{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:HW4NQQASS3F6IBD4HKHQMPRZNY","short_pith_number":"pith:HW4NQQAS","schema_version":"1.0","canonical_sha256":"3db8d8401296cbe4047c3a8f063e396e084b072b6afe2be66cf8de42b39ede70","source":{"kind":"arxiv","id":"2605.13497","version":1},"attestation_state":"computed","paper":{"title":"Task-Aware Automated User Profile Generation for Recommendation Simulation Using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"APG4RecSim automatically generates realistic user profiles for LLM-based recommender simulations with minimal supervision.","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chenglong Ma, Danula Hettiachchi, Jeffrey Chan, Xinye Wanyan, Ziqi Xu","submitted_at":"2026-05-13T13:20:39Z","abstract_excerpt":"Large Language Model (LLM)-based agent simulation has emerged as a promising approach to meet the increasing demand for real-time and rigorous evaluation in modern recommender systems. A typical LLM-driven simulation framework comprises three essential components: the profile module, memory module, and action module. However, existing studies have primarily concentrated on enhancing the memory and action modules, with limited attention to profile generation, which plays a pivotal role in ensuring realistic agent behaviours and aligning simulated interactions with real user dynamics. Moreover, "},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.13497","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-13T13:20:39Z","cross_cats_sorted":[],"title_canon_sha256":"9291bb7dd19f9e058c3508d21df2cabeecb5eb43dbdc2cbf061252264a00fd36","abstract_canon_sha256":"2b59c795f02340e2c0b340672a5b3aadeab86bb9f041a31093428cfb7eecb441"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:41.070577Z","signature_b64":"RtlMu329eJyO+trPa/6QjTTLiO64K5xMcslEEi9S1/6bMjbXzmHRAYaX8H8rxeUyy4iCzaY0gqaJCkNrCVwADw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3db8d8401296cbe4047c3a8f063e396e084b072b6afe2be66cf8de42b39ede70","last_reissued_at":"2026-05-18T02:44:41.069912Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:41.069912Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Task-Aware Automated User Profile Generation for Recommendation Simulation Using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"APG4RecSim automatically generates realistic user profiles for LLM-based recommender simulations with minimal supervision.","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Chenglong Ma, Danula Hettiachchi, Jeffrey Chan, Xinye Wanyan, Ziqi Xu","submitted_at":"2026-05-13T13:20:39Z","abstract_excerpt":"Large Language Model (LLM)-based agent simulation has emerged as a promising approach to meet the increasing demand for real-time and rigorous evaluation in modern recommender systems. A typical LLM-driven simulation framework comprises three essential components: the profile module, memory module, and action module. However, existing studies have primarily concentrated on enhancing the memory and action modules, with limited attention to profile generation, which plays a pivotal role in ensuring realistic agent behaviours and aligning simulated interactions with real user dynamics. Moreover, "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"APG4RecSim achieves the best overall performance on discrimination, ranking, and rating tasks, improving ranking quality by up to 7% in nDCG@10 and reducing rating distribution divergence by 8% in JSD compared to existing profile-generation baselines.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That profiles generated by the LLM with minimal supervision accurately capture real user dynamics and produce simulated interactions that align with actual user behavior across datasets.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"APG4RecSim automatically generates realistic user profiles for LLM-based recommendation simulations, outperforming manual baselines by up to 7% in nDCG@10 and 8% in JSD on three benchmark datasets.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"APG4RecSim automatically generates realistic user profiles for LLM-based recommender simulations with minimal supervision.