{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:6I66KAUZK2MLYGAANTCOELWF66","short_pith_number":"pith:6I66KAUZ","canonical_record":{"source":{"id":"2209.09481","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-20T05:48:30Z","cross_cats_sorted":[],"title_canon_sha256":"abf00474d0ef90c85d13cea59bd425526b50bd11d4ab72c56e1cd42d4dbd14b1","abstract_canon_sha256":"c17ca66bd311ebc5ed04dcf0103a8c4b36235156df787650553d68443f85822c"},"schema_version":"1.0"},"canonical_sha256":"f23de502995698bc18006cc4e22ec5f798dddf6571090aee42f2fddd4c70243f","source":{"kind":"arxiv","id":"2209.09481","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.09481","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"2209.09481v1","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.09481","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"6I66KAUZK2ML","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"pith_short_16","alias_value":"6I66KAUZK2MLYGAA","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"pith_short_8","alias_value":"6I66KAUZ","created_at":"2026-07-05T04:59:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:6I66KAUZK2MLYGAANTCOELWF66","target":"record","payload":{"canonical_record":{"source":{"id":"2209.09481","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-20T05:48:30Z","cross_cats_sorted":[],"title_canon_sha256":"abf00474d0ef90c85d13cea59bd425526b50bd11d4ab72c56e1cd42d4dbd14b1","abstract_canon_sha256":"c17ca66bd311ebc5ed04dcf0103a8c4b36235156df787650553d68443f85822c"},"schema_version":"1.0"},"canonical_sha256":"f23de502995698bc18006cc4e22ec5f798dddf6571090aee42f2fddd4c70243f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:59:00.581779Z","signature_b64":"cvKBRyAu4UK4LZSwmso9gjH6lFE9xRVidQ9IhXMsWs4sRvXs9VFEQZ4UKUdriXzeyptIXlZ07Mx/Qgo5QU/VBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f23de502995698bc18006cc4e22ec5f798dddf6571090aee42f2fddd4c70243f","last_reissued_at":"2026-07-05T04:59:00.581358Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:59:00.581358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2209.09481","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":"2026-07-05T04:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dwBa7UNPjvpJe/u19cwCUXEoPyRzYPmaTj4+ovGmOTHsAfPs6jDlHwrajSY3x/dpf7kfFyP92M5CJN3k66raAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T15:57:50.273271Z"},"content_sha256":"4ada8a6d6257a4fbb405792edb31ad84228c2a4a3ec829b7ce0d1164efa77c2d","schema_version":"1.0","event_id":"sha256:4ada8a6d6257a4fbb405792edb31ad84228c2a4a3ec829b7ce0d1164efa77c2d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:6I66KAUZK2MLYGAANTCOELWF66","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Feature embedding in click-through rate prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Davorin Kopi\\v{c}, Jure Dem\\v{s}ar, Samo Pahor","submitted_at":"2022-09-20T05:48:30Z","abstract_excerpt":"We tackle the challenge of feature embedding for the purposes of improving the click-through rate prediction process. We select three models: logistic regression, factorization machines and deep factorization machines, as our baselines and propose five different feature embedding modules: embedding scaling, FM embedding, embedding encoding, NN embedding and the embedding reweighting module. The embedding modules act as a way to improve baseline model feature embeddings and are trained alongside the rest of the model parameters in an end-to-end manner. Each module is individually added to a bas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.09481","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/2209.09481/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":"2026-07-05T04:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+dRplJtzNBMV8Z8J0VYvDzyiDbYa8iGVsRU9qEfKrN992ZWjCVsrZQAD/q8hCnwSW+LKpgWq1RS09fLWv60UBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T15:57:50.274141Z"},"content_sha256":"c9734ef3c20e47655d52630b16f81eca95a2de5f01668b06df8f44c1eede194d","schema_version":"1.0","event_id":"sha256:c9734ef3c20e47655d52630b16f81eca95a2de5f01668b06df8f44c1eede194d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6I66KAUZK2MLYGAANTCOELWF66/bundle.json","state_url":"https://pith.science/pith/6I66KAUZK2MLYGAANTCOELWF66/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6I66KAUZK2MLYGAANTCOELWF66/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":"2026-07-05T15:57:50Z","links":{"resolver":"https://pith.science/pith/6I66KAUZK2MLYGAANTCOELWF66","bundle":"https://pith.science/pith/6I66KAUZK2MLYGAANTCOELWF66/bundle.json","state":"https://pith.science/pith/6I66KAUZK2MLYGAANTCOELWF66/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6I66KAUZK2MLYGAANTCOELWF66/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:6I66KAUZK2MLYGAANTCOELWF66","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":"c17ca66bd311ebc5ed04dcf0103a8c4b36235156df787650553d68443f85822c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-20T05:48:30Z","title_canon_sha256":"abf00474d0ef90c85d13cea59bd425526b50bd11d4ab72c56e1cd42d4dbd14b1"},"schema_version":"1.0","source":{"id":"2209.09481","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2209.09481","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"2209.09481v1","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.09481","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"6I66KAUZK2ML","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"pith_short_16","alias_value":"6I66KAUZK2MLYGAA","created_at":"2026-07-05T04:59:00Z"},{"alias_kind":"pith_short_8","alias_value":"6I66KAUZ","created_at":"2026-07-05T04:59:00Z"}],"graph_snapshots":[{"event_id":"sha256:c9734ef3c20e47655d52630b16f81eca95a2de5f01668b06df8f44c1eede194d","target":"graph","created_at":"2026-07-05T04:59:00Z","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/2209.09481/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We tackle the challenge of feature embedding for the purposes of improving the click-through rate prediction process. We select three models: logistic regression, factorization machines and deep factorization machines, as our baselines and propose five different feature embedding modules: embedding scaling, FM embedding, embedding encoding, NN embedding and the embedding reweighting module. The embedding modules act as a way to improve baseline model feature embeddings and are trained alongside the rest of the model parameters in an end-to-end manner. Each module is individually added to a bas","authors_text":"Davorin Kopi\\v{c}, Jure Dem\\v{s}ar, Samo Pahor","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-20T05:48:30Z","title":"Feature embedding in click-through rate prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.09481","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":"sha256:4ada8a6d6257a4fbb405792edb31ad84228c2a4a3ec829b7ce0d1164efa77c2d","target":"record","created_at":"2026-07-05T04:59:00Z","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":"c17ca66bd311ebc5ed04dcf0103a8c4b36235156df787650553d68443f85822c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-09-20T05:48:30Z","title_canon_sha256":"abf00474d0ef90c85d13cea59bd425526b50bd11d4ab72c56e1cd42d4dbd14b1"},"schema_version":"1.0","source":{"id":"2209.09481","kind":"arxiv","version":1}},"canonical_sha256":"f23de502995698bc18006cc4e22ec5f798dddf6571090aee42f2fddd4c70243f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f23de502995698bc18006cc4e22ec5f798dddf6571090aee42f2fddd4c70243f","first_computed_at":"2026-07-05T04:59:00.581358Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:59:00.581358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cvKBRyAu4UK4LZSwmso9gjH6lFE9xRVidQ9IhXMsWs4sRvXs9VFEQZ4UKUdriXzeyptIXlZ07Mx/Qgo5QU/VBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T04:59:00.581779Z","signed_message":"canonical_sha256_bytes"},"source_id":"2209.09481","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ada8a6d6257a4fbb405792edb31ad84228c2a4a3ec829b7ce0d1164efa77c2d","sha256:c9734ef3c20e47655d52630b16f81eca95a2de5f01668b06df8f44c1eede194d"],"state_sha256":"936a1c024222c3a87aec8b36920aa09728cfd1619c1df94668a09976e39d6660"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"76X2h/yCLqsRr+9561injCmKLMgUQhJenEgayuwUo1RougvQ8JW8ywTC2XtlogmQBZXFV1/NDvjQCM7WDhB9Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T15:57:50.278684Z","bundle_sha256":"5e9f0f6464e7efe02329c08dd7acd7d1724ff9caa5a5f797a43634417d84ad04"}}