{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:WMSZYRSJVLOREQDRDZB2QA3MPP","short_pith_number":"pith:WMSZYRSJ","canonical_record":{"source":{"id":"2005.13258","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-27T09:50:46Z","cross_cats_sorted":[],"title_canon_sha256":"d9dde1fdd9c842993948516793a016edbdcf177d247815d40cf2a76c8eb16ad6","abstract_canon_sha256":"888973b9e5d3591bb19d2ff6cb4d52e4e6324e3cd9a17c27506cbf251bb8896e"},"schema_version":"1.0"},"canonical_sha256":"b3259c4649aadd1240711e43a8036c7bdcf6a40dc7c447f95cc7bbe32d4d6931","source":{"kind":"arxiv","id":"2005.13258","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.13258","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"arxiv_version","alias_value":"2005.13258v1","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.13258","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"pith_short_12","alias_value":"WMSZYRSJVLOR","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"pith_short_16","alias_value":"WMSZYRSJVLOREQDR","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"pith_short_8","alias_value":"WMSZYRSJ","created_at":"2026-07-05T02:06:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:WMSZYRSJVLOREQDRDZB2QA3MPP","target":"record","payload":{"canonical_record":{"source":{"id":"2005.13258","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-27T09:50:46Z","cross_cats_sorted":[],"title_canon_sha256":"d9dde1fdd9c842993948516793a016edbdcf177d247815d40cf2a76c8eb16ad6","abstract_canon_sha256":"888973b9e5d3591bb19d2ff6cb4d52e4e6324e3cd9a17c27506cbf251bb8896e"},"schema_version":"1.0"},"canonical_sha256":"b3259c4649aadd1240711e43a8036c7bdcf6a40dc7c447f95cc7bbe32d4d6931","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:06:50.250883Z","signature_b64":"e6xSrWz6u/ghaK6EAG2uFWcmNsHEDaZPP3oCzhztmBmuNOSk3o6QZZMh5zZHvHWtMy8VEVPHDvVnHSrK/1oSBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3259c4649aadd1240711e43a8036c7bdcf6a40dc7c447f95cc7bbe32d4d6931","last_reissued_at":"2026-07-05T02:06:50.250438Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:06:50.250438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.13258","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":"2026-07-05T02:06:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dBbQaDJwEwEpAEmQ+wSNWDzmqkeErjwK1dX7YGKt2/SwLV0nPUY8QfW/FR+sakOM+lSNRuk6VmISuiTNx1dVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:47:27.366253Z"},"content_sha256":"0ae2463914968a233453ce72005ae5c74bab66de48ed99178d40cb4418bc7b55","schema_version":"1.0","event_id":"sha256:0ae2463914968a233453ce72005ae5c74bab66de48ed99178d40cb4418bc7b55"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:WMSZYRSJVLOREQDRDZB2QA3MPP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"How to Retrain Recommender System? A Sequential Meta-Learning Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chenxu Wang, Fuli Feng, Meng Wang, Xiangnan He, Yang Zhang, Yan Li, Yongdong Zhang","submitted_at":"2020-05-27T09:50:46Z","abstract_excerpt":"Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research commun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.13258","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/2005.13258/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":"2026-07-05T02:06:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dcU+fTfmB0bFDgk6ECWmRzR4aklPYUUEAoG0jEcjLCe7RJJcTKQ+MV5qQ7qWH47pYiCrZ0ev8aoqfqY64jemDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:47:27.366635Z"},"content_sha256":"d942824a73f5ffd2ee87fc3c3add982b22c2e4f37d2166839ff5bf0040570c2a","schema_version":"1.0","event_id":"sha256:d942824a73f5ffd2ee87fc3c3add982b22c2e4f37d2166839ff5bf0040570c2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WMSZYRSJVLOREQDRDZB2QA3MPP/bundle.json","state_url":"https://pith.science/pith/WMSZYRSJVLOREQDRDZB2QA3MPP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WMSZYRSJVLOREQDRDZB2QA3MPP/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":"2026-07-07T07:47:27Z","links":{"resolver":"https://pith.science/pith/WMSZYRSJVLOREQDRDZB2QA3MPP","bundle":"https://pith.science/pith/WMSZYRSJVLOREQDRDZB2QA3MPP/bundle.json","state":"https://pith.science/pith/WMSZYRSJVLOREQDRDZB2QA3MPP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WMSZYRSJVLOREQDRDZB2QA3MPP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:WMSZYRSJVLOREQDRDZB2QA3MPP","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":"888973b9e5d3591bb19d2ff6cb4d52e4e6324e3cd9a17c27506cbf251bb8896e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-27T09:50:46Z","title_canon_sha256":"d9dde1fdd9c842993948516793a016edbdcf177d247815d40cf2a76c8eb16ad6"},"schema_version":"1.0","source":{"id":"2005.13258","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.13258","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"arxiv_version","alias_value":"2005.13258v1","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.13258","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"pith_short_12","alias_value":"WMSZYRSJVLOR","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"pith_short_16","alias_value":"WMSZYRSJVLOREQDR","created_at":"2026-07-05T02:06:50Z"},{"alias_kind":"pith_short_8","alias_value":"WMSZYRSJ","created_at":"2026-07-05T02:06:50Z"}],"graph_snapshots":[{"event_id":"sha256:d942824a73f5ffd2ee87fc3c3add982b22c2e4f37d2166839ff5bf0040570c2a","target":"graph","created_at":"2026-07-05T02:06:50Z","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/2005.13258/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research commun","authors_text":"Chenxu Wang, Fuli Feng, Meng Wang, Xiangnan He, Yang Zhang, Yan Li, Yongdong Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-27T09:50:46Z","title":"How to Retrain Recommender System? A Sequential Meta-Learning Method"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.13258","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":"sha256:0ae2463914968a233453ce72005ae5c74bab66de48ed99178d40cb4418bc7b55","target":"record","created_at":"2026-07-05T02:06:50Z","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":"888973b9e5d3591bb19d2ff6cb4d52e4e6324e3cd9a17c27506cbf251bb8896e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2020-05-27T09:50:46Z","title_canon_sha256":"d9dde1fdd9c842993948516793a016edbdcf177d247815d40cf2a76c8eb16ad6"},"schema_version":"1.0","source":{"id":"2005.13258","kind":"arxiv","version":1}},"canonical_sha256":"b3259c4649aadd1240711e43a8036c7bdcf6a40dc7c447f95cc7bbe32d4d6931","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3259c4649aadd1240711e43a8036c7bdcf6a40dc7c447f95cc7bbe32d4d6931","first_computed_at":"2026-07-05T02:06:50.250438Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:06:50.250438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e6xSrWz6u/ghaK6EAG2uFWcmNsHEDaZPP3oCzhztmBmuNOSk3o6QZZMh5zZHvHWtMy8VEVPHDvVnHSrK/1oSBA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:06:50.250883Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.13258","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0ae2463914968a233453ce72005ae5c74bab66de48ed99178d40cb4418bc7b55","sha256:d942824a73f5ffd2ee87fc3c3add982b22c2e4f37d2166839ff5bf0040570c2a"],"state_sha256":"49255bb95cc9d1f4c017c97b922e5e0338ffa569eaaf0335505174dcaf02ee12"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FsLBU3kymlvGqC4eBi2qxTLFv2nBuZdT7xt/tA4KthvH2ZJ3aHC8NHM/QpuK8ygwjBOOea07CpHRNFKkmH/9AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:47:27.368625Z","bundle_sha256":"ea0a741dfb8d82e80bd0889a43840c96e370ae513e0bc08da79d9414748d5b2a"}}