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d25f85a076a745c43c289788c4d48c76b2a74f0f727e17cf5ed8498b3c75fa9e"},"source":{"id":"2605.13497","kind":"arxiv","version":1},"verdict":{"id":"a832b348-b149-46d4-8665-8e71a7f96785","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T18:18:02.132887Z","strongest_claim":"APG4RecSim achieves the best overall performance on discrimination, ranking, and rating tasks, improving ranking quality by up to 7% in nDCG@10 and reducing rating distribution divergence by 8% in JSD compared to existing profile-generation baselines.","one_line_summary":"APG4RecSim automatically generates realistic user profiles for LLM-based recommendation simulations, outperforming manual baselines by up to 7% in nDCG@10 and 8% in JSD on three benchmark datasets.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That profiles generated by the LLM with minimal supervision accurately capture real user dynamics and produce simulated interactions that align with actual user behavior across datasets.","pith_extraction_headline":"APG4RecSim automatically generates realistic user profiles for LLM-based recommender simulations with minimal supervision."},"references":{"count":51,"sample":[{"doi":"10.1145/3450613","year":2021,"title":"Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher, and Edward Malthouse. 2021. User-centered Evaluation of Popularity Bias in Recom- mender Systems. InProceedings of the 29th ACM Con","work_id":"486bb563-eb99-467e-91da-2ca1040c4284","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1016/j.knosys.2016.03.006","year":2016,"title":"Mohammad Yahya H. Al-Shamri. 2016. User profiling approaches for demo- graphic recommender systems.Know.-Based Syst.100, C (May 2016), 175–187. doi:10.1016/j.knosys.2016.03.006","work_id":"ead7636a-b726-471a-ab37-7eaa13d48705","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1145/3613904.3642081","year":2024,"title":"InProceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24)","work_id":"de206c3b-7e99-4e52-8b63-f058b0773327","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.18653/v1/2025.acl-","year":2025,"title":"In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","work_id":"1ef694be-4483-4d1c-b319-30be63d024de","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramèr, and Chiyuan Zhang. 2023. Quantifying Memorization Across Neu- ral Language Models. InThe Eleventh International Con","work_id":"f616300e-1733-4ee6-8062-63a9356edbc3","ref_index":6,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":51,"snapshot_sha256":"0c857abd8e00f2ba03f631566ca1c566987407b5a1840cfabb1071a69cc678d3","internal_anchors":1},"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":"2605.13497","created_at":"2026-05-18T02:44:41.070007+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.13497v1","created_at":"2026-05-18T02:44:41.070007+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13497","created_at":"2026-05-18T02:44:41.070007+00:00"},{"alias_kind":"pith_short_12","alias_value":"HW4NQQASS3F6","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"HW4NQQASS3F6IBD4","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"HW4NQQAS","created_at":"2026-05-18T12:33:37.589309+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/HW4NQQASS3F6IBD4HKHQMPRZNY","json":"https://pith.science/pith/HW4NQQASS3F6IBD4HKHQMPRZNY.json","graph_json":"https://pith.science/api/pith-number/HW4NQQASS3F6IBD4HKHQMPRZNY/graph.json","events_json":"https://pith.science/api/pith-number/HW4NQQASS3F6IBD4HKHQMPRZNY/events.json","paper":"https://pith.science/paper/HW4NQQAS"},"agent_actions":{"view_html":"https://pith.science/pith/HW4NQQASS3F6IBD4HKHQMPRZNY","download_json":"https://pith.science/pith/HW4NQQASS3F6IBD4HKHQMPRZNY.json","view_paper":"https://pith.science/paper/HW4NQQAS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.13497&json=true","fetch_graph":"https://pith.science/api/pith-number/HW4NQQASS3F6IBD4HKHQMPRZNY/graph.json","fetch_events":"https://pith.science/api/pith-number/HW4NQQASS3F6IBD4HKHQMPRZNY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HW4NQQASS3F6IBD4HKHQMPRZNY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HW4NQQASS3F6IBD4HKHQMPRZNY/action/storage_attestation","attest_author":"https://pith.science/pith/HW4NQQASS3F6IBD4HKHQMPRZNY/action/author_attestation","sign_citation":"https://pith.science/pith/HW4NQQASS3F6IBD4HKHQMPRZNY/action/citation_signature","submit_replication":"https://pith.science/pith/HW4NQQASS3F6IBD4HKHQMPRZNY/action/replication_record"}},"created_at":"2026-05-18T02:44:41.070007+00:00","updated_at":"2026-05-18T02:44:41.070007+00:00"}