{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KHY42OPEE4MG2KARTTDSXO5G45","short_pith_number":"pith:KHY42OPE","schema_version":"1.0","canonical_sha256":"51f1cd39e427186d28119cc72bbba6e77fd6877b5de0b7deef4c58d6f4181aef","source":{"kind":"arxiv","id":"2406.02377","version":2},"attestation_state":"computed","paper":{"title":"XRec: Large Language Models for Explainable Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Chao Huang, Qiyao Ma, Xubin Ren","submitted_at":"2024-06-04T14:55:14Z","abstract_excerpt":"Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adopted approach, but while advanced techniques like graph neural networks (GNNs) and self-supervised learning (SSL) have enhanced CF models for better user representations, they often lack the ability to provide explanations for the recommended items. Explainable recommendations aim to address this gap by offering transparency and insights into the recommendation decision-making process, enhancing users' understanding. "},"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":"2406.02377","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-06-04T14:55:14Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"d71854e70f2d57c965a1a96f0f8566f5676508bfa244ddc6beeca47bf1ce4d4f","abstract_canon_sha256":"b2b46795e52fb5acc4248369957538a0a4aeca8f955d8607c660532d4e3399b8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:10:00.236385Z","signature_b64":"BH1GUK9QZ9hfJlzCji8IV0hJBQqRLT662ka8eUtqmtxvwHg2yN5cohJM2fIFTkHEMu8EyyK+wekTMfYkN3QwCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51f1cd39e427186d28119cc72bbba6e77fd6877b5de0b7deef4c58d6f4181aef","last_reissued_at":"2026-07-05T09:10:00.235852Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:10:00.235852Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XRec: Large Language Models for Explainable Recommendation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Chao Huang, Qiyao Ma, Xubin Ren","submitted_at":"2024-06-04T14:55:14Z","abstract_excerpt":"Recommender systems help users navigate information overload by providing personalized recommendations aligned with their preferences. Collaborative Filtering (CF) is a widely adopted approach, but while advanced techniques like graph neural networks (GNNs) and self-supervised learning (SSL) have enhanced CF models for better user representations, they often lack the ability to provide explanations for the recommended items. Explainable recommendations aim to address this gap by offering transparency and insights into the recommendation decision-making process, enhancing users' understanding. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02377","kind":"arxiv","version":2},"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/2406.02377/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":"2406.02377","created_at":"2026-07-05T09:10:00.235914+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.02377v2","created_at":"2026-07-05T09:10:00.235914+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02377","created_at":"2026-07-05T09:10:00.235914+00:00"},{"alias_kind":"pith_short_12","alias_value":"KHY42OPEE4MG","created_at":"2026-07-05T09:10:00.235914+00:00"},{"alias_kind":"pith_short_16","alias_value":"KHY42OPEE4MG2KAR","created_at":"2026-07-05T09:10:00.235914+00:00"},{"alias_kind":"pith_short_8","alias_value":"KHY42OPE","created_at":"2026-07-05T09:10:00.235914+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00540","citing_title":"Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges","ref_index":158,"is_internal_anchor":false},{"citing_arxiv_id":"2512.24366","citing_title":"On the Factual Consistency of Text-based Explainable Recommendation Models","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03666","citing_title":"MMP-Refer: Multimodal Path Retrieval-augmented LLMs For Explainable Recommendation","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03724","citing_title":"Rank, Don't Generate: Statement-level Ranking for Explainable Recommendation","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2604.05341","citing_title":"Curr-RLCER:Curriculum Reinforcement Learning For Coherence Explainable Recommendation","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45","json":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45.json","graph_json":"https://pith.science/api/pith-number/KHY42OPEE4MG2KARTTDSXO5G45/graph.json","events_json":"https://pith.science/api/pith-number/KHY42OPEE4MG2KARTTDSXO5G45/events.json","paper":"https://pith.science/paper/KHY42OPE"},"agent_actions":{"view_html":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45","download_json":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45.json","view_paper":"https://pith.science/paper/KHY42OPE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.02377&json=true","fetch_graph":"https://pith.science/api/pith-number/KHY42OPEE4MG2KARTTDSXO5G45/graph.json","fetch_events":"https://pith.science/api/pith-number/KHY42OPEE4MG2KARTTDSXO5G45/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45/action/storage_attestation","attest_author":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45/action/author_attestation","sign_citation":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45/action/citation_signature","submit_replication":"https://pith.science/pith/KHY42OPEE4MG2KARTTDSXO5G45/action/replication_record"}},"created_at":"2026-07-05T09:10:00.235914+00:00","updated_at":"2026-07-05T09:10:00.235914+00:00"}