{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:N7Q7FRTSSETTWQK3543UGEMM7P","short_pith_number":"pith:N7Q7FRTS","schema_version":"1.0","canonical_sha256":"6fe1f2c67291273b415bef3743118cfbe9e9b5fa10831148ff7ba4c6a2979c59","source":{"kind":"arxiv","id":"2411.05317","version":1},"attestation_state":"computed","paper":{"title":"SeqRFM: Fast RFM Analysis in Sequence Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Philippe Fournier-Viger, Pinlyu Zhou, Wensheng Gan, Yanxin Zheng, Zefeng Chen","submitted_at":"2024-11-08T04:18:07Z","abstract_excerpt":"In recent years, data mining technologies have been well applied to many domains, including e-commerce. In customer relationship management (CRM), the RFM analysis model is one of the most effective approaches to increase the profits of major enterprises. However, with the rapid development of e-commerce, the diversity and abundance of e-commerce data pose a challenge to mining efficiency. Moreover, in actual market transactions, the chronological order of transactions reflects customer behavior and preferences. To address these challenges, we develop an effective algorithm called SeqRFM, whic"},"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":"2411.05317","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2024-11-08T04:18:07Z","cross_cats_sorted":[],"title_canon_sha256":"12fe40c44058ff053583223aa9715c17b5537c8bd0a3a0da1c1bd9c21535ce5f","abstract_canon_sha256":"d4f86ef0c37e56fde3a62e140c59ae89e8e406ffd9951a371d39d847e1e7534c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:58.405572Z","signature_b64":"6cFBrrXysby0hXjcvG57ZUV9XHnagVrovP3MxBWcxNVBO6HYVn9UmvpOUs/ASKsuZbSpGyhD56VJI/TOH9YYDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6fe1f2c67291273b415bef3743118cfbe9e9b5fa10831148ff7ba4c6a2979c59","last_reissued_at":"2026-07-05T09:32:58.404967Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:58.404967Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SeqRFM: Fast RFM Analysis in Sequence Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Philippe Fournier-Viger, Pinlyu Zhou, Wensheng Gan, Yanxin Zheng, Zefeng Chen","submitted_at":"2024-11-08T04:18:07Z","abstract_excerpt":"In recent years, data mining technologies have been well applied to many domains, including e-commerce. In customer relationship management (CRM), the RFM analysis model is one of the most effective approaches to increase the profits of major enterprises. However, with the rapid development of e-commerce, the diversity and abundance of e-commerce data pose a challenge to mining efficiency. Moreover, in actual market transactions, the chronological order of transactions reflects customer behavior and preferences. To address these challenges, we develop an effective algorithm called SeqRFM, whic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.05317","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/2411.05317/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":"2411.05317","created_at":"2026-07-05T09:32:58.405028+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.05317v1","created_at":"2026-07-05T09:32:58.405028+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.05317","created_at":"2026-07-05T09:32:58.405028+00:00"},{"alias_kind":"pith_short_12","alias_value":"N7Q7FRTSSETT","created_at":"2026-07-05T09:32:58.405028+00:00"},{"alias_kind":"pith_short_16","alias_value":"N7Q7FRTSSETTWQK3","created_at":"2026-07-05T09:32:58.405028+00:00"},{"alias_kind":"pith_short_8","alias_value":"N7Q7FRTS","created_at":"2026-07-05T09:32:58.405028+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/N7Q7FRTSSETTWQK3543UGEMM7P","json":"https://pith.science/pith/N7Q7FRTSSETTWQK3543UGEMM7P.json","graph_json":"https://pith.science/api/pith-number/N7Q7FRTSSETTWQK3543UGEMM7P/graph.json","events_json":"https://pith.science/api/pith-number/N7Q7FRTSSETTWQK3543UGEMM7P/events.json","paper":"https://pith.science/paper/N7Q7FRTS"},"agent_actions":{"view_html":"https://pith.science/pith/N7Q7FRTSSETTWQK3543UGEMM7P","download_json":"https://pith.science/pith/N7Q7FRTSSETTWQK3543UGEMM7P.json","view_paper":"https://pith.science/paper/N7Q7FRTS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.05317&json=true","fetch_graph":"https://pith.science/api/pith-number/N7Q7FRTSSETTWQK3543UGEMM7P/graph.json","fetch_events":"https://pith.science/api/pith-number/N7Q7FRTSSETTWQK3543UGEMM7P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N7Q7FRTSSETTWQK3543UGEMM7P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N7Q7FRTSSETTWQK3543UGEMM7P/action/storage_attestation","attest_author":"https://pith.science/pith/N7Q7FRTSSETTWQK3543UGEMM7P/action/author_attestation","sign_citation":"https://pith.science/pith/N7Q7FRTSSETTWQK3543UGEMM7P/action/citation_signature","submit_replication":"https://pith.science/pith/N7Q7FRTSSETTWQK3543UGEMM7P/action/replication_record"}},"created_at":"2026-07-05T09:32:58.405028+00:00","updated_at":"2026-07-05T09:32:58.405028+00:00"}