{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:HPOAJZ2T7AJ344DDLTT55M22FO","short_pith_number":"pith:HPOAJZ2T","canonical_record":{"source":{"id":"2402.13750","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-02-21T12:22:01Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"f0d6c2a5c1e03af538d36a9485f1f5ca955d3f86d11c529b9f75a74b96abd38c","abstract_canon_sha256":"3a4483c7f11f4504eb2fad8982983eac5a4bbdf7fa6c083b41cb80fbb329b7b5"},"schema_version":"1.0"},"canonical_sha256":"3bdc04e753f813be70635ce7deb35a2b9d75663500e3638ba4040e8a0665d93e","source":{"kind":"arxiv","id":"2402.13750","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.13750","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"arxiv_version","alias_value":"2402.13750v1","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.13750","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"pith_short_12","alias_value":"HPOAJZ2T7AJ3","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"pith_short_16","alias_value":"HPOAJZ2T7AJ344DD","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"pith_short_8","alias_value":"HPOAJZ2T","created_at":"2026-07-05T07:47:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:HPOAJZ2T7AJ344DDLTT55M22FO","target":"record","payload":{"canonical_record":{"source":{"id":"2402.13750","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-02-21T12:22:01Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"f0d6c2a5c1e03af538d36a9485f1f5ca955d3f86d11c529b9f75a74b96abd38c","abstract_canon_sha256":"3a4483c7f11f4504eb2fad8982983eac5a4bbdf7fa6c083b41cb80fbb329b7b5"},"schema_version":"1.0"},"canonical_sha256":"3bdc04e753f813be70635ce7deb35a2b9d75663500e3638ba4040e8a0665d93e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:48.547739Z","signature_b64":"Hf0KIl1KAFwtEiL/hn7H4SZke04lUeaixh+hQvQI55e2EJDEZS5hTr/7zNebCEKDjyPaUW2RYTlzHTcucJieBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3bdc04e753f813be70635ce7deb35a2b9d75663500e3638ba4040e8a0665d93e","last_reissued_at":"2026-07-05T07:47:48.547274Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:48.547274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.13750","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:47:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0el0bbbh9Wcojuv5QGpto9XA7oq3qIrIrTiI12Y59VJNEEdLUHApoVqFthnxeaLKyZmdtz/0oEPR91Vlj2kBCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:51:46.241414Z"},"content_sha256":"763baa6fb94f6d71cb454a9969e370bc2e1b14799169fead2242793964225535","schema_version":"1.0","event_id":"sha256:763baa6fb94f6d71cb454a9969e370bc2e1b14799169fead2242793964225535"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:HPOAJZ2T7AJ344DDLTT55M22FO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Gong-Duo Zhang, Hao Qian, Lihong Gu, Qian Zhao, Ziqi Liu","submitted_at":"2024-02-21T12:22:01Z","abstract_excerpt":"Recommendation systems are widely used in e-commerce websites and online platforms to address information overload. However, existing systems primarily rely on historical data and user feedback, making it difficult to capture user intent transitions. Recently, Knowledge Base (KB)-based models are proposed to incorporate expert knowledge, but it struggle to adapt to new items and the evolving e-commerce environment. To address these challenges, we propose a novel Large Language Model based Complementary Knowledge Enhanced Recommendation System (LLM-KERec). It introduces an entity extractor that"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.13750","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/2402.13750/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:47:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dLgjATVyL5pvW+r50BGlCpt4ssDn2o5rrv4QWLxTEWSqkrXWKXIbPo2fpE1SUoHOEgm5PokSY+Gae4OktmoWDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:51:46.242305Z"},"content_sha256":"d307d50dbaa63f279a75e7e5871963a1c9383372ff29d296ee0167f2374fd069","schema_version":"1.0","event_id":"sha256:d307d50dbaa63f279a75e7e5871963a1c9383372ff29d296ee0167f2374fd069"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HPOAJZ2T7AJ344DDLTT55M22FO/bundle.json","state_url":"https://pith.science/pith/HPOAJZ2T