","submitted_at":"2024-03-11T01:50:41Z","abstract_excerpt":"Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences; 2) Batching-based training needs to ensure that the sequences in each batch have the same length. The special value \\emph{0} is usually used as the padding content, which does not contain the actual information and is ignored in the model calculations. This common-sense padding strategy leads us to a problem that has"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.06372","kind":"arxiv","version":3},"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/2403.06372/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":"2403.06372","created_at":"2026-07-05T11:37:08.620948+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.06372v3","created_at":"2026-07-05T11:37:08.620948+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.06372","created_at":"2026-07-05T11:37:08.620948+00:00"},{"alias_kind":"pith_short_12","alias_value":"SSTJWGC2WBCN","created_at":"2026-07-05T11:37:08.620948+00:00"},{"alias_kind":"pith_short_16","alias_value":"SSTJWGC2WBCN5JOO","created_at":"2026-07-05T11:37:08.620948+00:00"},{"alias_kind":"pith_short_8","alias_value":"SSTJWGC2","created_at":"2026-07-05T11:37:08.620948+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.08300","citing_title":"Augmenting Sequential Recommendation with Balanced Relevance and Diversity","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6","json":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6.json","graph_json":"https://pith.science/api/pith-number/SSTJWGC2WBCN5JOO6TXVINPQF6/graph.json","events_json":"https://pith.science/api/pith-number/SSTJWGC2WBCN5JOO6TXVINPQF6/events.json","paper":"https://pith.science/paper/SSTJWGC2"},"agent_actions":{"view_html":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6","download_json":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6.json","view_paper":"https://pith.science/paper/SSTJWGC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.06372&json=true","fetch_graph":"https://pith.science/api/pith-number/SSTJWGC2WBCN5JOO6TXVINPQF6/graph.json","fetch_events":"https://pith.science/api/pith-number/SSTJWGC2WBCN5JOO6TXVINPQF6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6/action/storage_attestation","attest_author":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6/action/author_attestation","sign_citation":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6/action/citation_signature","submit_replication":"https://pith.science/pith/SSTJWGC2WBCN5JOO6TXVINPQF6/action/replication_record"}},"created_at":"2026-07-05T11:37:08.620948+00:00","updated_at":"2026-07-05T11:37:08.620948+00:00"}