{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:E2LY437AAYFVFTCUORGQV6CTZV","short_pith_number":"pith:E2LY437A","canonical_record":{"source":{"id":"2502.03307","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-02-05T16:08:05Z","cross_cats_sorted":[],"title_canon_sha256":"30c22ab2d25e21fccd004018a871ed1a74e2da8dc3bdf397f995a274c6bb3a3c","abstract_canon_sha256":"1190375daa87ad0fdce288a3be16250224780348ae17bf65fae561cf99680f7b"},"schema_version":"1.0"},"canonical_sha256":"26978e6fe0060b52cc54744d0af853cd470b8b5d6aabc2b6ddd4cfdfbf92eb4d","source":{"kind":"arxiv","id":"2502.03307","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.03307","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"arxiv_version","alias_value":"2502.03307v4","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.03307","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"pith_short_12","alias_value":"E2LY437AAYFV","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"pith_short_16","alias_value":"E2LY437AAYFVFTCU","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"pith_short_8","alias_value":"E2LY437A","created_at":"2026-07-05T10:46:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:E2LY437AAYFVFTCUORGQV6CTZV","target":"record","payload":{"canonical_record":{"source":{"id":"2502.03307","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-02-05T16:08:05Z","cross_cats_sorted":[],"title_canon_sha256":"30c22ab2d25e21fccd004018a871ed1a74e2da8dc3bdf397f995a274c6bb3a3c","abstract_canon_sha256":"1190375daa87ad0fdce288a3be16250224780348ae17bf65fae561cf99680f7b"},"schema_version":"1.0"},"canonical_sha256":"26978e6fe0060b52cc54744d0af853cd470b8b5d6aabc2b6ddd4cfdfbf92eb4d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:46:40.907592Z","signature_b64":"xTLlK2PhKhKMBhuYnUlamwFrzV+3xjVCkzgw2WyNtlkUKUIDLoLdV38VbYH//F+abRXF6UmvdJfKo4pNZcLaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26978e6fe0060b52cc54744d0af853cd470b8b5d6aabc2b6ddd4cfdfbf92eb4d","last_reissued_at":"2026-07-05T10:46:40.907141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:46:40.907141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.03307","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:46:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nmbeDQAP6aKRDVa6TAJECMtlPqedNeDBBWXuhdYYv8BIQxLcI7AcjUzWaUod6Hv/ur31VINlTyR1NdqQdPcZAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:59:23.997651Z"},"content_sha256":"43770f62d8f82b25de05efe6f1e4014a44fa7194b26af8d73936145a941792e9","schema_version":"1.0","event_id":"sha256:43770f62d8f82b25de05efe6f1e4014a44fa7194b26af8d73936145a941792e9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:E2LY437AAYFVFTCUORGQV6CTZV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Intent Representation Learning with Large Language Model for Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Lei Sang, Yiwen Zhang, Yi Zhang, Yu Wang","submitted_at":"2025-02-05T16:08:05Z","abstract_excerpt":"Intent-based recommender systems have garnered significant attention for uncovering latent fine-grained preferences. Intents, as underlying factors of interactions, are crucial for improving recommendation interpretability. Most methods define intents as learnable parameters updated alongside interactions. However, existing frameworks often overlook textual information (e.g., user reviews, item descriptions), which is crucial for alleviating the sparsity of interaction intents. Exploring these multimodal intents, especially the inherent differences in representation spaces, poses two key chall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.03307","kind":"arxiv","version":4},"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/2502.03307/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:46:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zwqn9jq1icCMsDMOvK60ZRP00S1o5CevlNIigH44/efhkIImkLttFnep5xnrT6wQLZQ3/WfyY9xEPTZLyPKMDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:59:23.998249Z"},"content_sha256":"95df3a09f0d941270c5fb337bb977bb79b8f6eecc21511208333075b9f6142dd","schema_version":"1.0","event_id":"sha256:95df3a09f0d941270c5fb337bb977bb79b8f6eecc21511208333075b9f6142dd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E2LY437AAYFVFTCUORGQV6CTZV/bundle.json","state_url":"https://pith.science/pith/E2LY