{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:LTCIKJBXLTWQMZIKOID4VNCQYJ","short_pith_number":"pith:LTCIKJBX","schema_version":"1.0","canonical_sha256":"5cc48524375ced06650a7207cab450c24dea5d1162fb4d636226bd971e84253b","source":{"kind":"arxiv","id":"1901.01418","version":1},"attestation_state":"computed","paper":{"title":"On hybrid modular recommendation systems for video streaming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Evripidis Tzamousis, Maria Papadopouli","submitted_at":"2019-01-05T14:02:56Z","abstract_excerpt":"The recommendation systems aim to improve the user engagement by recommending appropriate personalized content to users, exploiting information about their preferences. We propose the enabler, a hybrid recommendation system which employs various machine-learning (ML) algorithms for learning an efficient combination of several recommendation algorithms and selects the best blending for a given input.Specifically, it integrates three layers, namely, the trainer which trains the underlying recommenders, the blender which determines the most efficient combination of the recommenders, and the teste"},"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":"1901.01418","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-01-05T14:02:56Z","cross_cats_sorted":[],"title_canon_sha256":"85273357fe45b922b7748b7a1f8eb82776c0250c09f78312c57aca8e0291a7e1","abstract_canon_sha256":"e503bc208285f775a8f54e2952a86de2c5fdfb7714df3c7d3c1704580face6ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:52.069461Z","signature_b64":"ydSD31Szl/BOyRwpXHiA5h8z7cW/hJ4fjJuqA9+BJLfu80IQpyCOJKSyn2YHx4qjnviHC3ggi+3ayTCC16weBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cc48524375ced06650a7207cab450c24dea5d1162fb4d636226bd971e84253b","last_reissued_at":"2026-05-17T23:56:52.069034Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:52.069034Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On hybrid modular recommendation systems for video streaming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Evripidis Tzamousis, Maria Papadopouli","submitted_at":"2019-01-05T14:02:56Z","abstract_excerpt":"The recommendation systems aim to improve the user engagement by recommending appropriate personalized content to users, exploiting information about their preferences. We propose the enabler, a hybrid recommendation system which employs various machine-learning (ML) algorithms for learning an efficient combination of several recommendation algorithms and selects the best blending for a given input.Specifically, it integrates three layers, namely, the trainer which trains the underlying recommenders, the blender which determines the most efficient combination of the recommenders, and the teste"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.01418","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":""},"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":"1901.01418","created_at":"2026-05-17T23:56:52.069091+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.01418v1","created_at":"2026-05-17T23:56:52.069091+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.01418","created_at":"2026-05-17T23:56:52.069091+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTCIKJBXLTWQ","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTCIKJBXLTWQMZIK","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTCIKJBX","created_at":"2026-05-18T12:33:21.387695+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/LTCIKJBXLTWQMZIKOID4VNCQYJ","json":"https://pith.science/pith/LTCIKJBXLTWQMZIKOID4VNCQYJ.json","graph_json":"https://pith.science/api/pith-number/LTCIKJBXLTWQMZIKOID4VNCQYJ/graph.json","events_json":"https://pith.science/api/pith-number/LTCIKJBXLTWQMZIKOID4VNCQYJ/events.json","paper":"https://pith.science/paper/LTCIKJBX"},"agent_actions":{"view_html":"https://pith.science/pith/LTCIKJBXLTWQMZIKOID4VNCQYJ","download_json":"https://pith.science/pith/LTCIKJBXLTWQMZIKOID4VNCQYJ.json","view_paper":"https://pith.science/paper/LTCIKJBX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.01418&json=true","fetch_graph":"https://pith.science/api/pith-number/LTCIKJBXLTWQMZIKOID4VNCQYJ/graph.json","fetch_events":"https://pith.science/api/pith-number/LTCIKJBXLTWQMZIKOID4VNCQYJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTCIKJBXLTWQMZIKOID4VNCQYJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTCIKJBXLTWQMZIKOID4VNCQYJ/action/storage_attestation","attest_author":"https://pith.science/pith/LTCIKJBXLTWQMZIKOID4VNCQYJ/action/author_attestation","sign_citation":"https://pith.science/pith/LTCIKJBXLTWQMZIKOID4VNCQYJ/action/citation_signature","submit_replication":"https://pith.science/pith/LTCIKJBXLTWQMZIKOID4VNCQYJ/action/replication_record"}},"created_at":"2026-05-17T23:56:52.069091+00:00","updated_at":"2026-05-17T23:56:52.069091+00:00"}