{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:ZII7AUGOLBQTGADUXCQRWISOML","short_pith_number":"pith:ZII7AUGO","canonical_record":{"source":{"id":"1906.02037","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2019-06-03T20:31:57Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"9457731704ff1d8370d3a6eeceeee08f1c370c64de7407b14de9611d539cc0e6","abstract_canon_sha256":"5019a32a7d8ad661e72645e9addf4d25421bb970b2589ab83a36ed0e1ce5fc2e"},"schema_version":"1.0"},"canonical_sha256":"ca11f050ce5861330074b8a11b224e62ee0f15863bf5730657304f44d052b5ae","source":{"kind":"arxiv","id":"1906.02037","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.02037","created_at":"2026-05-17T23:44:05Z"},{"alias_kind":"arxiv_version","alias_value":"1906.02037v1","created_at":"2026-05-17T23:44:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.02037","created_at":"2026-05-17T23:44:05Z"},{"alias_kind":"pith_short_12","alias_value":"ZII7AUGOLBQT","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZII7AUGOLBQTGADU","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZII7AUGO","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:ZII7AUGOLBQTGADUXCQRWISOML","target":"record","payload":{"canonical_record":{"source":{"id":"1906.02037","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2019-06-03T20:31:57Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"9457731704ff1d8370d3a6eeceeee08f1c370c64de7407b14de9611d539cc0e6","abstract_canon_sha256":"5019a32a7d8ad661e72645e9addf4d25421bb970b2589ab83a36ed0e1ce5fc2e"},"schema_version":"1.0"},"canonical_sha256":"ca11f050ce5861330074b8a11b224e62ee0f15863bf5730657304f44d052b5ae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:05.409562Z","signature_b64":"7kp7ET9S+kw2Zxtl6gOEe0Af+jbj97r0O+ct8tF53W6MEvCb6OBw8TGoY2p9UlQp6RNFBpw3uQPSXz4/0xluDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca11f050ce5861330074b8a11b224e62ee0f15863bf5730657304f44d052b5ae","last_reissued_at":"2026-05-17T23:44:05.408959Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:05.408959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.02037","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":"2026-05-17T23:44:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EZ2ZPlTWDRNG3Jo5oAdLYB4/Gc4fHxkRSg+x3ZJiD0Uh/1B/p3eeoWL4mjeepSRh9dhH7ujHE4BfUXyqjKCLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T02:35:50.952758Z"},"content_sha256":"13702368efad133d3b5433942d1d638592d745784342f2978b9f45dc9db58062","schema_version":"1.0","event_id":"sha256:13702368efad133d3b5433942d1d638592d745784342f2978b9f45dc9db58062"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:ZII7AUGOLBQTGADUXCQRWISOML","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The FacT: Taming Latent Factor Models for Explainability with Factorization Trees","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.IR","authors_text":"Hongning Wang, Nan Wang, Yiling Jia, Yiyi Tao","submitted_at":"2019-06-03T20:31:57Z","abstract_excerpt":"Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors. Specifically, we build regression trees on users and items respectively with user-generated reviews, and associate a latent profile to each node on the trees to represent users and items. With the growth of regression tree, the latent factors are gradually refined under the regula"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.02037","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":"2026-05-17T23:44:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JItRWE8yYzeTF/kqGaPRnSO6VVNiuyw+DxawiZyD2uLbcalJVcef8JRzDKl+WhfZGrEuy7g2ntkbuibSYANACQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T02:35:50.953537Z"},"content_sha256":"058878e57f925b9d92ee4a48406a9f9645188e620f96d2fae9a02aa8eeee8afc","schema_version":"1.0","event_id":"sha256:058878e57f925b9d92ee4a48406a9f9645188e620f96d2fae9a02aa8eeee8afc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZII7AUGOLBQTGADUXCQRWISOML/bundle.json","state_url":"https://pith.science/pith/ZII7AUGOLBQTGADUXCQRWISOML/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZII7AUGOLBQTGADUXCQRWISOML/