{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:SJ2PXVB3FXZ6KPQI763RZHT4W6","short_pith_number":"pith:SJ2PXVB3","schema_version":"1.0","canonical_sha256":"9274fbd43b2df3e53e08ffb71c9e7cb78a4e442ab60fe1ecc87dafcc6e530c90","source":{"kind":"arxiv","id":"2109.12839","version":1},"attestation_state":"computed","paper":{"title":"Review of Clustering-Based Recommender Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Irina Beregovskaya, Mikhail Koroteev","submitted_at":"2021-09-27T07:17:30Z","abstract_excerpt":"Recommender systems are one of the most applied methods in machine learning and find applications in many areas, ranging from economics to the Internet of things. This article provides a general overview of modern approaches to recommender system design using clustering as a preliminary step to improve overall performance. Using clustering can address several known issues in recommendation systems, including increasing the diversity, consistency, and reliability of recommendations; the data sparsity of user-preference matrices; and changes in user preferences over time. This work will be usefu"},"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":"2109.12839","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2021-09-27T07:17:30Z","cross_cats_sorted":[],"title_canon_sha256":"080bd23273446f85ae09cb24884e0e7e8b62cf6282db848d9c45194d2ead87ea","abstract_canon_sha256":"0f6b9b97d8cf219559726dc1d6821c47406f2c9e2d13fe50068e40eff956e3cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:17:37.149663Z","signature_b64":"pySjOurqy/UmMV07V3K8fq44em+0BqqkXX49o3WoncXSzoKZX0HIgJLWfnjTDHfKVDfW+l8X+bqeUyq+biWmCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9274fbd43b2df3e53e08ffb71c9e7cb78a4e442ab60fe1ecc87dafcc6e530c90","last_reissued_at":"2026-07-05T03:17:37.149180Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:17:37.149180Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Review of Clustering-Based Recommender Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Irina Beregovskaya, Mikhail Koroteev","submitted_at":"2021-09-27T07:17:30Z","abstract_excerpt":"Recommender systems are one of the most applied methods in machine learning and find applications in many areas, ranging from economics to the Internet of things. This article provides a general overview of modern approaches to recommender system design using clustering as a preliminary step to improve overall performance. Using clustering can address several known issues in recommendation systems, including increasing the diversity, consistency, and reliability of recommendations; the data sparsity of user-preference matrices; and changes in user preferences over time. This work will be usefu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.12839","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/2109.12839/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":"2109.12839","created_at":"2026-07-05T03:17:37.149237+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.12839v1","created_at":"2026-07-05T03:17:37.149237+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.12839","created_at":"2026-07-05T03:17:37.149237+00:00"},{"alias_kind":"pith_short_12","alias_value":"SJ2PXVB3FXZ6","created_at":"2026-07-05T03:17:37.149237+00:00"},{"alias_kind":"pith_short_16","alias_value":"SJ2PXVB3FXZ6KPQI","created_at":"2026-07-05T03:17:37.149237+00:00"},{"alias_kind":"pith_short_8","alias_value":"SJ2PXVB3","created_at":"2026-07-05T03:17:37.149237+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.26983","citing_title":"Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6","json":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6.json","graph_json":"https://pith.science/api/pith-number/SJ2PXVB3FXZ6KPQI763RZHT4W6/graph.json","events_json":"https://pith.science/api/pith-number/SJ2PXVB3FXZ6KPQI763RZHT4W6/events.json","paper":"https://pith.science/paper/SJ2PXVB3"},"agent_actions":{"view_html":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6","download_json":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6.json","view_paper":"https://pith.science/paper/SJ2PXVB3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.12839&json=true","fetch_graph":"https://pith.science/api/pith-number/SJ2PXVB3FXZ6KPQI763RZHT4W6/graph.json","fetch_events":"https://pith.science/api/pith-number/SJ2PXVB3FXZ6KPQI763RZHT4W6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6/action/storage_attestation","attest_author":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6/action/author_attestation","sign_citation":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6/action/citation_signature","submit_replication":"https://pith.science/pith/SJ2PXVB3FXZ6KPQI763RZHT4W6/action/replication_record"}},"created_at":"2026-07-05T03:17:37.149237+00:00","updated_at":"2026-07-05T03:17:37.149237+00:00"}