437AAYFVFTCUORGQV6CTZV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E2LY437AAYFVFTCUORGQV6CTZV/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":"2026-08-08T13:59:24Z","links":{"resolver":"https://pith.science/pith/E2LY437AAYFVFTCUORGQV6CTZV","bundle":"https://pith.science/pith/E2LY437AAYFVFTCUORGQV6CTZV/bundle.json","state":"https://pith.science/pith/E2LY437AAYFVFTCUORGQV6CTZV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E2LY437AAYFVFTCUORGQV6CTZV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:E2LY437AAYFVFTCUORGQV6CTZV","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":"1190375daa87ad0fdce288a3be16250224780348ae17bf65fae561cf99680f7b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-02-05T16:08:05Z","title_canon_sha256":"30c22ab2d25e21fccd004018a871ed1a74e2da8dc3bdf397f995a274c6bb3a3c"},"schema_version":"1.0","source":{"id":"2502.03307","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.03307","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"arxiv_version","alias_value":"2502.03307v4","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.03307","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"pith_short_12","alias_value":"E2LY437AAYFV","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"pith_short_16","alias_value":"E2LY437AAYFVFTCU","created_at":"2026-07-05T10:46:40Z"},{"alias_kind":"pith_short_8","alias_value":"E2LY437A","created_at":"2026-07-05T10:46:40Z"}],"graph_snapshots":[{"event_id":"sha256:95df3a09f0d941270c5fb337bb977bb79b8f6eecc21511208333075b9f6142dd","target":"graph","created_at":"2026-07-05T10:46:40Z","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/2502.03307/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Intent-based recommender systems have garnered significant attention for uncovering latent fine-grained preferences. Intents, as underlying factors of interactions, are crucial for improving recommendation interpretability. Most methods define intents as learnable parameters updated alongside interactions. However, existing frameworks often overlook textual information (e.g., user reviews, item descriptions), which is crucial for alleviating the sparsity of interaction intents. Exploring these multimodal intents, especially the inherent differences in representation spaces, poses two key chall","authors_text":"Lei Sang, Yiwen Zhang, Yi Zhang, Yu Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-02-05T16:08:05Z","title":"Intent Representation Learning with Large Language Model for Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.03307","kind":"arxiv","version":4},"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":"sha256:43770f62d8f82b25de05efe6f1e4014a44fa7194b26af8d73936145a941792e9","target":"record","created_at":"2026-07-05T10:46:40Z","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":"1190375daa87ad0fdce288a3be16250224780348ae17bf65fae561cf99680f7b","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-02-05T16:08:05Z","title_canon_sha256":"30c22ab2d25e21fccd004018a871ed1a74e2da8dc3bdf397f995a274c6bb3a3c"},"schema_version":"1.0","source":{"id":"2502.03307","kind":"arxiv","version":4}},"canonical_sha256":"26978e6fe0060b52cc54744d0af853cd470b8b5d6aabc2b6ddd4cfdfbf92eb4d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26978e6fe0060b52cc54744d0af853cd470b8b5d6aabc2b6ddd4cfdfbf92eb4d","first_computed_at":"2026-07-05T10:46:40.907141Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:46:40.907141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xTLlK2PhKhKMBhuYnUlamwFrzV+3xjVCkzgw2WyNtlkUKUIDLoLdV38VbYH//F+abRXF6UmvdJfKo4pNZcLaBg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:46:40.907592Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.03307","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43770f62d8f82b25de05efe6f1e4014a44fa7194b26af8d73936145a941792e9","sha256:95df3a09f0d941270c5fb337bb977bb79b8f6eecc21511208333075b9f6142dd"],"state_sha256":"77674d6ce3b937ce1d4b91a285e7241821111d10d85a3a8fab7af1b449c78792"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1aPXJl/V5wa53gSJxcK/TkHtQ4vtg7jsxgn2qcBk/Hy+dHoyjnoPL46TbnZ5puydDu7uZbhDdTrPBlA9Bea5Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T13:59:24.001933Z","bundle_sha256":"e97240cd46e88f3ad4ba8e11871d0acf30de0167e570a0417fd1689b75bf0785"}}