{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2DRK2ICFQ2KB67BDN7YDLLPNDG","short_pith_number":"pith:2DRK2ICF","schema_version":"1.0","canonical_sha256":"d0e2ad204586941f7c236ff035aded19ab654d821cf520c2a0a5abdda831806d","source":{"kind":"arxiv","id":"2603.25126","version":2},"attestation_state":"computed","paper":{"title":"MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Bing Yin, Chao Wang, Hao Li, Junjie Meng, Ranxu zhang, Yanyong Zhang, Ying Sun, Ziqi Xu","submitted_at":"2026-03-26T07:51:14Z","abstract_excerpt":"Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling and alleviate data sparsity issues in traditional single-behavior approaches. However, existing MBR methods face fundamental challenges: they lack principled frameworks to model complex confounding effects from user behavioral habits and item multi-behavior distributions, struggle with effective aggregation of heterogeneous auxiliary behaviors, and fail to align behavioral representations across semantic gaps while accounting for bias distortions. To addr"},"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":"2603.25126","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-03-26T07:51:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7e4322a1285e52c9a50f4a6cfce95b752f2ea72c3c43a081c0b2510137df0096","abstract_canon_sha256":"14e8ef7e74dccec7029e5e52a56fe8ebfb8f6c2f36c1c18212ac2bf5f0bf0b54"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T02:19:49.988630Z","signature_b64":"iX+f/5zFzDLrfPESxoZhRQ8yHcXne5wvnLsX6e+zU+u9S8216XZyayNdAB1RAoMwvdmyIXeo6fGmIfxUDRB+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0e2ad204586941f7c236ff035aded19ab654d821cf520c2a0a5abdda831806d","last_reissued_at":"2026-07-07T02:19:49.987668Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T02:19:49.987668Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Bing Yin, Chao Wang, Hao Li, Junjie Meng, Ranxu zhang, Yanyong Zhang, Ying Sun, Ziqi Xu","submitted_at":"2026-03-26T07:51:14Z","abstract_excerpt":"Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling and alleviate data sparsity issues in traditional single-behavior approaches. However, existing MBR methods face fundamental challenges: they lack principled frameworks to model complex confounding effects from user behavioral habits and item multi-behavior distributions, struggle with effective aggregation of heterogeneous auxiliary behaviors, and fail to align behavioral representations across semantic gaps while accounting for bias distortions. To addr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.25126","kind":"arxiv","version":2},"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/2603.25126/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":"2603.25126","created_at":"2026-07-07T02:19:49.987798+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.25126v2","created_at":"2026-07-07T02:19:49.987798+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.25126","created_at":"2026-07-07T02:19:49.987798+00:00"},{"alias_kind":"pith_short_12","alias_value":"2DRK2ICFQ2KB","created_at":"2026-07-07T02:19:49.987798+00:00"},{"alias_kind":"pith_short_16","alias_value":"2DRK2ICFQ2KB67BD","created_at":"2026-07-07T02:19:49.987798+00:00"},{"alias_kind":"pith_short_8","alias_value":"2DRK2ICF","created_at":"2026-07-07T02:19:49.987798+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/2DRK2ICFQ2KB67BDN7YDLLPNDG","json":"https://pith.science/pith/2DRK2ICFQ2KB67BDN7YDLLPNDG.json","graph_json":"https://pith.science/api/pith-number/2DRK2ICFQ2KB67BDN7YDLLPNDG/graph.json","events_json":"https://pith.science/api/pith-number/2DRK2ICFQ2KB67BDN7YDLLPNDG/events.json","paper":"https://pith.science/paper/2DRK2ICF"},"agent_actions":{"view_html":"https://pith.science/pith/2DRK2ICFQ2KB67BDN7YDLLPNDG","download_json":"https://pith.science/pith/2DRK2ICFQ2KB67BDN7YDLLPNDG.json","view_paper":"https://pith.science/paper/2DRK2ICF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.25126&json=true","fetch_graph":"https://pith.science/api/pith-number/2DRK2ICFQ2KB67BDN7YDLLPNDG/graph.json","fetch_events":"https://pith.science/api/pith-number/2DRK2ICFQ2KB67BDN7YDLLPNDG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2DRK2ICFQ2KB67BDN7YDLLPNDG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2DRK2ICFQ2KB67BDN7YDLLPNDG/action/storage_attestation","attest_author":"https://pith.science/pith/2DRK2ICFQ2KB67BDN7YDLLPNDG/action/author_attestation","sign_citation":"https://pith.science/pith/2DRK2ICFQ2KB67BDN7YDLLPNDG/action/citation_signature","submit_replication":"https://pith.science/pith/2DRK2ICFQ2KB67BDN7YDLLPNDG/action/replication_record"}},"created_at":"2026-07-07T02:19:49.987798+00:00","updated_at":"2026-07-07T02:19:49.987798+00:00"}