{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FS5GCQO4CINFP2XW75A6ZDEALM","short_pith_number":"pith:FS5GCQO4","schema_version":"1.0","canonical_sha256":"2cba6141dc121a57eaf6ff41ec8c805b1c1f8f295fe3f01d44c25119af6eebcc","source":{"kind":"arxiv","id":"2602.15505","version":2},"attestation_state":"computed","paper":{"title":"Binge Watch: Reproducible Multimodal Benchmarks Datasets for Large-Scale Movie Recommendation on MovieLens-10M and 20M","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Alessandro Petruzzelli, Cataldo Musto, Giovanni Semeraro, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops","submitted_at":"2026-02-17T11:22:20Z","abstract_excerpt":"As Multimodal Recommender Systems gain interest, high-quality datasets with multimedia side information have become essential. However, much of the current literature reports experiments that rely on small-scale, undocumented, or non-public datasets. In this paper, we introduce M3L-10M and M3L-20M, two large-scale, fully documented and reproducible datasets that enrich MovieLens-10M and MovieLens-20M with multimodal features. Following a documented pipeline, we collect movie plots, posters, and trailers and extract features using state-of-the-art encoders. We publicly release raw data mappings"},"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":"2602.15505","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2026-02-17T11:22:20Z","cross_cats_sorted":[],"title_canon_sha256":"dc1f9b3dd38c5df4497d9b742eb0cb2f04cd7886f7d271f4fe2324cc666911dd","abstract_canon_sha256":"dd7405a91a5b0a9a2a3ddaacaad5053386d2df329f403fc43d0d60b75fed789c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T02:23:26.239568Z","signature_b64":"eAdtQ/2zrPM7w849Vak32cvmv4Wx9GYiX6i/tZQt6YjRKj2+8VN9+bAm87bn+aLaovUdyldKkNrVgm+tWPtIDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2cba6141dc121a57eaf6ff41ec8c805b1c1f8f295fe3f01d44c25119af6eebcc","last_reissued_at":"2026-07-28T02:23:26.238519Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T02:23:26.238519Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Binge Watch: Reproducible Multimodal Benchmarks Datasets for Large-Scale Movie Recommendation on MovieLens-10M and 20M","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Alessandro Petruzzelli, Cataldo Musto, Giovanni Semeraro, Giuseppe Spillo, Marco de Gemmis, Pasquale Lops","submitted_at":"2026-02-17T11:22:20Z","abstract_excerpt":"As Multimodal Recommender Systems gain interest, high-quality datasets with multimedia side information have become essential. However, much of the current literature reports experiments that rely on small-scale, undocumented, or non-public datasets. In this paper, we introduce M3L-10M and M3L-20M, two large-scale, fully documented and reproducible datasets that enrich MovieLens-10M and MovieLens-20M with multimodal features. Following a documented pipeline, we collect movie plots, posters, and trailers and extract features using state-of-the-art encoders. We publicly release raw data mappings"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.15505","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/2602.15505/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":"2602.15505","created_at":"2026-07-28T02:23:26.238995+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.15505v2","created_at":"2026-07-28T02:23:26.238995+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.15505","created_at":"2026-07-28T02:23:26.238995+00:00"},{"alias_kind":"pith_short_12","alias_value":"FS5GCQO4CINF","created_at":"2026-07-28T02:23:26.238995+00:00"},{"alias_kind":"pith_short_16","alias_value":"FS5GCQO4CINFP2XW","created_at":"2026-07-28T02:23:26.238995+00:00"},{"alias_kind":"pith_short_8","alias_value":"FS5GCQO4","created_at":"2026-07-28T02:23:26.238995+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/FS5GCQO4CINFP2XW75A6ZDEALM","json":"https://pith.science/pith/FS5GCQO4CINFP2XW75A6ZDEALM.json","graph_json":"https://pith.science/api/pith-number/FS5GCQO4CINFP2XW75A6ZDEALM/graph.json","events_json":"https://pith.science/api/pith-number/FS5GCQO4CINFP2XW75A6ZDEALM/events.json","paper":"https://pith.science/paper/FS5GCQO4"},"agent_actions":{"view_html":"https://pith.science/pith/FS5GCQO4CINFP2XW75A6ZDEALM","download_json":"https://pith.science/pith/FS5GCQO4CINFP2XW75A6ZDEALM.json","view_paper":"https://pith.science/paper/FS5GCQO4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.15505&json=true","fetch_graph":"https://pith.science/api/pith-number/FS5GCQO4CINFP2XW75A6ZDEALM/graph.json","fetch_events":"https://pith.science/api/pith-number/FS5GCQO4CINFP2XW75A6ZDEALM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FS5GCQO4CINFP2XW75A6ZDEALM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FS5GCQO4CINFP2XW75A6ZDEALM/action/storage_attestation","attest_author":"https://pith.science/pith/FS5GCQO4CINFP2XW75A6ZDEALM/action/author_attestation","sign_citation":"https://pith.science/pith/FS5GCQO4CINFP2XW75A6ZDEALM/action/citation_signature","submit_replication":"https://pith.science/pith/FS5GCQO4CINFP2XW75A6ZDEALM/action/replication_record"}},"created_at":"2026-07-28T02:23:26.238995+00:00","updated_at":"2026-07-28T02:23:26.238995+00:00"}