{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:22U3BPTYHSOVZRPWB6VGV3773K","short_pith_number":"pith:22U3BPTY","canonical_record":{"source":{"id":"1711.10327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-11-28T15:00:24Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"180d18602c5c6d4c44e74f3c3b5b96c6ecf9bd7db496d7d9563f752a8246c75a","abstract_canon_sha256":"9c49d7fe4c212371f5c87c6af35e26eccd1ce0486ad9970e9821dc80c1bf33e6"},"schema_version":"1.0"},"canonical_sha256":"d6a9b0be783c9d5cc5f60faa6aefffda97c692f1622fea82c5ff40978f4597a1","source":{"kind":"arxiv","id":"1711.10327","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.10327","created_at":"2026-05-18T00:29:22Z"},{"alias_kind":"arxiv_version","alias_value":"1711.10327v1","created_at":"2026-05-18T00:29:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10327","created_at":"2026-05-18T00:29:22Z"},{"alias_kind":"pith_short_12","alias_value":"22U3BPTYHSOV","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"22U3BPTYHSOVZRPW","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"22U3BPTY","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:22U3BPTYHSOVZRPWB6VGV3773K","target":"record","payload":{"canonical_record":{"source":{"id":"1711.10327","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-11-28T15:00:24Z","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"title_canon_sha256":"180d18602c5c6d4c44e74f3c3b5b96c6ecf9bd7db496d7d9563f752a8246c75a","abstract_canon_sha256":"9c49d7fe4c212371f5c87c6af35e26eccd1ce0486ad9970e9821dc80c1bf33e6"},"schema_version":"1.0"},"canonical_sha256":"d6a9b0be783c9d5cc5f60faa6aefffda97c692f1622fea82c5ff40978f4597a1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:22.531518Z","signature_b64":"gYA95Yzv/x4zDJzFTweQ4HEYYNfywNsBIsQ0kIib+Jwfm3XO8k6u2tsBAWp+bnG9VzCwKkFVGWq8ikxQNHiCCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6a9b0be783c9d5cc5f60faa6aefffda97c692f1622fea82c5ff40978f4597a1","last_reissued_at":"2026-05-18T00:29:22.530863Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:22.530863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.10327","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":"2026-05-18T00:29:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2x7jGFsVvyxj0PRS6I1lIiP1B9NJukN9tWJF+GaRDwMaFMiOIorVUaox8rl4eiLTwHt75Hpjoca8RrlDulFGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T13:39:13.652082Z"},"content_sha256":"4c70fc114feaa0fca1c2cfa096f0df62a15c8b4865d0eacd95521d9a8f7d8dd1","schema_version":"1.0","event_id":"sha256:4c70fc114feaa0fca1c2cfa096f0df62a15c8b4865d0eacd95521d9a8f7d8dd1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:22U3BPTYHSOVZRPWB6VGV3773K","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generative Interest Estimation for Document Recommendations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Alexander Immer, Danijar Hafner, Fabian Windheuser, Willi Raschkowski","submitted_at":"2017-11-28T15:00:24Z","abstract_excerpt":"Learning distributed representations of documents has pushed the state-of-the-art in several natural language processing tasks and was successfully applied to the field of recommender systems recently. In this paper, we propose a novel content-based recommender system based on learned representations and a generative model of user interest. Our method works as follows: First, we learn representations on a corpus of text documents. Then, we capture a user's interest as a generative model in the space of the document representations. In particular, we model the distribution of interest for each "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10327","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":"2026-05-18T00:29:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OqaUUMys0lpD0PnQf0H9QGRqHGdmoJbQliX0WQ5mXbyoARtbWmV2UMmW/Go+9F4l4hh+NSqLOB/Pguv6DhFbAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T13:39:13.652434Z"},"content_sha256":"229de79528dac8c693d2c85de8998790de00e27e883ebbf53bc6783db002e006","schema_version":"1.0","event_id":"sha256:229de79528dac8c693d2c85de8998790de00e27e883ebbf53bc6783db002e006"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/22U3BPTYHSOVZRPWB6VGV3773K/bundle.json","state_url":"https://pith.science/pith/22U3BPTYHSOVZRPWB