{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AYNGESKXHF3TQ6RKJ7S6AKEYNB","short_pith_number":"pith:AYNGESKX","schema_version":"1.0","canonical_sha256":"061a6249573977387a2a4fe5e02898686d86479a286d019cf79474da5e5bf84d","source":{"kind":"arxiv","id":"2501.07294","version":2},"attestation_state":"computed","paper":{"title":"Dataset-Agnostic Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Edoardo D'Amico, Tri Kurniawan Wijaya, Xinyang Shao","submitted_at":"2025-01-13T13:01:00Z","abstract_excerpt":"Recommender systems have become a cornerstone of personalized user experiences, yet their development typically involves significant manual intervention, including dataset-specific feature engineering, hyperparameter tuning, and configuration. To this end, we introduce a novel paradigm: Dataset-Agnostic Recommender Systems (DAReS) that aims to enable a single codebase to autonomously adapt to various datasets without the need for fine-tuning, for a given recommender system task. Central to this approach is the Dataset Description Language (DsDL), a structured format that provides metadata abou"},"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":"2501.07294","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-01-13T13:01:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1e71383d4680e9b99a73e6bdb92c90180c0721e2c6248653b05f01ad56d2a07d","abstract_canon_sha256":"fe6cc48c294bf802eb8cbe2e20a689cd288b1e64036fd322c5f839c508d555b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:52:57.622074Z","signature_b64":"XL2di6ffnbyuqlDFqi3Dqz+Rg60/cB+rIJJBtgLBlNzJaUzfYFZRHKqYxmPrUMpjilaJ46ynWVeVz2+YMWx6Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"061a6249573977387a2a4fe5e02898686d86479a286d019cf79474da5e5bf84d","last_reissued_at":"2026-07-05T10:52:57.621271Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:52:57.621271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dataset-Agnostic Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Edoardo D'Amico, Tri Kurniawan Wijaya, Xinyang Shao","submitted_at":"2025-01-13T13:01:00Z","abstract_excerpt":"Recommender systems have become a cornerstone of personalized user experiences, yet their development typically involves significant manual intervention, including dataset-specific feature engineering, hyperparameter tuning, and configuration. To this end, we introduce a novel paradigm: Dataset-Agnostic Recommender Systems (DAReS) that aims to enable a single codebase to autonomously adapt to various datasets without the need for fine-tuning, for a given recommender system task. Central to this approach is the Dataset Description Language (DsDL), a structured format that provides metadata abou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.07294","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/2501.07294/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":"2501.07294","created_at":"2026-07-05T10:52:57.621393+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.07294v2","created_at":"2026-07-05T10:52:57.621393+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.07294","created_at":"2026-07-05T10:52:57.621393+00:00"},{"alias_kind":"pith_short_12","alias_value":"AYNGESKXHF3T","created_at":"2026-07-05T10:52:57.621393+00:00"},{"alias_kind":"pith_short_16","alias_value":"AYNGESKXHF3TQ6RK","created_at":"2026-07-05T10:52:57.621393+00:00"},{"alias_kind":"pith_short_8","alias_value":"AYNGESKX","created_at":"2026-07-05T10:52:57.621393+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.03391","citing_title":"Universal Reusability in Recommender Systems: The Case for Dataset- and Task-Independent Frameworks","ref_index":69,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB","json":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB.json","graph_json":"https://pith.science/api/pith-number/AYNGESKXHF3TQ6RKJ7S6AKEYNB/graph.json","events_json":"https://pith.science/api/pith-number/AYNGESKXHF3TQ6RKJ7S6AKEYNB/events.json","paper":"https://pith.science/paper/AYNGESKX"},"agent_actions":{"view_html":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB","download_json":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB.json","view_paper":"https://pith.science/paper/AYNGESKX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.07294&json=true","fetch_graph":"https://pith.science/api/pith-number/AYNGESKXHF3TQ6RKJ7S6AKEYNB/graph.json","fetch_events":"https://pith.science/api/pith-number/AYNGESKXHF3TQ6RKJ7S6AKEYNB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB/action/storage_attestation","attest_author":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB/action/author_attestation","sign_citation":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB/action/citation_signature","submit_replication":"https://pith.science/pith/AYNGESKXHF3TQ6RKJ7S6AKEYNB/action/replication_record"}},"created_at":"2026-07-05T10:52:57.621393+00:00","updated_at":"2026-07-05T10:52:57.621393+00:00"}