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":"2026-06-01T02:35:50Z","links":{"resolver":"https://pith.science/pith/ZII7AUGOLBQTGADUXCQRWISOML","bundle":"https://pith.science/pith/ZII7AUGOLBQTGADUXCQRWISOML/bundle.json","state":"https://pith.science/pith/ZII7AUGOLBQTGADUXCQRWISOML/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZII7AUGOLBQTGADUXCQRWISOML/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:ZII7AUGOLBQTGADUXCQRWISOML","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":"5019a32a7d8ad661e72645e9addf4d25421bb970b2589ab83a36ed0e1ce5fc2e","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2019-06-03T20:31:57Z","title_canon_sha256":"9457731704ff1d8370d3a6eeceeee08f1c370c64de7407b14de9611d539cc0e6"},"schema_version":"1.0","source":{"id":"1906.02037","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.02037","created_at":"2026-05-17T23:44:05Z"},{"alias_kind":"arxiv_version","alias_value":"1906.02037v1","created_at":"2026-05-17T23:44:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.02037","created_at":"2026-05-17T23:44:05Z"},{"alias_kind":"pith_short_12","alias_value":"ZII7AUGOLBQT","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"ZII7AUGOLBQTGADU","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"ZII7AUGO","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:058878e57f925b9d92ee4a48406a9f9645188e620f96d2fae9a02aa8eeee8afc","target":"graph","created_at":"2026-05-17T23:44:05Z","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":"Latent factor models have achieved great success in personalized recommendations, but they are also notoriously difficult to explain. In this work, we integrate regression trees to guide the learning of latent factor models for recommendation, and use the learnt tree structure to explain the resulting latent factors. Specifically, we build regression trees on users and items respectively with user-generated reviews, and associate a latent profile to each node on the trees to represent users and items. With the growth of regression tree, the latent factors are gradually refined under the regula","authors_text":"Hongning Wang, Nan Wang, Yiling Jia, Yiyi Tao","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2019-06-03T20:31:57Z","title":"The FacT: Taming Latent Factor Models for Explainability with Factorization Trees"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.02037","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":"sha256:13702368efad133d3b5433942d1d638592d745784342f2978b9f45dc9db58062","target":"record","created_at":"2026-05-17T23:44:05Z","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":"5019a32a7d8ad661e72645e9addf4d25421bb970b2589ab83a36ed0e1ce5fc2e","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2019-06-03T20:31:57Z","title_canon_sha256":"9457731704ff1d8370d3a6eeceeee08f1c370c64de7407b14de9611d539cc0e6"},"schema_version":"1.0","source":{"id":"1906.02037","kind":"arxiv","version":1}},"canonical_sha256":"ca11f050ce5861330074b8a11b224e62ee0f15863bf5730657304f44d052b5ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ca11f050ce5861330074b8a11b224e62ee0f15863bf5730657304f44d052b5ae","first_computed_at":"2026-05-17T23:44:05.408959Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:05.408959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7kp7ET9S+kw2Zxtl6gOEe0Af+jbj97r0O+ct8tF53W6MEvCb6OBw8TGoY2p9UlQp6RNFBpw3uQPSXz4/0xluDA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:05.409562Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.02037","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:13702368efad133d3b5433942d1d638592d745784342f2978b9f45dc9db58062","sha256:058878e57f925b9d92ee4a48406a9f9645188e620f96d2fae9a02aa8eeee8afc"],"state_sha256":"41efce19faa2ff569e96dde7d583c95709bc5339a77a503b9b4598ae945a9079"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w2+hhmGh8lsaqS1mkrXxO3Raq6o/gvkB8hHmcPSTGA3Nxf+jEeemYTcGh8rm6/gbQmWpUIFNAbC2HlH+V2S2Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T02:35:50.960629Z","bundle_sha256":"783f2a018e40a45b8dda22685d5b2be02ea5d486b8d6391fc82c1036ab068cb7"}}