{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:IL2OGLHFR2D5UFBLVD7L3KWRN3","short_pith_number":"pith:IL2OGLHF","canonical_record":{"source":{"id":"2402.02855","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-02-05T10:16:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"63207bf912ceb99da691507de4e448c940354f2a3baec32afde897402299152f","abstract_canon_sha256":"1305a574f1a9f9c880cda7ec07bc878b1ae0a1a95db964ebec32c031a510207b"},"schema_version":"1.0"},"canonical_sha256":"42f4e32ce58e87da142ba8febdaad16ee61e64c4bd95b915ca136bd5e3f1cdf1","source":{"kind":"arxiv","id":"2402.02855","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02855","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02855v1","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02855","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"pith_short_12","alias_value":"IL2OGLHFR2D5","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"pith_short_16","alias_value":"IL2OGLHFR2D5UFBL","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"pith_short_8","alias_value":"IL2OGLHF","created_at":"2026-07-05T07:41:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:IL2OGLHFR2D5UFBLVD7L3KWRN3","target":"record","payload":{"canonical_record":{"source":{"id":"2402.02855","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-02-05T10:16:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"63207bf912ceb99da691507de4e448c940354f2a3baec32afde897402299152f","abstract_canon_sha256":"1305a574f1a9f9c880cda7ec07bc878b1ae0a1a95db964ebec32c031a510207b"},"schema_version":"1.0"},"canonical_sha256":"42f4e32ce58e87da142ba8febdaad16ee61e64c4bd95b915ca136bd5e3f1cdf1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:41:19.002182Z","signature_b64":"ERt8P7hvnv8LpJJyxpvd4cWvN+rulqWjBMs5RxRDRz+jBpdyVkSZJbLAictwsqLi1hxEEqdSpcm0M9gqlkbhCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42f4e32ce58e87da142ba8febdaad16ee61e64c4bd95b915ca136bd5e3f1cdf1","last_reissued_at":"2026-07-05T07:41:19.001828Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:41:19.001828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.02855","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:41:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XCvOp4vxrNymWRTBwqi4zQrPvmrMsDSNn6mads3pF1U+IxJ/hTVeb0APGojT8lQ8muIV6p3dqqEDkIXIEc3dCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T07:27:01.181368Z"},"content_sha256":"2ddbcfbc8bd4d27ed08e24e48cd5cc646eaaadc5b52718d168d8353d8d544fe8","schema_version":"1.0","event_id":"sha256:2ddbcfbc8bd4d27ed08e24e48cd5cc646eaaadc5b52718d168d8353d8d544fe8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:IL2OGLHFR2D5UFBLVD7L3KWRN3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Hui Xiong, Jiancan Wu, Shuyao Wang, Yongduo Sui, Zhi Zheng","submitted_at":"2024-02-05T10:16:20Z","abstract_excerpt":"In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment. This challenge is primarily twofold: reducing the model size while effectively learning user and item representations for efficient recommendations. Despite considerable advancements in model compression and architecture search, prevalent approaches face notable constraints. These include substantial additional computational costs from pre-training/re-training in model compression and an extensiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02855","kind":"arxiv","version":1},"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/2402.02855/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":"2026-07-05T07:41:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oi8jmSSBlPjxxdNudnE7yR4P5/JpkKyUdmR1VxkMgnSOPOApMm+ggT8sF0wyXgUOVbd2P/nM1QYfgqGiTHGGCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T07:27:01.182332Z"},"content_sha256":"30a0a85b046e86af2811cc8329f1c31488ab8cdffb04828fa0cb02554327d89f","schema_version":"1.0","event_id":"sha256:30a0a85b046e86af2811cc8329f1c31488ab8cdffb04828fa0cb02554327d89f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IL2OGLHFR2D5UFBLVD7L3KWRN3/bundle.json","state_url":"https://pith.science/pith/IL2OGLHFR2D5UFBLVD7L3KWRN3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IL2OGLHFR2D5UFBLVD7L3KWRN3/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":"2026-08-18T07:27:01Z","links":{"resolver":"https://pith.science/pith/IL2OGLHFR2D5UFBLVD7L3KWRN3","bundle":"https://pith.science/pith/IL2OGLHFR2D5UFBLVD7L3KWRN3/bundle.json","state":"https://pith.science/pith/IL2OGLHFR2D5UFBLVD7L3KWRN3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IL2OGLHFR2D5UFBLVD7L3KWRN3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:IL2OGLHFR2D5UFBLVD7L3KWRN3","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":"1305a574f1a9f9c880cda7ec07bc878b1ae0a1a95db964ebec32c031a510207b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-02-05T10:16:20Z","title_canon_sha256":"63207bf912ceb99da691507de4e448c940354f2a3baec32afde897402299152f"},"schema_version":"1.0","source":{"id":"2402.02855","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.02855","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"arxiv_version","alias_value":"2402.02855v1","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.02855","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"pith_short_12","alias_value":"IL2OGLHFR2D5","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"pith_short_16","alias_value":"IL2OGLHFR2D5UFBL","created_at":"2026-07-05T07:41:19Z"},{"alias_kind":"pith_short_8","alias_value":"IL2OGLHF","created_at":"2026-07-05T07:41:19Z"}],"graph_snapshots":[{"event_id":"sha256:30a0a85b046e86af2811cc8329f1c31488ab8cdffb04828fa0cb02554327d89f","target":"graph","created_at":"2026-07-05T07:41:19Z","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/2402.02855/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment. This challenge is primarily twofold: reducing the model size while effectively learning user and item representations for efficient recommendations. Despite considerable advancements in model compression and architecture search, prevalent approaches face notable constraints. These include substantial additional computational costs from pre-training/re-training in model compression and an extensiv","authors_text":"Hui Xiong, Jiancan Wu, Shuyao Wang, Yongduo Sui, Zhi Zheng","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-02-05T10:16:20Z","title":"Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.02855","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":"sha256:2ddbcfbc8bd4d27ed08e24e48cd5cc646eaaadc5b52718d168d8353d8d544fe8","target":"record","created_at":"2026-07-05T07:41:19Z","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":"1305a574f1a9f9c880cda7ec07bc878b1ae0a1a95db964ebec32c031a510207b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-02-05T10:16:20Z","title_canon_sha256":"63207bf912ceb99da691507de4e448c940354f2a3baec32afde897402299152f"},"schema_version":"1.0","source":{"id":"2402.02855","kind":"arxiv","version":1}},"canonical_sha256":"42f4e32ce58e87da142ba8febdaad16ee61e64c4bd95b915ca136bd5e3f1cdf1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"42f4e32ce58e87da142ba8febdaad16ee61e64c4bd95b915ca136bd5e3f1cdf1","first_computed_at":"2026-07-05T07:41:19.001828Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:41:19.001828Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ERt8P7hvnv8LpJJyxpvd4cWvN+rulqWjBMs5RxRDRz+jBpdyVkSZJbLAictwsqLi1hxEEqdSpcm0M9gqlkbhCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:41:19.002182Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.02855","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2ddbcfbc8bd4d27ed08e24e48cd5cc646eaaadc5b52718d168d8353d8d544fe8","sha256:30a0a85b046e86af2811cc8329f1c31488ab8cdffb04828fa0cb02554327d89f"],"state_sha256":"6d6a7483bf07d1a50fe6fd635e99d27d5e16049c5d945588c0221d24f48bf61c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BQLLiByP+PGhaLDdJkCRUaC2qrVEqRnYnB7iR17PjmCv6SWFJT/J+tVpky+E6oONlCKwuqJQRjalDPfFU1x6CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T07:27:01.189885Z","bundle_sha256":"c6ce003d2eb60c947fab164ecd21c01d6ec5e68c61affdf522db8e53b24bdd16"}}