{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:FYZ7LPVWVH7B5L5PWNKCPXUJ33","short_pith_number":"pith:FYZ7LPVW","canonical_record":{"source":{"id":"2606.07492","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-05T17:46:36Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ef8d6da8f2e849d9080b446a6a67f429a1646a66e51b3d922062a2ab4567cc5b","abstract_canon_sha256":"1437b107f214b96972cb3c7b7e8c7e9a711d3ad5a131c6c1f61263f378b73f24"},"schema_version":"1.0"},"canonical_sha256":"2e33f5beb6a9fe1eafafb35427de89def8a7104947fc75ec2ae166b690939c6f","source":{"kind":"arxiv","id":"2606.07492","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07492","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07492v1","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07492","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"pith_short_12","alias_value":"FYZ7LPVWVH7B","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"pith_short_16","alias_value":"FYZ7LPVWVH7B5L5P","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"pith_short_8","alias_value":"FYZ7LPVW","created_at":"2026-06-08T01:05:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:FYZ7LPVWVH7B5L5PWNKCPXUJ33","target":"record","payload":{"canonical_record":{"source":{"id":"2606.07492","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-05T17:46:36Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ef8d6da8f2e849d9080b446a6a67f429a1646a66e51b3d922062a2ab4567cc5b","abstract_canon_sha256":"1437b107f214b96972cb3c7b7e8c7e9a711d3ad5a131c6c1f61263f378b73f24"},"schema_version":"1.0"},"canonical_sha256":"2e33f5beb6a9fe1eafafb35427de89def8a7104947fc75ec2ae166b690939c6f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:05:30.151332Z","signature_b64":"7v5H0vbEuhTHf/be6M60C8OySzz7OPF/IaQcfnpan4csU1XQO8L+ghdtWJu7oS7B8rfJvI+Ubj06i1t8/rPXDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e33f5beb6a9fe1eafafb35427de89def8a7104947fc75ec2ae166b690939c6f","last_reissued_at":"2026-06-08T01:05:30.150723Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:05:30.150723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.07492","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":"2026-06-08T01:05:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RYcPPt0VAqtAy+teo3Gsns+OrbEuj9cklxcgzY44/iHkxGUvxCY2Q76lKfbEgzIXvgMbuSnIYab9KnqUssynCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T13:19:17.388872Z"},"content_sha256":"fae4fdbd8c34fbece8208ec963254ffd14aaad4b9b89c51a68e696984f8ac9da","schema_version":"1.0","event_id":"sha256:fae4fdbd8c34fbece8208ec963254ffd14aaad4b9b89c51a68e696984f8ac9da"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:FYZ7LPVWVH7B5L5PWNKCPXUJ33","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Alexander Derevyagin, Anton Lysenko, Askar Tsyganov, Daria Korovaitceva, Ekaterina Grishina, Evgeny Frolov, Ilya Ivanov, Margarita Rusanova, Sergey Samsonov, Stepan Kuznetsov, Uliana Parkina","submitted_at":"2026-06-05T17:46:36Z","abstract_excerpt":"The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale. This drives a demand for a proper methodology for fair comparison between algorithms. Naive aggregation of performance metrics (e.g., averaging NDCG over benchmarks) can yield misleading rankings, undermining practical selection. To address this problem, we introduce a novel, data-driven ranking methodology based on Bradley-Terry (BT) model. We demonstrate that the obtained ranking depends on key dataset statistics."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07492","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/2606.07492/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":"2026-06-08T01:05:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wex34BB59mFukc1obRKzWwsD2OJJoE6n3hQHFU5je2EoQk1TtGex1KbidnTrjGYvCVU5hKO/Yz1CZ7J6yjDbAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T13:19:17.389279Z"},"content_sha256":"c419955b34a61a7bcb9f086af6c2979e970a17a0b87fa232f462034c3554f46c","schema_version":"1.0","event_id":"sha256:c419955b34a61a7bcb9f086af6c2979e970a17a0b87fa232f462034c3554f46c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FYZ7LPVWVH7B5L5PWNKCPXUJ33/bundle.json","state_url":"https://pith.science/pith/FYZ7LPVWVH7B5L5PWNKCPXUJ33/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FYZ7LPVWVH7B5L5PWNKCPXUJ33