{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VL2HQSIQQ2KY76EIQ2ZGWNMBX6","short_pith_number":"pith:VL2HQSIQ","schema_version":"1.0","canonical_sha256":"aaf478491086958ff88886b26b3581bfbd35ddc3d118c5a5d1fe48989f81e9ba","source":{"kind":"arxiv","id":"2410.03432","version":1},"attestation_state":"computed","paper":{"title":"EB-NeRD: A Large-Scale Dataset for News Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Abhishek Srivastava, Anshuk Uppal, Claudio Pomo, Jes Frellsen, Johannes Kruse, Kasper Lindskow, Marco Polignano, Michael Riis Andersen, Saikishore Kalloori","submitted_at":"2024-10-04T13:43:29Z","abstract_excerpt":"Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million unique users and more than 37 million impression logs from Ekstra Bladet. It also includes a collection of over 125,000 Danish news articles, complete with titles, abstracts, bodies, and metadata, such "},"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":"2410.03432","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-10-04T13:43:29Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"25207ef88a4e01db3f72b3c6a1460e1d022ca6dd76d633998b94e1dabd1a8a6a","abstract_canon_sha256":"db61dc96cfd0d0f20f1e9f93bb8aa341a725f7ad15dba772fe339358371598b9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:16:01.043138Z","signature_b64":"UzhjEkzoc2QMwNsb6zck2sBwef7yBiNzjUridM/Yx67y9aLxcxMulKermH+SSihBQadu5nE1C0I4NXE6XOD7Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aaf478491086958ff88886b26b3581bfbd35ddc3d118c5a5d1fe48989f81e9ba","last_reissued_at":"2026-07-05T09:16:01.042671Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:16:01.042671Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EB-NeRD: A Large-Scale Dataset for News Recommendation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.IR","authors_text":"Abhishek Srivastava, Anshuk Uppal, Claudio Pomo, Jes Frellsen, Johannes Kruse, Kasper Lindskow, Marco Polignano, Michael Riis Andersen, Saikishore Kalloori","submitted_at":"2024-10-04T13:43:29Z","abstract_excerpt":"Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recommendation Dataset (EB-NeRD). The dataset encompasses data from over a million unique users and more than 37 million impression logs from Ekstra Bladet. It also includes a collection of over 125,000 Danish news articles, complete with titles, abstracts, bodies, and metadata, such "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.03432","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/2410.03432/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":"2410.03432","created_at":"2026-07-05T09:16:01.042730+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.03432v1","created_at":"2026-07-05T09:16:01.042730+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.03432","created_at":"2026-07-05T09:16:01.042730+00:00"},{"alias_kind":"pith_short_12","alias_value":"VL2HQSIQQ2KY","created_at":"2026-07-05T09:16:01.042730+00:00"},{"alias_kind":"pith_short_16","alias_value":"VL2HQSIQQ2KY76EI","created_at":"2026-07-05T09:16:01.042730+00:00"},{"alias_kind":"pith_short_8","alias_value":"VL2HQSIQ","created_at":"2026-07-05T09:16:01.042730+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/VL2HQSIQQ2KY76EIQ2ZGWNMBX6","json":"https://pith.science/pith/VL2HQSIQQ2KY76EIQ2ZGWNMBX6.json","graph_json":"https://pith.science/api/pith-number/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/graph.json","events_json":"https://pith.science/api/pith-number/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/events.json","paper":"https://pith.science/paper/VL2HQSIQ"},"agent_actions":{"view_html":"https://pith.science/pith/VL2HQSIQQ2KY76EIQ2ZGWNMBX6","download_json":"https://pith.science/pith/VL2HQSIQQ2KY76EIQ2ZGWNMBX6.json","view_paper":"https://pith.science/paper/VL2HQSIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.03432&json=true","fetch_graph":"https://pith.science/api/pith-number/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/graph.json","fetch_events":"https://pith.science/api/pith-number/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/action/storage_attestation","attest_author":"https://pith.science/pith/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/action/author_attestation","sign_citation":"https://pith.science/pith/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/action/citation_signature","submit_replication":"https://pith.science/pith/VL2HQSIQQ2KY76EIQ2ZGWNMBX6/action/replication_record"}},"created_at":"2026-07-05T09:16:01.042730+00:00","updated_at":"2026-07-05T09:16:01.042730+00:00"}