{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:62PZJC4QQX23J63QEKQVMIHM26","short_pith_number":"pith:62PZJC4Q","schema_version":"1.0","canonical_sha256":"f69f948b9085f5b4fb7022a15620ecd78457dd6d0d8a0b4109d370b2428d9f1f","source":{"kind":"arxiv","id":"2312.09631","version":3},"attestation_state":"computed","paper":{"title":"Context-Driven Interactive Query Simulations Based on Generative Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bj\\\"orn Engelmann, Jana Isabelle Friese, Norbert Fuhr, Philipp Schaer, Timo Breuer","submitted_at":"2023-12-15T09:21:11Z","abstract_excerpt":"Simulating user interactions enables a more user-oriented evaluation of information retrieval (IR) systems. While user simulations are cost-efficient and reproducible, many approaches often lack fidelity regarding real user behavior. Most notably, current user models neglect the user's context, which is the primary driver of perceived relevance and the interactions with the search results. To this end, this work introduces the simulation of context-driven query reformulations. The proposed query generation methods build upon recent Large Language Model (LLM) approaches and consider the user's "},"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":"2312.09631","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-12-15T09:21:11Z","cross_cats_sorted":[],"title_canon_sha256":"1c7b1dbbf2834043795ba7f246784defb2be103a3c5baea9cb8086a1595284a9","abstract_canon_sha256":"6ff71647990a2482ac32fac7066cbee47b25e1eca1682cc8190c412e69e6bf29"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:37:53.852922Z","signature_b64":"fd/MCbOWXoqydM0Y+6KFplJrRIP40kt2gaiB7HtulIXFXGtzCv1Mv9k4z1V6Pz/VgDufXkLEQSZ04tOYgVj9BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f69f948b9085f5b4fb7022a15620ecd78457dd6d0d8a0b4109d370b2428d9f1f","last_reissued_at":"2026-07-05T07:37:53.852464Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:37:53.852464Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Context-Driven Interactive Query Simulations Based on Generative Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Bj\\\"orn Engelmann, Jana Isabelle Friese, Norbert Fuhr, Philipp Schaer, Timo Breuer","submitted_at":"2023-12-15T09:21:11Z","abstract_excerpt":"Simulating user interactions enables a more user-oriented evaluation of information retrieval (IR) systems. While user simulations are cost-efficient and reproducible, many approaches often lack fidelity regarding real user behavior. Most notably, current user models neglect the user's context, which is the primary driver of perceived relevance and the interactions with the search results. To this end, this work introduces the simulation of context-driven query reformulations. The proposed query generation methods build upon recent Large Language Model (LLM) approaches and consider the user's "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.09631","kind":"arxiv","version":3},"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/2312.09631/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":"2312.09631","created_at":"2026-07-05T07:37:53.852521+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.09631v3","created_at":"2026-07-05T07:37:53.852521+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.09631","created_at":"2026-07-05T07:37:53.852521+00:00"},{"alias_kind":"pith_short_12","alias_value":"62PZJC4QQX23","created_at":"2026-07-05T07:37:53.852521+00:00"},{"alias_kind":"pith_short_16","alias_value":"62PZJC4QQX23J63Q","created_at":"2026-07-05T07:37:53.852521+00:00"},{"alias_kind":"pith_short_8","alias_value":"62PZJC4Q","created_at":"2026-07-05T07:37:53.852521+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.23406","citing_title":"IIRSim Studio: A Dashboard for User Simulation","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26","json":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26.json","graph_json":"https://pith.science/api/pith-number/62PZJC4QQX23J63QEKQVMIHM26/graph.json","events_json":"https://pith.science/api/pith-number/62PZJC4QQX23J63QEKQVMIHM26/events.json","paper":"https://pith.science/paper/62PZJC4Q"},"agent_actions":{"view_html":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26","download_json":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26.json","view_paper":"https://pith.science/paper/62PZJC4Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.09631&json=true","fetch_graph":"https://pith.science/api/pith-number/62PZJC4QQX23J63QEKQVMIHM26/graph.json","fetch_events":"https://pith.science/api/pith-number/62PZJC4QQX23J63QEKQVMIHM26/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26/action/timestamp_anchor","attest_storage":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26/action/storage_attestation","attest_author":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26/action/author_attestation","sign_citation":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26/action/citation_signature","submit_replication":"https://pith.science/pith/62PZJC4QQX23J63QEKQVMIHM26/action/replication_record"}},"created_at":"2026-07-05T07:37:53.852521+00:00","updated_at":"2026-07-05T07:37:53.852521+00:00"}