/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":"2026-06-08T13:19:17Z","links":{"resolver":"https://pith.science/pith/FYZ7LPVWVH7B5L5PWNKCPXUJ33","bundle":"https://pith.science/pith/FYZ7LPVWVH7B5L5PWNKCPXUJ33/bundle.json","state":"https://pith.science/pith/FYZ7LPVWVH7B5L5PWNKCPXUJ33/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FYZ7LPVWVH7B5L5PWNKCPXUJ33/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:FYZ7LPVWVH7B5L5PWNKCPXUJ33","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":"1437b107f214b96972cb3c7b7e8c7e9a711d3ad5a131c6c1f61263f378b73f24","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-05T17:46:36Z","title_canon_sha256":"ef8d6da8f2e849d9080b446a6a67f429a1646a66e51b3d922062a2ab4567cc5b"},"schema_version":"1.0","source":{"id":"2606.07492","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07492","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07492v1","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07492","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"pith_short_12","alias_value":"FYZ7LPVWVH7B","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"pith_short_16","alias_value":"FYZ7LPVWVH7B5L5P","created_at":"2026-06-08T01:05:30Z"},{"alias_kind":"pith_short_8","alias_value":"FYZ7LPVW","created_at":"2026-06-08T01:05:30Z"}],"graph_snapshots":[{"event_id":"sha256:c419955b34a61a7bcb9f086af6c2979e970a17a0b87fa232f462034c3554f46c","target":"graph","created_at":"2026-06-08T01:05:30Z","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/2606.07492/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The ranking of recommendation algorithms is a challenging problem since model performance is sensitive to dataset characteristics such as sparsity, sequential structure, and scale. This drives a demand for a proper methodology for fair comparison between algorithms. Naive aggregation of performance metrics (e.g., averaging NDCG over benchmarks) can yield misleading rankings, undermining practical selection. To address this problem, we introduce a novel, data-driven ranking methodology based on Bradley-Terry (BT) model. We demonstrate that the obtained ranking depends on key dataset statistics.","authors_text":"Alexander Derevyagin, Anton Lysenko, Askar Tsyganov, Daria Korovaitceva, Ekaterina Grishina, Evgeny Frolov, Ilya Ivanov, Margarita Rusanova, Sergey Samsonov, Stepan Kuznetsov, Uliana Parkina","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-05T17:46:36Z","title":"Bradley-Terry Rankings for Recommender Systems Across Dataset Taxonomies"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07492","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:fae4fdbd8c34fbece8208ec963254ffd14aaad4b9b89c51a68e696984f8ac9da","target":"record","created_at":"2026-06-08T01:05:30Z","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":"1437b107f214b96972cb3c7b7e8c7e9a711d3ad5a131c6c1f61263f378b73f24","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-05T17:46:36Z","title_canon_sha256":"ef8d6da8f2e849d9080b446a6a67f429a1646a66e51b3d922062a2ab4567cc5b"},"schema_version":"1.0","source":{"id":"2606.07492","kind":"arxiv","version":1}},"canonical_sha256":"2e33f5beb6a9fe1eafafb35427de89def8a7104947fc75ec2ae166b690939c6f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e33f5beb6a9fe1eafafb35427de89def8a7104947fc75ec2ae166b690939c6f","first_computed_at":"2026-06-08T01:05:30.150723Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:05:30.150723Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7v5H0vbEuhTHf/be6M60C8OySzz7OPF/IaQcfnpan4csU1XQO8L+ghdtWJu7oS7B8rfJvI+Ubj06i1t8/rPXDg==","signature_status":"signed_v1","signed_at":"2026-06-08T01:05:30.151332Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07492","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fae4fdbd8c34fbece8208ec963254ffd14aaad4b9b89c51a68e696984f8ac9da","sha256:c419955b34a61a7bcb9f086af6c2979e970a17a0b87fa232f462034c3554f46c"],"state_sha256":"bd2e15e476f57373d6736f005391915936e6bf8dbce0ccaf0ba3529c85cbeb75"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u9vmnJdYEcdrH7fcJGRBBq3EQs94MfcmqxHC5x6P0l4F31E8JsKDBaZ+LARHLt4i94KVIw1r5LVj/4psW/jlBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T13:19:17.391320Z","bundle_sha256":"0a6d9747b8190495fa791f5e0de58eaa3dd6b42acce43dad0cfbacdba6ff3188"}}