6VGV3773K/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/22U3BPTYHSOVZRPWB6VGV3773K/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":"2026-07-17T13:39:13Z","links":{"resolver":"https://pith.science/pith/22U3BPTYHSOVZRPWB6VGV3773K","bundle":"https://pith.science/pith/22U3BPTYHSOVZRPWB6VGV3773K/bundle.json","state":"https://pith.science/pith/22U3BPTYHSOVZRPWB6VGV3773K/state.json","well_known_bundle":"https://pith.science/.well-known/pith/22U3BPTYHSOVZRPWB6VGV3773K/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:22U3BPTYHSOVZRPWB6VGV3773K","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":"9c49d7fe4c212371f5c87c6af35e26eccd1ce0486ad9970e9821dc80c1bf33e6","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-11-28T15:00:24Z","title_canon_sha256":"180d18602c5c6d4c44e74f3c3b5b96c6ecf9bd7db496d7d9563f752a8246c75a"},"schema_version":"1.0","source":{"id":"1711.10327","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.10327","created_at":"2026-05-18T00:29:22Z"},{"alias_kind":"arxiv_version","alias_value":"1711.10327v1","created_at":"2026-05-18T00:29:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.10327","created_at":"2026-05-18T00:29:22Z"},{"alias_kind":"pith_short_12","alias_value":"22U3BPTYHSOV","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"22U3BPTYHSOVZRPW","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"22U3BPTY","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:229de79528dac8c693d2c85de8998790de00e27e883ebbf53bc6783db002e006","target":"graph","created_at":"2026-05-18T00:29:22Z","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":"Learning distributed representations of documents has pushed the state-of-the-art in several natural language processing tasks and was successfully applied to the field of recommender systems recently. In this paper, we propose a novel content-based recommender system based on learned representations and a generative model of user interest. Our method works as follows: First, we learn representations on a corpus of text documents. Then, we capture a user's interest as a generative model in the space of the document representations. In particular, we model the distribution of interest for each ","authors_text":"Alexander Immer, Danijar Hafner, Fabian Windheuser, Willi Raschkowski","cross_cats":["cs.CL","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-11-28T15:00:24Z","title":"Generative Interest Estimation for Document Recommendations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.10327","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":"sha256:4c70fc114feaa0fca1c2cfa096f0df62a15c8b4865d0eacd95521d9a8f7d8dd1","target":"record","created_at":"2026-05-18T00:29:22Z","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":"9c49d7fe4c212371f5c87c6af35e26eccd1ce0486ad9970e9821dc80c1bf33e6","cross_cats_sorted":["cs.CL","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-11-28T15:00:24Z","title_canon_sha256":"180d18602c5c6d4c44e74f3c3b5b96c6ecf9bd7db496d7d9563f752a8246c75a"},"schema_version":"1.0","source":{"id":"1711.10327","kind":"arxiv","version":1}},"canonical_sha256":"d6a9b0be783c9d5cc5f60faa6aefffda97c692f1622fea82c5ff40978f4597a1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d6a9b0be783c9d5cc5f60faa6aefffda97c692f1622fea82c5ff40978f4597a1","first_computed_at":"2026-05-18T00:29:22.530863Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:22.530863Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gYA95Yzv/x4zDJzFTweQ4HEYYNfywNsBIsQ0kIib+Jwfm3XO8k6u2tsBAWp+bnG9VzCwKkFVGWq8ikxQNHiCCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:22.531518Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.10327","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4c70fc114feaa0fca1c2cfa096f0df62a15c8b4865d0eacd95521d9a8f7d8dd1","sha256:229de79528dac8c693d2c85de8998790de00e27e883ebbf53bc6783db002e006"],"state_sha256":"f6f8dc6f4ebb4338c9a35fcbb569c4a7b47509cbffcc4906bd512306b75cd300"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Og1Za3qAjiDzVZiJe7ymrc8kDaetZrmDoyvF2aAxUREzfM9+0P860e5jFraohgmn1CZkA5KNosAWMluiLhbeCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-17T13:39:13.654495Z","bundle_sha256":"8ef7090626b13cec7c66e8b07bc774372525845dc7dd04f404f8f4e2808b6afd"}}