{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IYHBOYTNRPQKQXRD3VYVTZQ2Q3","short_pith_number":"pith:IYHBOYTN","schema_version":"1.0","canonical_sha256":"460e17626d8be0a85e23dd7159e61a86cffd516f71ad1d3f76809a5ae68dbae5","source":{"kind":"arxiv","id":"2310.03148","version":1},"attestation_state":"computed","paper":{"title":"Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Ainur Yessenalina, Belhassen Bayar, Farnoosh Javadi, Phanideep Gampa","submitted_at":"2023-09-25T00:11:33Z","abstract_excerpt":"Various data imbalances that naturally arise in a multi-territory personalized recommender system can lead to a significant item bias for globally prevalent items. A locally popular item can be overshadowed by a globally prevalent item. Moreover, users' viewership patterns/statistics can drastically change from one geographic location to another which may suggest to learn specific user embeddings. In this paper, we propose a multi-task learning (MTL) technique, along with an adaptive upsampling method to reduce popularity bias in multi-territory recommendations. Our proposed framework is desig"},"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":"2310.03148","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-09-25T00:11:33Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"df643c8e5f5e5138b4450c9afe6690432ecfe2f63de78b3ab0841bbdbe9817ae","abstract_canon_sha256":"7ecc9b0943d0f46b322ab308a8ab25cc5af18f5066cbbe4705fde9a2c96b9f1c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:57:29.371302Z","signature_b64":"LMlaLjC7S/JLxJLj/fW88ilF7sKKr7H0E+ERxDHy+hty64kAJLh0ppMXhKTlte50UWi6KQGjLLHVKgZ1k2nBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"460e17626d8be0a85e23dd7159e61a86cffd516f71ad1d3f76809a5ae68dbae5","last_reissued_at":"2026-07-05T06:57:29.370820Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:57:29.370820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Task Learning For Reduced Popularity Bias In Multi-Territory Video Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Ainur Yessenalina, Belhassen Bayar, Farnoosh Javadi, Phanideep Gampa","submitted_at":"2023-09-25T00:11:33Z","abstract_excerpt":"Various data imbalances that naturally arise in a multi-territory personalized recommender system can lead to a significant item bias for globally prevalent items. A locally popular item can be overshadowed by a globally prevalent item. Moreover, users' viewership patterns/statistics can drastically change from one geographic location to another which may suggest to learn specific user embeddings. In this paper, we propose a multi-task learning (MTL) technique, along with an adaptive upsampling method to reduce popularity bias in multi-territory recommendations. Our proposed framework is desig"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.03148","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/2310.03148/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":"2310.03148","created_at":"2026-07-05T06:57:29.370882+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.03148v1","created_at":"2026-07-05T06:57:29.370882+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.03148","created_at":"2026-07-05T06:57:29.370882+00:00"},{"alias_kind":"pith_short_12","alias_value":"IYHBOYTNRPQK","created_at":"2026-07-05T06:57:29.370882+00:00"},{"alias_kind":"pith_short_16","alias_value":"IYHBOYTNRPQKQXRD","created_at":"2026-07-05T06:57:29.370882+00:00"},{"alias_kind":"pith_short_8","alias_value":"IYHBOYTN","created_at":"2026-07-05T06:57:29.370882+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/IYHBOYTNRPQKQXRD3VYVTZQ2Q3","json":"https://pith.science/pith/IYHBOYTNRPQKQXRD3VYVTZQ2Q3.json","graph_json":"https://pith.science/api/pith-number/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/graph.json","events_json":"https://pith.science/api/pith-number/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/events.json","paper":"https://pith.science/paper/IYHBOYTN"},"agent_actions":{"view_html":"https://pith.science/pith/IYHBOYTNRPQKQXRD3VYVTZQ2Q3","download_json":"https://pith.science/pith/IYHBOYTNRPQKQXRD3VYVTZQ2Q3.json","view_paper":"https://pith.science/paper/IYHBOYTN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.03148&json=true","fetch_graph":"https://pith.science/api/pith-number/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/graph.json","fetch_events":"https://pith.science/api/pith-number/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/action/storage_attestation","attest_author":"https://pith.science/pith/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/action/author_attestation","sign_citation":"https://pith.science/pith/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/action/citation_signature","submit_replication":"https://pith.science/pith/IYHBOYTNRPQKQXRD3VYVTZQ2Q3/action/replication_record"}},"created_at":"2026-07-05T06:57:29.370882+00:00","updated_at":"2026-07-05T06:57:29.370882+00:00"}