{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:3OZCT34DLG7MNNSMSGU6ZUCQSO","short_pith_number":"pith:3OZCT34D","canonical_record":{"source":{"id":"1609.02116","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-07T19:05:42Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"c6c0fb79a06fa9abc0f03d7e0a39939c3e75d89ff756bf6cbfeefeaab080957f","abstract_canon_sha256":"7a387338194c7002dc60f786ffb25e815d362e5643f459db3a50b12111791d3a"},"schema_version":"1.0"},"canonical_sha256":"dbb229ef8359bec6b64c91a9ecd0509399da7464128cd0b76125e12cfc845485","source":{"kind":"arxiv","id":"1609.02116","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.02116","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"1609.02116v2","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.02116","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"3OZCT34DLG7M","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"3OZCT34DLG7MNNSM","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"3OZCT34D","created_at":"2026-05-18T12:29:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:3OZCT34DLG7MNNSMSGU6ZUCQSO","target":"record","payload":{"canonical_record":{"source":{"id":"1609.02116","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-07T19:05:42Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"c6c0fb79a06fa9abc0f03d7e0a39939c3e75d89ff756bf6cbfeefeaab080957f","abstract_canon_sha256":"7a387338194c7002dc60f786ffb25e815d362e5643f459db3a50b12111791d3a"},"schema_version":"1.0"},"canonical_sha256":"dbb229ef8359bec6b64c91a9ecd0509399da7464128cd0b76125e12cfc845485","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:53.131948Z","signature_b64":"VMwykLoo1knrOYtb7Ny3GMCtP6XDI72kjDKCKAGjd98P/90GYKIfY040eLjJsvVQUByqF1Jx+CDEFYe25GikDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dbb229ef8359bec6b64c91a9ecd0509399da7464128cd0b76125e12cfc845485","last_reissued_at":"2026-05-18T01:04:53.131425Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:53.131425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1609.02116","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:04:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tRm/3zTJfdGBmYoNz6itv0897oW39eTnyjUCKSeNa22LZCvjovy+zngE8o/Kb76B1KnH17iQu+dFbrqahKsoDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T20:55:28.952239Z"},"content_sha256":"e534cd1ff8ce33f1f15db7d96d5d7b14b91de9217fa3119189e152b69ba9ebd0","schema_version":"1.0","event_id":"sha256:e534cd1ff8ce33f1f15db7d96d5d7b14b91de9217fa3119189e152b69ba9ebd0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:3OZCT34DLG7MNNSMSGU6ZUCQSO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Ask the GRU: Multi-Task Learning for Deep Text Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"stat.ML","authors_text":"Andrew McCallum, David Belanger, Trapit Bansal","submitted_at":"2016-09-07T19:05:42Z","abstract_excerpt":"In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.02116","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":""},"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":"2026-05-18T01:04:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MreddyaImfLW0K2zfrr+76iy8gWRuqqqqo5RywpfJAjlYiRBMKGiwRp11EWjY5E+kuTGaqcpGgEMerD8JXXSDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T20:55:28.952943Z"},"content_sha256":"45566b9f5a3f4e7bf37ccddf254a479107c60686031121caea09832aad633c59","schema_version":"1.0","event_id":"sha256:45566b9f5a3f4e7bf37ccddf254a479107c60686031121caea09832aad633c59"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3OZCT34DLG7MNNSMSGU6ZUCQSO/bundle.json","state_url":"https://pith.science/pith/3OZCT34DLG7MNNSMSGU6ZUCQSO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3OZCT34DLG7MNNSMSGU6ZUCQSO/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":"2026-05-27T20:55:28Z","links":{"resolver":"https://pith.science/pith/3OZCT34DLG7MNNSMSGU6ZUCQSO","bundle":"https://pith.science/pith/3OZCT34DLG7MNNSMSGU6ZUCQSO/bundle.json","state":"https://pith.science/pith/3OZCT34DLG7MNNSMSGU6ZUCQSO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3OZCT34DLG7MNNSMSGU6ZUCQSO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:3OZCT34DLG7MNNSMSGU6ZUCQSO","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":"7a387338194c7002dc60f786ffb25e815d362e5643f459db3a50b12111791d3a","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-07T19:05:42Z","title_canon_sha256":"c6c0fb79a06fa9abc0f03d7e0a39939c3e75d89ff756bf6cbfeefeaab080957f"},"schema_version":"1.0","source":{"id":"1609.02116","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1609.02116","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"arxiv_version","alias_value":"1609.02116v2","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.02116","created_at":"2026-05-18T01:04:53Z"},{"alias_kind":"pith_short_12","alias_value":"3OZCT34DLG7M","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"3OZCT34DLG7MNNSM","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"3OZCT34D","created_at":"2026-05-18T12:29:55Z"}],"graph_snapshots":[{"event_id":"sha256:45566b9f5a3f4e7bf37ccddf254a479107c60686031121caea09832aad633c59","target":"graph","created_at":"2026-05-18T01:04:53Z","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 a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news articles, blog posts, etc. Recommendation approaches based on latent factor models can be extended naturally to leverage text by employing an explicit mapping from text to factors. This enables recommendations for new, unseen content, and may generalize better, since the factors for all items are produced by a compactly-parametrized model. Previous work has used topic models or averages of word embeddings for this mapping","authors_text":"Andrew McCallum, David Belanger, Trapit Bansal","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-07T19:05:42Z","title":"Ask the GRU: Multi-Task Learning for Deep Text Recommendations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.02116","kind":"arxiv","version":2},"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:e534cd1ff8ce33f1f15db7d96d5d7b14b91de9217fa3119189e152b69ba9ebd0","target":"record","created_at":"2026-05-18T01:04:53Z","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":"7a387338194c7002dc60f786ffb25e815d362e5643f459db3a50b12111791d3a","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-09-07T19:05:42Z","title_canon_sha256":"c6c0fb79a06fa9abc0f03d7e0a39939c3e75d89ff756bf6cbfeefeaab080957f"},"schema_version":"1.0","source":{"id":"1609.02116","kind":"arxiv","version":2}},"canonical_sha256":"dbb229ef8359bec6b64c91a9ecd0509399da7464128cd0b76125e12cfc845485","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"dbb229ef8359bec6b64c91a9ecd0509399da7464128cd0b76125e12cfc845485","first_computed_at":"2026-05-18T01:04:53.131425Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:04:53.131425Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VMwykLoo1knrOYtb7Ny3GMCtP6XDI72kjDKCKAGjd98P/90GYKIfY040eLjJsvVQUByqF1Jx+CDEFYe25GikDA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:04:53.131948Z","signed_message":"canonical_sha256_bytes"},"source_id":"1609.02116","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e534cd1ff8ce33f1f15db7d96d5d7b14b91de9217fa3119189e152b69ba9ebd0","sha256:45566b9f5a3f4e7bf37ccddf254a479107c60686031121caea09832aad633c59"],"state_sha256":"c1cd6a6acf4d1a07efabcaa81ce2c7a86179f36bc9d7afdf0c0abbe2eb1cb590"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IUUpqCuaPAXjjLXS6g0pBjiymz6a2yRUpUFR83uM0xCkrxK3mQSVThV4gyhetn00UFOr1lkfuWRmYBjId3eVCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T20:55:28.956512Z","bundle_sha256":"3ee77df247fc6d4b427bea85a21d74a8813073277bcee9cababfb130b2d3302c"}}