{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:NZHOHLWOWCCMZJKDB2KO34ROOC","short_pith_number":"pith:NZHOHLWO","canonical_record":{"source":{"id":"1804.06439","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-17T19:11:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cfc10f9349799ba80457a9be4d122747ebe51ba2c69da994b45bc0f1c0b74024","abstract_canon_sha256":"afe94fd3cd19b8a824ad18941dd738acf4ce0ee454977ed2c2918358c81c5d48"},"schema_version":"1.0"},"canonical_sha256":"6e4ee3aeceb084cca5430e94edf22e70aca340c23b59fbd830319cf0c160c578","source":{"kind":"arxiv","id":"1804.06439","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.06439","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"arxiv_version","alias_value":"1804.06439v3","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06439","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"pith_short_12","alias_value":"NZHOHLWOWCCM","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"NZHOHLWOWCCMZJKD","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"NZHOHLWO","created_at":"2026-05-18T12:32:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:NZHOHLWOWCCMZJKDB2KO34ROOC","target":"record","payload":{"canonical_record":{"source":{"id":"1804.06439","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-17T19:11:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cfc10f9349799ba80457a9be4d122747ebe51ba2c69da994b45bc0f1c0b74024","abstract_canon_sha256":"afe94fd3cd19b8a824ad18941dd738acf4ce0ee454977ed2c2918358c81c5d48"},"schema_version":"1.0"},"canonical_sha256":"6e4ee3aeceb084cca5430e94edf22e70aca340c23b59fbd830319cf0c160c578","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:16:21.006350Z","signature_b64":"UTKaxZpfBM/B8bzIIN7vCrUw+WT2zM9qmg6xijaoGelQJX/l6dXoDQ4jY62B+esqF5pEx74CoKWmFnns8YImCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e4ee3aeceb084cca5430e94edf22e70aca340c23b59fbd830319cf0c160c578","last_reissued_at":"2026-05-18T00:16:21.005758Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:16:21.005758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.06439","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:16:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SRK9IogwQx5RYErxqX5wG9r2yJery6clsOkg45QOrnDmLo0I3sRxBqcQGs8Z+bkd3XDG3p2oPFVJgyGV+onsDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T01:19:06.565734Z"},"content_sha256":"383e0a3af5728e52dbc1042a9851e66bcccdca5a84732bc642f3e0370535a126","schema_version":"1.0","event_id":"sha256:383e0a3af5728e52dbc1042a9851e66bcccdca5a84732bc642f3e0370535a126"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:NZHOHLWOWCCMZJKDB2KO34ROOC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Personalized neural language models for real-world query auto completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Nicolas Fiorini, Zhiyong Lu","submitted_at":"2018-04-17T19:11:14Z","abstract_excerpt":"Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query. Such systems update their suggestions after the user types each character, predicting the user's intent using various signals - one of the most common being popularity. Recently, deep learning approaches have been proposed for the QAC task, to specifically address the main limitation of previous popularity-based methods: the inability to predict unseen queries. In this work we improve previous methods based on neural language modeling, with the goal of building an end-to-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06439","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":""},"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":"2026-05-18T00:16:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CCRhfIqVaK73vjuaGOhdIJ3VgomQR7btyMcFMlmYT7EK7930zjKfwF4yesGv8TwY4szwaOHvYrRbNk6aCWxvDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T01:19:06.566085Z"},"content_sha256":"50f3215f147f165c7217fd731778c2d55b19024d68ef32cc780f21d80ecdf8f8","schema_version":"1.0","event_id":"sha256:50f3215f147f165c7217fd731778c2d55b19024d68ef32cc780f21d80ecdf8f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NZHOHLWOWCCMZJKDB2KO34ROOC/bundle.json","state_url":"https://pith.science/pith/NZHOHLWOWCCMZJKDB2KO34ROOC