{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NKKPQQ6GYZBCD4BNFQ3B24GZMS","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":"4f869115c016c11d305946c548d1d871a5a098cfebb2e6867bf7fa993650b461","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-04-09T07:32:55Z","title_canon_sha256":"a3fde7b752fcf4badf43e540c6b354481a6ba37eb60255be6a09ce59d51a6f4d"},"schema_version":"1.0","source":{"id":"2404.06078","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.06078","created_at":"2026-07-05T08:06:03Z"},{"alias_kind":"arxiv_version","alias_value":"2404.06078v1","created_at":"2026-07-05T08:06:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.06078","created_at":"2026-07-05T08:06:03Z"},{"alias_kind":"pith_short_12","alias_value":"NKKPQQ6GYZBC","created_at":"2026-07-05T08:06:03Z"},{"alias_kind":"pith_short_16","alias_value":"NKKPQQ6GYZBCD4BN","created_at":"2026-07-05T08:06:03Z"},{"alias_kind":"pith_short_8","alias_value":"NKKPQQ6G","created_at":"2026-07-05T08:06:03Z"}],"graph_snapshots":[{"event_id":"sha256:a1f40268b08c30acca05755cce46c067c2daee0827f3adb105dc5face9907604","target":"graph","created_at":"2026-07-05T08:06:03Z","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/2404.06078/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traditional recommender systems heavily rely on ID features, which often encounter challenges related to cold-start and generalization. Modeling pre-extracted content features can mitigate these issues, but is still a suboptimal solution due to the discrepancies between training tasks and model parameters. End-to-end training presents a promising solution for these problems, yet most of the existing works mainly focus on retrieval models, leaving the multimodal techniques under-utilized. In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training ","authors_text":"Di Zhang, Erpeng Xue, Guorui Zhou, Han Li, Jinkai Yu, Lu Xu, Na Mou, Shen Jiang, Xiuqi Deng, Xiyao Li, Yang Song, Zhaojie Liu, Zhongyuan Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-04-09T07:32:55Z","title":"End-to-end training of Multimodal Model and ranking Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.06078","kind":"arxiv","version":1},"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":"sha256:1c5a46054cdbfac76b41b35c58e90d934bf02303508df96b3d60529f823c45f5","target":"record","created_at":"2026-07-05T08:06:03Z","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":"4f869115c016c11d305946c548d1d871a5a098cfebb2e6867bf7fa993650b461","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-04-09T07:32:55Z","title_canon_sha256":"a3fde7b752fcf4badf43e540c6b354481a6ba37eb60255be6a09ce59d51a6f4d"},"schema_version":"1.0","source":{"id":"2404.06078","kind":"arxiv","version":1}},"canonical_sha256":"6a94f843c6c64221f02d2c361d70d964bff10e298227d3ac7c2816ec7b06ea6b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6a94f843c6c64221f02d2c361d70d964bff10e298227d3ac7c2816ec7b06ea6b","first_computed_at":"2026-07-05T08:06:03.396655Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:06:03.396655Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DOsGIMOvXuk7wOPFQb4Qi5E3vtqAk7Dk+SpHPY9J53rPddNz5u5OVZ5siPWq6zHkBFQ9yAMimMTh8UJ7EoPBDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:06:03.397185Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.06078","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c5a46054cdbfac76b41b35c58e90d934bf02303508df96b3d60529f823c45f5","sha256:a1f40268b08c30acca05755cce46c067c2daee0827f3adb105dc5face9907604"],"state_sha256":"071f635fcf32d8af9da47e53516cf42a218bc71b2b89c806e8c9d28b7d008a23"}