{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:W4YVRXCOGHVKPVYDMEB2DEOPBH","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":"18b94eba1f4c0cbd32221105ce9871c147c0a2703a4852688020a942b3262d0a","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-04-08T17:06:39Z","title_canon_sha256":"ab042947c5c1c4f36da89ccb78b9054c408affa5d6ae214aea07887e3c6f9485"},"schema_version":"1.0","source":{"id":"2204.04179","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.04179","created_at":"2026-07-05T05:18:02Z"},{"alias_kind":"arxiv_version","alias_value":"2204.04179v2","created_at":"2026-07-05T05:18:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.04179","created_at":"2026-07-05T05:18:02Z"},{"alias_kind":"pith_short_12","alias_value":"W4YVRXCOGHVK","created_at":"2026-07-05T05:18:02Z"},{"alias_kind":"pith_short_16","alias_value":"W4YVRXCOGHVKPVYD","created_at":"2026-07-05T05:18:02Z"},{"alias_kind":"pith_short_8","alias_value":"W4YVRXCO","created_at":"2026-07-05T05:18:02Z"}],"graph_snapshots":[{"event_id":"sha256:5ecdd82fe353f70b8951518afdda015382ff29cd620620ca5ffc51b411701c87","target":"graph","created_at":"2026-07-05T05:18:02Z","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/2204.04179/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Content-based collaborative filtering (CCF) predicts user-item interactions based on both users' interaction history and items' content information. Recently, pre-trained language models (PLM) have been used to extract high-quality item encodings for CCF. However, it is resource-intensive to train a PLM-based CCF model in an end-to-end (E2E) manner, since optimization involves back-propagating through every content encoding within a given user interaction sequence. To tackle this issue, we propose GRAM (GRadient Accumulation for Multi-modality in CCF), which exploits the fact that a given item","authors_text":"Juneyoung Park, Kyu Seok Kim, Minsam Kim, Yoonseok Yang","cross_cats":["cs.AI","cs.IR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-04-08T17:06:39Z","title":"GRAM: Fast Fine-tuning of Pre-trained Language Models for Content-based Collaborative Filtering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.04179","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":"sha256:6a99b0d06818f6f8fdd0f4ffed6d02b0789cec083a960d4aa7d4798c59118b26","target":"record","created_at":"2026-07-05T05:18:02Z","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":"18b94eba1f4c0cbd32221105ce9871c147c0a2703a4852688020a942b3262d0a","cross_cats_sorted":["cs.AI","cs.IR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-04-08T17:06:39Z","title_canon_sha256":"ab042947c5c1c4f36da89ccb78b9054c408affa5d6ae214aea07887e3c6f9485"},"schema_version":"1.0","source":{"id":"2204.04179","kind":"arxiv","version":2}},"canonical_sha256":"b73158dc4e31eaa7d7036103a191cf09e1dda51d22f5c4846b29a25b22f778bf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b73158dc4e31eaa7d7036103a191cf09e1dda51d22f5c4846b29a25b22f778bf","first_computed_at":"2026-07-05T05:18:02.764120Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:18:02.764120Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kzTrO43FGVq7muZk5xu5rUlOTxWLNse0h+RASdvmYUxlYrj8aBCPB+/umi0+Ybrc6V1/LcD3uNbKcpZCafMxAA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:18:02.764641Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.04179","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6a99b0d06818f6f8fdd0f4ffed6d02b0789cec083a960d4aa7d4798c59118b26","sha256:5ecdd82fe353f70b8951518afdda015382ff29cd620620ca5ffc51b411701c87"],"state_sha256":"ecdd2f11dfec40b40209fe0105259262ef33a2fbbe6641afe3e964ac85ea900f"}