{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FEKHKBBETSIT5PEYKFJKFCETMD","short_pith_number":"pith:FEKHKBBE","canonical_record":{"source":{"id":"2507.07909","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-10T16:41:10Z","cross_cats_sorted":[],"title_canon_sha256":"c720a899a05750c1af03f5df01821e01a3adf7127e826dd3a60607627ac71586","abstract_canon_sha256":"ec55c3b613f1b3dadca94978c516f9c2e9020bb1004ec28d5fa2af472dbb0922"},"schema_version":"1.0"},"canonical_sha256":"29147504249c913ebc985152a2889360cb1acb7bf8258bb48cedc07b77299b60","source":{"kind":"arxiv","id":"2507.07909","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.07909","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"arxiv_version","alias_value":"2507.07909v1","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07909","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"pith_short_12","alias_value":"FEKHKBBETSIT","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"pith_short_16","alias_value":"FEKHKBBETSIT5PEY","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"pith_short_8","alias_value":"FEKHKBBE","created_at":"2026-07-05T11:35:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FEKHKBBETSIT5PEYKFJKFCETMD","target":"record","payload":{"canonical_record":{"source":{"id":"2507.07909","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-10T16:41:10Z","cross_cats_sorted":[],"title_canon_sha256":"c720a899a05750c1af03f5df01821e01a3adf7127e826dd3a60607627ac71586","abstract_canon_sha256":"ec55c3b613f1b3dadca94978c516f9c2e9020bb1004ec28d5fa2af472dbb0922"},"schema_version":"1.0"},"canonical_sha256":"29147504249c913ebc985152a2889360cb1acb7bf8258bb48cedc07b77299b60","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:06.954719Z","signature_b64":"VcghfRR2HmBtcaT/KvXkkqfNu5GE1/kuLhqGctZSR7zn53Zlh2IYtcoZiagf6edJiDUKjielYY9JGitis6VuCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29147504249c913ebc985152a2889360cb1acb7bf8258bb48cedc07b77299b60","last_reissued_at":"2026-07-05T11:35:06.954247Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:06.954247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.07909","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":"2026-07-05T11:35:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oY5so1OgNSFwWeYJQkSukqOdevsfEO6T46odHcsDSZlqQv+8YHZiXS02X5VPk2rC2QR9QOGSmVnA7/m2E8MwCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T18:02:34.331018Z"},"content_sha256":"8f8bb4b0892ca976a1c89d363414ba568ce21a41f7750d8921b49fe5dffd0f4c","schema_version":"1.0","event_id":"sha256:8f8bb4b0892ca976a1c89d363414ba568ce21a41f7750d8921b49fe5dffd0f4c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FEKHKBBETSIT5PEYKFJKFCETMD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Document Similarity Enhanced IPS Estimation for Unbiased Learning to Rank","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Graham McDonald, Iadh Ounis, Zeyan Liang","submitted_at":"2025-07-10T16:41:10Z","abstract_excerpt":"Learning to Rank (LTR) models learn from historical user interactions, such as user clicks. However, there is an inherent bias in the clicks of users due to position bias, i.e., users are more likely to click highly-ranked documents than low-ranked documents. To address this bias when training LTR models, many approaches from the literature re-weight the users' click data using Inverse Propensity Scoring (IPS). IPS re-weights the user's clicks proportionately to the position in the historical ranking that a document was placed when it was clicked since low-ranked documents are less likely to b"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07909","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/2507.07909/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":"2026-07-05T11:35:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7tVPaIC7ECHB2zjRrELr1OpS81vD8bbxVSaWcJZ33a6HTk61l4JhO+xW/+uNf6hmps5gbXN9QdTNlWLYIt8+Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T18:02:34.331955Z"},"content_sha256":"2f11709d7ce1c4748150da5e9bf2e7536e610591fa6413b2ae2990b5d5a37016","schema_version":"1.0","event_id":"sha256:2f11709d7ce1c4748150da5e9bf2e7536e610591fa6413b2ae2990b5d5a37016"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FEKHKBBETSIT5PEYKFJKFCETMD/bundle.json","state_url":"https://pith.science