{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:IG6KOUTJSHKGBNHBYINEWJCN7Q","short_pith_number":"pith:IG6KOUTJ","schema_version":"1.0","canonical_sha256":"41bca7526991d460b4e1c21a4b244dfc311695283d9890b2a6a17e2b6faf7091","source":{"kind":"arxiv","id":"2112.07191","version":1},"attestation_state":"computed","paper":{"title":"An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Chaozhuo Li, Jiliang Tang, Lei Chen, Mingzheng Li, Philip S. Yu, Xing Xie, Yiqi Wang, Zheng Liu","submitted_at":"2021-12-14T06:53:13Z","abstract_excerpt":"Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have obtained very appealing performance. However, most GNN-based recommendation methods suffer from the problem of data sparsity in practice. Meanwhile, pre-training techniques have achieved great success in mitigating data sparsity in various domains such as natural language processing (NLP) and computer vision (CV). Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations. However, pre-training GNNs for recommendations face unique challenges. For example, use"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2112.07191","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2021-12-14T06:53:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7ec69f20a5dce325fe9383d1bf2b6461236e6f058824915d7c4cbe0546777cc6","abstract_canon_sha256":"279c2ff1c746c9b4c4892135d8ffd1cad2c683d1e100c1a6d9d10a1e6d7bbdc4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:40:55.650166Z","signature_b64":"F6ObsOsCVaSFMiXLtzCitSWnXWnk805me89dKtldi/rM1Z6m6QgSb2Q6dgB9kaJmnIQIP4/cRTpSA8vtEtxtCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"41bca7526991d460b4e1c21a4b244dfc311695283d9890b2a6a17e2b6faf7091","last_reissued_at":"2026-07-05T03:40:55.649739Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:40:55.649739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Chaozhuo Li, Jiliang Tang, Lei Chen, Mingzheng Li, Philip S. Yu, Xing Xie, Yiqi Wang, Zheng Liu","submitted_at":"2021-12-14T06:53:13Z","abstract_excerpt":"Graph neural networks (GNNs) have been widely applied in the recommendation tasks and have obtained very appealing performance. However, most GNN-based recommendation methods suffer from the problem of data sparsity in practice. Meanwhile, pre-training techniques have achieved great success in mitigating data sparsity in various domains such as natural language processing (NLP) and computer vision (CV). Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations. However, pre-training GNNs for recommendations face unique challenges. For example, use"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.07191","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/2112.07191/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2112.07191","created_at":"2026-07-05T03:40:55.649796+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.07191v1","created_at":"2026-07-05T03:40:55.649796+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.07191","created_at":"2026-07-05T03:40:55.649796+00:00"},{"alias_kind":"pith_short_12","alias_value":"IG6KOUTJSHKG","created_at":"2026-07-05T03:40:55.649796+00:00"},{"alias_kind":"pith_short_16","alias_value":"IG6KOUTJSHKGBNHB","created_at":"2026-07-05T03:40:55.649796+00:00"},{"alias_kind":"pith_short_8","alias_value":"IG6KOUTJ","created_at":"2026-07-05T03:40:55.649796+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q","json":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q.json","graph_json":"https://pith.science/api/pith-number/IG6KOUTJSHKGBNHBYINEWJCN7Q/graph.json","events_json":"https://pith.science/api/pith-number/IG6KOUTJSHKGBNHBYINEWJCN7Q/events.json","paper":"https://pith.science/paper/IG6KOUTJ"},"agent_actions":{"view_html":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q","download_json":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q.json","view_paper":"https://pith.science/paper/IG6KOUTJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.07191&json=true","fetch_graph":"https://pith.science/api/pith-number/IG6KOUTJSHKGBNHBYINEWJCN7Q/graph.json","fetch_events":"https://pith.science/api/pith-number/IG6KOUTJSHKGBNHBYINEWJCN7Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q/action/storage_attestation","attest_author":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q/action/author_attestation","sign_citation":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q/action/citation_signature","submit_replication":"https://pith.science/pith/IG6KOUTJSHKGBNHBYINEWJCN7Q/action/replication_record"}},"created_at":"2026-07-05T03:40:55.649796+00:00","updated_at":"2026-07-05T03:40:55.649796+00:00"}