{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:UWLZ5RQV6GUJYRKYHYQOG5BG54","short_pith_number":"pith:UWLZ5RQV","canonical_record":{"source":{"id":"1805.08720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T16:26:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"477425529bd1d973a06c0b538bf091323e430d1e35caa5fb80cdb013fafb67af","abstract_canon_sha256":"6859c620b15ec8b727827d04521fd17541393a257680bb9303f7a3f72acce021"},"schema_version":"1.0"},"canonical_sha256":"a5979ec615f1a89c45583e20e37426ef3478574ef2ae7cba34daaa2e5c66522f","source":{"kind":"arxiv","id":"1805.08720","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08720","created_at":"2026-05-18T00:15:25Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08720v1","created_at":"2026-05-18T00:15:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08720","created_at":"2026-05-18T00:15:25Z"},{"alias_kind":"pith_short_12","alias_value":"UWLZ5RQV6GUJ","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"UWLZ5RQV6GUJYRKY","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"UWLZ5RQV","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:UWLZ5RQV6GUJYRKYHYQOG5BG54","target":"record","payload":{"canonical_record":{"source":{"id":"1805.08720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T16:26:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"477425529bd1d973a06c0b538bf091323e430d1e35caa5fb80cdb013fafb67af","abstract_canon_sha256":"6859c620b15ec8b727827d04521fd17541393a257680bb9303f7a3f72acce021"},"schema_version":"1.0"},"canonical_sha256":"a5979ec615f1a89c45583e20e37426ef3478574ef2ae7cba34daaa2e5c66522f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:25.264858Z","signature_b64":"kaCr6L2IJiyTrbuEFzcCUOZe5Mvy0xeUJxdenO9Mg7qsXG5cMle7kcn92v2J1vZ5jcRiUlrcNKJEek54skl0Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5979ec615f1a89c45583e20e37426ef3478574ef2ae7cba34daaa2e5c66522f","last_reissued_at":"2026-05-18T00:15:25.264346Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:25.264346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.08720","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":"2026-05-18T00:15:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wW/hMbQyBUMRuxMVTqoFoVtl2KK92lnLkFbWw4athkfn6Xn2Ln7m0JfETbmBrwTPIhD2TUdHr5cRZeF+uAv7AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T05:05:03.767423Z"},"content_sha256":"1b0238962df8f6e692e97c00d39b233964993072e7a4c44bbaeb9e7ecff5c8bf","schema_version":"1.0","event_id":"sha256:1b0238962df8f6e692e97c00d39b233964993072e7a4c44bbaeb9e7ecff5c8bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:UWLZ5RQV6GUJYRKYHYQOG5BG54","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adversarial Training of Word2Vec for Basket Completion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Flavian Vasile, Mike Gartrell, Ugo Tanielian","submitted_at":"2018-05-22T16:26:50Z","abstract_excerpt":"In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language modeling tasks, such as word analogy and word similarity, and in recommendation tasks, through Prod2Vec, an extension that applies to modeling user shopping activity and user preferences. Several methods that aim to improve upon the standard Negative Sampling loss have been proposed. In our paper we pursue more sophisticated Negative Sampling, by leveraging ideas from the field of Generative Adversarial Networks (GANs)"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08720","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":""},"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":"2026-05-18T00:15:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ntDG1mwrJMcUaIB6Dwh+q1AErR/fHnnyuyfdL4lLsJ7ej+Ppu6H8eX61c1/LKdeG0Xm1a7RjbTfpXxWmOOxnAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T05:05:03.767905Z"},"content_sha256":"4308fc859c6ac50d3f49aeb797cfdb8cbfb80aef067e8e2542103908d09b0687","schema_version":"1.0","event_id":"sha256:4308fc859c6ac50d3f49aeb797cfdb8cbfb80aef067e8e2542103908d09b0687"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UWLZ5RQV6GUJYRKYHYQOG5BG54/bundle.json","state_url":"https://pith.science/pith/UWLZ5RQV6GUJYRKYHYQOG5BG54/