/pith/FEKHKBBETSIT5PEYKFJKFCETMD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FEKHKBBETSIT5PEYKFJKFCETMD/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":"2026-08-14T18:02:34Z","links":{"resolver":"https://pith.science/pith/FEKHKBBETSIT5PEYKFJKFCETMD","bundle":"https://pith.science/pith/FEKHKBBETSIT5PEYKFJKFCETMD/bundle.json","state":"https://pith.science/pith/FEKHKBBETSIT5PEYKFJKFCETMD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FEKHKBBETSIT5PEYKFJKFCETMD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FEKHKBBETSIT5PEYKFJKFCETMD","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":"ec55c3b613f1b3dadca94978c516f9c2e9020bb1004ec28d5fa2af472dbb0922","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-10T16:41:10Z","title_canon_sha256":"c720a899a05750c1af03f5df01821e01a3adf7127e826dd3a60607627ac71586"},"schema_version":"1.0","source":{"id":"2507.07909","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.07909","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"arxiv_version","alias_value":"2507.07909v1","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07909","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"pith_short_12","alias_value":"FEKHKBBETSIT","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"pith_short_16","alias_value":"FEKHKBBETSIT5PEY","created_at":"2026-07-05T11:35:06Z"},{"alias_kind":"pith_short_8","alias_value":"FEKHKBBE","created_at":"2026-07-05T11:35:06Z"}],"graph_snapshots":[{"event_id":"sha256:2f11709d7ce1c4748150da5e9bf2e7536e610591fa6413b2ae2990b5d5a37016","target":"graph","created_at":"2026-07-05T11:35:06Z","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/2507.07909/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning to Rank (LTR) models learn from historical user interactions, such as user clicks. However, there is an inherent bias in the clicks of users due to position bias, i.e., users are more likely to click highly-ranked documents than low-ranked documents. To address this bias when training LTR models, many approaches from the literature re-weight the users' click data using Inverse Propensity Scoring (IPS). IPS re-weights the user's clicks proportionately to the position in the historical ranking that a document was placed when it was clicked since low-ranked documents are less likely to b","authors_text":"Graham McDonald, Iadh Ounis, Zeyan Liang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-10T16:41:10Z","title":"Document Similarity Enhanced IPS Estimation for Unbiased Learning to Rank"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07909","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":"sha256:8f8bb4b0892ca976a1c89d363414ba568ce21a41f7750d8921b49fe5dffd0f4c","target":"record","created_at":"2026-07-05T11:35:06Z","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":"ec55c3b613f1b3dadca94978c516f9c2e9020bb1004ec28d5fa2af472dbb0922","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-07-10T16:41:10Z","title_canon_sha256":"c720a899a05750c1af03f5df01821e01a3adf7127e826dd3a60607627ac71586"},"schema_version":"1.0","source":{"id":"2507.07909","kind":"arxiv","version":1}},"canonical_sha256":"29147504249c913ebc985152a2889360cb1acb7bf8258bb48cedc07b77299b60","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"29147504249c913ebc985152a2889360cb1acb7bf8258bb48cedc07b77299b60","first_computed_at":"2026-07-05T11:35:06.954247Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:35:06.954247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VcghfRR2HmBtcaT/KvXkkqfNu5GE1/kuLhqGctZSR7zn53Zlh2IYtcoZiagf6edJiDUKjielYY9JGitis6VuCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:35:06.954719Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.07909","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8f8bb4b0892ca976a1c89d363414ba568ce21a41f7750d8921b49fe5dffd0f4c","sha256:2f11709d7ce1c4748150da5e9bf2e7536e610591fa6413b2ae2990b5d5a37016"],"state_sha256":"dd082388a585b71acdd544a098eefa6b6a215b5a0a3570bfb5c9cf765e3d7a85"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qofJY2oNJ9kinP+K839deQ+gwX9+XAILe0b8GuJ13tWhQPK1g9bEQVkNUTEnDBwTFoGoBhQJGt564g9TYg6mDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T18:02:34.338232Z","bundle_sha256":"0c41d40d74f0106968b9259dc05fe409c9a51c0f07a3889deff553de26d4a687"}}