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NZHOHLWOWCCMZJKDB2KO34ROOC/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":"2026-06-02T01:19:06Z","links":{"resolver":"https://pith.science/pith/NZHOHLWOWCCMZJKDB2KO34ROOC","bundle":"https://pith.science/pith/NZHOHLWOWCCMZJKDB2KO34ROOC/bundle.json","state":"https://pith.science/pith/NZHOHLWOWCCMZJKDB2KO34ROOC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NZHOHLWOWCCMZJKDB2KO34ROOC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:NZHOHLWOWCCMZJKDB2KO34ROOC","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":"afe94fd3cd19b8a824ad18941dd738acf4ce0ee454977ed2c2918358c81c5d48","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-17T19:11:14Z","title_canon_sha256":"cfc10f9349799ba80457a9be4d122747ebe51ba2c69da994b45bc0f1c0b74024"},"schema_version":"1.0","source":{"id":"1804.06439","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.06439","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"arxiv_version","alias_value":"1804.06439v3","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06439","created_at":"2026-05-18T00:16:21Z"},{"alias_kind":"pith_short_12","alias_value":"NZHOHLWOWCCM","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"NZHOHLWOWCCMZJKD","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"NZHOHLWO","created_at":"2026-05-18T12:32:40Z"}],"graph_snapshots":[{"event_id":"sha256:50f3215f147f165c7217fd731778c2d55b19024d68ef32cc780f21d80ecdf8f8","target":"graph","created_at":"2026-05-18T00:16:21Z","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"},"paper":{"abstract_excerpt":"Query auto completion (QAC) systems are a standard part of search engines in industry, helping users formulate their query. Such systems update their suggestions after the user types each character, predicting the user's intent using various signals - one of the most common being popularity. Recently, deep learning approaches have been proposed for the QAC task, to specifically address the main limitation of previous popularity-based methods: the inability to predict unseen queries. In this work we improve previous methods based on neural language modeling, with the goal of building an end-to-","authors_text":"Nicolas Fiorini, Zhiyong Lu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-17T19:11:14Z","title":"Personalized neural language models for real-world query auto completion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06439","kind":"arxiv","version":3},"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":"sha256:383e0a3af5728e52dbc1042a9851e66bcccdca5a84732bc642f3e0370535a126","target":"record","created_at":"2026-05-18T00:16:21Z","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":"afe94fd3cd19b8a824ad18941dd738acf4ce0ee454977ed2c2918358c81c5d48","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-04-17T19:11:14Z","title_canon_sha256":"cfc10f9349799ba80457a9be4d122747ebe51ba2c69da994b45bc0f1c0b74024"},"schema_version":"1.0","source":{"id":"1804.06439","kind":"arxiv","version":3}},"canonical_sha256":"6e4ee3aeceb084cca5430e94edf22e70aca340c23b59fbd830319cf0c160c578","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6e4ee3aeceb084cca5430e94edf22e70aca340c23b59fbd830319cf0c160c578","first_computed_at":"2026-05-18T00:16:21.005758Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:16:21.005758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UTKaxZpfBM/B8bzIIN7vCrUw+WT2zM9qmg6xijaoGelQJX/l6dXoDQ4jY62B+esqF5pEx74CoKWmFnns8YImCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:16:21.006350Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.06439","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:383e0a3af5728e52dbc1042a9851e66bcccdca5a84732bc642f3e0370535a126","sha256:50f3215f147f165c7217fd731778c2d55b19024d68ef32cc780f21d80ecdf8f8"],"state_sha256":"fd734cf0b67528eeea0780bfb9c9a2010a53160853f9207c97250dac8b586d91"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wxJzY4q23sdcAubgmTMU4fwnCZ/ozIQtyMSZdg5kGi2P60xKDGxx/Wz9izg3gLjyVtnbVvQW7Tnu9rO7kq8BCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T01:19:06.568055Z","bundle_sha256":"2eb713b9022e174b48d889a418c2232b18307dfd466f9a59384671187ae99274"}}