7AJ344DDLTT55M22FO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HPOAJZ2T7AJ344DDLTT55M22FO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T22:51:46Z","links":{"resolver":"https://pith.science/pith/HPOAJZ2T7AJ344DDLTT55M22FO","bundle":"https://pith.science/pith/HPOAJZ2T7AJ344DDLTT55M22FO/bundle.json","state":"https://pith.science/pith/HPOAJZ2T7AJ344DDLTT55M22FO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HPOAJZ2T7AJ344DDLTT55M22FO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HPOAJZ2T7AJ344DDLTT55M22FO","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3a4483c7f11f4504eb2fad8982983eac5a4bbdf7fa6c083b41cb80fbb329b7b5","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-02-21T12:22:01Z","title_canon_sha256":"f0d6c2a5c1e03af538d36a9485f1f5ca955d3f86d11c529b9f75a74b96abd38c"},"schema_version":"1.0","source":{"id":"2402.13750","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.13750","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"arxiv_version","alias_value":"2402.13750v1","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.13750","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"pith_short_12","alias_value":"HPOAJZ2T7AJ3","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"pith_short_16","alias_value":"HPOAJZ2T7AJ344DD","created_at":"2026-07-05T07:47:48Z"},{"alias_kind":"pith_short_8","alias_value":"HPOAJZ2T","created_at":"2026-07-05T07:47:48Z"}],"graph_snapshots":[{"event_id":"sha256:d307d50dbaa63f279a75e7e5871963a1c9383372ff29d296ee0167f2374fd069","target":"graph","created_at":"2026-07-05T07:47:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2402.13750/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recommendation systems are widely used in e-commerce websites and online platforms to address information overload. However, existing systems primarily rely on historical data and user feedback, making it difficult to capture user intent transitions. Recently, Knowledge Base (KB)-based models are proposed to incorporate expert knowledge, but it struggle to adapt to new items and the evolving e-commerce environment. To address these challenges, we propose a novel Large Language Model based Complementary Knowledge Enhanced Recommendation System (LLM-KERec). It introduces an entity extractor that","authors_text":"Gong-Duo Zhang, Hao Qian, Lihong Gu, Qian Zhao, Ziqi Liu","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-02-21T12:22:01Z","title":"Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.13750","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:763baa6fb94f6d71cb454a9969e370bc2e1b14799169fead2242793964225535","target":"record","created_at":"2026-07-05T07:47:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3a4483c7f11f4504eb2fad8982983eac5a4bbdf7fa6c083b41cb80fbb329b7b5","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-02-21T12:22:01Z","title_canon_sha256":"f0d6c2a5c1e03af538d36a9485f1f5ca955d3f86d11c529b9f75a74b96abd38c"},"schema_version":"1.0","source":{"id":"2402.13750","kind":"arxiv","version":1}},"canonical_sha256":"3bdc04e753f813be70635ce7deb35a2b9d75663500e3638ba4040e8a0665d93e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3bdc04e753f813be70635ce7deb35a2b9d75663500e3638ba4040e8a0665d93e","first_computed_at":"2026-07-05T07:47:48.547274Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:47:48.547274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Hf0KIl1KAFwtEiL/hn7H4SZke04lUeaixh+hQvQI55e2EJDEZS5hTr/7zNebCEKDjyPaUW2RYTlzHTcucJieBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:47:48.547739Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.13750","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:763baa6fb94f6d71cb454a9969e370bc2e1b14799169fead2242793964225535","sha256:d307d50dbaa63f279a75e7e5871963a1c9383372ff29d296ee0167f2374fd069"],"state_sha256":"bb29ea608d8ee1fe43871c81b1bde1b82a12d2a64665c31309da3b2632f73080"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z0zOLKpsXdv4e/id5qfHA+WrNcmX5gXC+pCXFlMqJc2IEpICS7jU0lRPTQJIJ8JdP2BEyHdK5BbLdeq1CvkVDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:51:46.248507Z","bundle_sha256":"5404bc21008556721d9404dcc2bd23b00f35bd80a6524d5de512bc212d6c24c1"}}