{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:VYH5BQ2SL6EGAMXON7OCAZTCOU","short_pith_number":"pith:VYH5BQ2S","schema_version":"1.0","canonical_sha256":"ae0fd0c3525f886032ee6fdc206662752a6e8330764a810d331d7eb513d7ee09","source":{"kind":"arxiv","id":"2511.00176","version":1},"attestation_state":"computed","paper":{"title":"Effectiveness of LLMs in Temporal User Profiling for Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Bamshad Mobasher, Kun Lin, Masoud Mansoury, Milad Sabouri","submitted_at":"2025-10-31T18:28:40Z","abstract_excerpt":"Effectively modeling the dynamic nature of user preferences is crucial for enhancing recommendation accuracy and fostering transparency in recommender systems. Traditional user profiling often overlooks the distinction between transitory short-term interests and stable long-term preferences. This paper examines the capability of leveraging Large Language Models (LLMs) to capture these temporal dynamics, generating richer user representations through distinct short-term and long-term textual summaries of interaction histories. Our observations suggest that while LLMs tend to improve recommendat"},"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":"2511.00176","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2025-10-31T18:28:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1e829bd19db375778ab82035c5d7ecaab756fcf6b62fb3350c726a7527a422a1","abstract_canon_sha256":"9ea67e75bea7148730e19e39ad545c8dccb1f634b635bfc8abf977f7353e2847"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T00:19:51.410218Z","signature_b64":"RMorlGQDrNN7nI6oa05XkqHGEV3Fpb/OyuW5cVIEBtYQ2OIGaySxqlhBu9A5MB2240m2JI12IOTGYKeO3EbQBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae0fd0c3525f886032ee6fdc206662752a6e8330764a810d331d7eb513d7ee09","last_reissued_at":"2026-07-21T00:19:51.409206Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T00:19:51.409206Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Effectiveness of LLMs in Temporal User Profiling for Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Bamshad Mobasher, Kun Lin, Masoud Mansoury, Milad Sabouri","submitted_at":"2025-10-31T18:28:40Z","abstract_excerpt":"Effectively modeling the dynamic nature of user preferences is crucial for enhancing recommendation accuracy and fostering transparency in recommender systems. Traditional user profiling often overlooks the distinction between transitory short-term interests and stable long-term preferences. This paper examines the capability of leveraging Large Language Models (LLMs) to capture these temporal dynamics, generating richer user representations through distinct short-term and long-term textual summaries of interaction histories. Our observations suggest that while LLMs tend to improve recommendat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.00176","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/2511.00176/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":"2511.00176","created_at":"2026-07-21T00:19:51.409681+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.00176v1","created_at":"2026-07-21T00:19:51.409681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.00176","created_at":"2026-07-21T00:19:51.409681+00:00"},{"alias_kind":"pith_short_12","alias_value":"VYH5BQ2SL6EG","created_at":"2026-07-21T00:19:51.409681+00:00"},{"alias_kind":"pith_short_16","alias_value":"VYH5BQ2SL6EGAMXO","created_at":"2026-07-21T00:19:51.409681+00:00"},{"alias_kind":"pith_short_8","alias_value":"VYH5BQ2S","created_at":"2026-07-21T00:19:51.409681+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/VYH5BQ2SL6EGAMXON7OCAZTCOU","json":"https://pith.science/pith/VYH5BQ2SL6EGAMXON7OCAZTCOU.json","graph_json":"https://pith.science/api/pith-number/VYH5BQ2SL6EGAMXON7OCAZTCOU/graph.json","events_json":"https://pith.science/api/pith-number/VYH5BQ2SL6EGAMXON7OCAZTCOU/events.json","paper":"https://pith.science/paper/VYH5BQ2S"},"agent_actions":{"view_html":"https://pith.science/pith/VYH5BQ2SL6EGAMXON7OCAZTCOU","download_json":"https://pith.science/pith/VYH5BQ2SL6EGAMXON7OCAZTCOU.json","view_paper":"https://pith.science/paper/VYH5BQ2S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.00176&json=true","fetch_graph":"https://pith.science/api/pith-number/VYH5BQ2SL6EGAMXON7OCAZTCOU/graph.json","fetch_events":"https://pith.science/api/pith-number/VYH5BQ2SL6EGAMXON7OCAZTCOU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VYH5BQ2SL6EGAMXON7OCAZTCOU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VYH5BQ2SL6EGAMXON7OCAZTCOU/action/storage_attestation","attest_author":"https://pith.science/pith/VYH5BQ2SL6EGAMXON7OCAZTCOU/action/author_attestation","sign_citation":"https://pith.science/pith/VYH5BQ2SL6EGAMXON7OCAZTCOU/action/citation_signature","submit_replication":"https://pith.science/pith/VYH5BQ2SL6EGAMXON7OCAZTCOU/action/replication_record"}},"created_at":"2026-07-21T00:19:51.409681+00:00","updated_at":"2026-07-21T00:19:51.409681+00:00"}