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UWLZ5RQV6GUJYRKYHYQOG5BG54/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":"2026-05-28T05:05:03Z","links":{"resolver":"https://pith.science/pith/UWLZ5RQV6GUJYRKYHYQOG5BG54","bundle":"https://pith.science/pith/UWLZ5RQV6GUJYRKYHYQOG5BG54/bundle.json","state":"https://pith.science/pith/UWLZ5RQV6GUJYRKYHYQOG5BG54/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UWLZ5RQV6GUJYRKYHYQOG5BG54/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:UWLZ5RQV6GUJYRKYHYQOG5BG54","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":"6859c620b15ec8b727827d04521fd17541393a257680bb9303f7a3f72acce021","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T16:26:50Z","title_canon_sha256":"477425529bd1d973a06c0b538bf091323e430d1e35caa5fb80cdb013fafb67af"},"schema_version":"1.0","source":{"id":"1805.08720","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.08720","created_at":"2026-05-18T00:15:25Z"},{"alias_kind":"arxiv_version","alias_value":"1805.08720v1","created_at":"2026-05-18T00:15:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08720","created_at":"2026-05-18T00:15:25Z"},{"alias_kind":"pith_short_12","alias_value":"UWLZ5RQV6GUJ","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"UWLZ5RQV6GUJYRKY","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"UWLZ5RQV","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:4308fc859c6ac50d3f49aeb797cfdb8cbfb80aef067e8e2542103908d09b0687","target":"graph","created_at":"2026-05-18T00:15:25Z","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":"In recent years, the Word2Vec model trained with the Negative Sampling loss function has shown state-of-the-art results in a number of machine learning tasks, including language modeling tasks, such as word analogy and word similarity, and in recommendation tasks, through Prod2Vec, an extension that applies to modeling user shopping activity and user preferences. Several methods that aim to improve upon the standard Negative Sampling loss have been proposed. In our paper we pursue more sophisticated Negative Sampling, by leveraging ideas from the field of Generative Adversarial Networks (GANs)","authors_text":"Flavian Vasile, Mike Gartrell, Ugo Tanielian","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T16:26:50Z","title":"Adversarial Training of Word2Vec for Basket Completion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08720","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":"sha256:1b0238962df8f6e692e97c00d39b233964993072e7a4c44bbaeb9e7ecff5c8bf","target":"record","created_at":"2026-05-18T00:15:25Z","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":"6859c620b15ec8b727827d04521fd17541393a257680bb9303f7a3f72acce021","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-05-22T16:26:50Z","title_canon_sha256":"477425529bd1d973a06c0b538bf091323e430d1e35caa5fb80cdb013fafb67af"},"schema_version":"1.0","source":{"id":"1805.08720","kind":"arxiv","version":1}},"canonical_sha256":"a5979ec615f1a89c45583e20e37426ef3478574ef2ae7cba34daaa2e5c66522f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5979ec615f1a89c45583e20e37426ef3478574ef2ae7cba34daaa2e5c66522f","first_computed_at":"2026-05-18T00:15:25.264346Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:15:25.264346Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kaCr6L2IJiyTrbuEFzcCUOZe5Mvy0xeUJxdenO9Mg7qsXG5cMle7kcn92v2J1vZ5jcRiUlrcNKJEek54skl0Dw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:15:25.264858Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.08720","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b0238962df8f6e692e97c00d39b233964993072e7a4c44bbaeb9e7ecff5c8bf","sha256:4308fc859c6ac50d3f49aeb797cfdb8cbfb80aef067e8e2542103908d09b0687"],"state_sha256":"9713d8c54b805042685fbac3ff745f146e8deac366fbc268ca1816b0269b4116"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7NZ8GDt92V+DZYaqZj2zy9b0fx7ra71E769vhioncC2QIPe8wZjfRoDGeQo+/cNUcvDEhBMVkNp1vmxuCkqhBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T05:05:03.770425Z","bundle_sha256":"cfd1c261aa6393d4db4efd3e2b1348dedab0b00914c460e993f839717e84efc8"}}