{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QORBMVDZ3D6QXVCEHIQ2WSJRXN","short_pith_number":"pith:QORBMVDZ","canonical_record":{"source":{"id":"1908.07738","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-08-21T07:52:39Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"4c561f2de17060b4448e15116d957b3e756a55420e36ea94178954911a9e15f4","abstract_canon_sha256":"266396bf6250bfa8eae0ce1dbfd5bce09ffc87010da4fc562a506ed5d408c6ed"},"schema_version":"1.0"},"canonical_sha256":"83a2165479d8fd0bd4443a21ab4931bb6ac008add60fd1cfd09e23489020a077","source":{"kind":"arxiv","id":"1908.07738","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.07738","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"1908.07738v1","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07738","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"QORBMVDZ3D6Q","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_16","alias_value":"QORBMVDZ3D6QXVCE","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_8","alias_value":"QORBMVDZ","created_at":"2026-07-04T23:59:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QORBMVDZ3D6QXVCEHIQ2WSJRXN","target":"record","payload":{"canonical_record":{"source":{"id":"1908.07738","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-08-21T07:52:39Z","cross_cats_sorted":["cs.MM"],"title_canon_sha256":"4c561f2de17060b4448e15116d957b3e756a55420e36ea94178954911a9e15f4","abstract_canon_sha256":"266396bf6250bfa8eae0ce1dbfd5bce09ffc87010da4fc562a506ed5d408c6ed"},"schema_version":"1.0"},"canonical_sha256":"83a2165479d8fd0bd4443a21ab4931bb6ac008add60fd1cfd09e23489020a077","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:00.806234Z","signature_b64":"A6YnL6PDoJE7C6fiE59yPZM3BrIdEE5t9wwY6jplRoINiw9l+YUG2VBfSTrGjlrJdCcx9GXU9pB/xhFUXUUXBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"83a2165479d8fd0bd4443a21ab4931bb6ac008add60fd1cfd09e23489020a077","last_reissued_at":"2026-07-04T23:59:00.805881Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:00.805881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.07738","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":"2026-07-04T23:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tC7o7PJUURCJg8YRtQtHm4diBiBSXlrx92vu8TkQIW3bR5+0d/PsvwtfG8m+bj2cOCJxEnXEbuyDA/DPnSoPCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T12:43:52.748013Z"},"content_sha256":"521d319d8b1f1c24b023af6054a3032ce15e8e1fa02b05c40e8e5991b809d328","schema_version":"1.0","event_id":"sha256:521d319d8b1f1c24b023af6054a3032ce15e8e1fa02b05c40e8e5991b809d328"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QORBMVDZ3D6QXVCEHIQ2WSJRXN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"User Diverse Preference Modeling by Multimodal Attentive Metric Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MM"],"primary_cat":"cs.IR","authors_text":"Changchang Sun, Fan Liu, Liqiang Nie, Mohan Kankanhalli, Yinglong Wang, Zhiyong Cheng","submitted_at":"2019-08-21T07:52:39Z","abstract_excerpt":"Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. However, the same vector cannot accurately capture a user's varying preferences on all items, especially when considering the diverse characteristics of various items. To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items. In particular, for each user-item pair, we propose an attention neural network, which exploit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07738","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/1908.07738/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":"2026-07-04T23:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ksYR9IV49VkD9xDsQacPdWqX3QOqk8RV/Fvjk38eQqfSx4Rfv6M06mD+MPxprytKd8Bu1mqluwCQrJ18jgstBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T12:43:52.748546Z"},"content_sha256":"9e5b883f65932f157a974bc7d2aa32168d6d94d38420aaea246697f5dcedfd85","schema_version":"1.0","event_id":"sha256:9e5b883f65932f157a974bc7d2aa32168d6d94d38420aaea246697f5dcedfd85"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QORBMVDZ3D6QXVCEHIQ2WSJRXN/bundle.json","state_url":"https://pith.science/pith/QORBMVDZ3D6QXVCEHIQ2WSJRXN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QORBMVDZ3D6QXVCEHIQ2WSJRXN/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":"2026-08-16T12:43:52Z","links":{"resolver":"https://pith.science/pith/QORBMVDZ3D6QXVCEHIQ2WSJRXN","bundle":"https://pith.science/pith/QORBMVDZ3D6QXVCEHIQ2WSJRXN/bundle.json","state":"https://pith.science/pith/QORBMVDZ3D6QXVCEHIQ2WSJRXN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QORBMVDZ3D6QXVCEHIQ2WSJRXN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QORBMVDZ3D6QXVCEHIQ2WSJRXN","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":"266396bf6250bfa8eae0ce1dbfd5bce09ffc87010da4fc562a506ed5d408c6ed","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-08-21T07:52:39Z","title_canon_sha256":"4c561f2de17060b4448e15116d957b3e756a55420e36ea94178954911a9e15f4"},"schema_version":"1.0","source":{"id":"1908.07738","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.07738","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"1908.07738v1","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.07738","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"QORBMVDZ3D6Q","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_16","alias_value":"QORBMVDZ3D6QXVCE","created_at":"2026-07-04T23:59:00Z"},{"alias_kind":"pith_short_8","alias_value":"QORBMVDZ","created_at":"2026-07-04T23:59:00Z"}],"graph_snapshots":[{"event_id":"sha256:9e5b883f65932f157a974bc7d2aa32168d6d94d38420aaea246697f5dcedfd85","target":"graph","created_at":"2026-07-04T23:59:00Z","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/1908.07738/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most existing recommender systems represent a user's preference with a feature vector, which is assumed to be fixed when predicting this user's preferences for different items. However, the same vector cannot accurately capture a user's varying preferences on all items, especially when considering the diverse characteristics of various items. To tackle this problem, in this paper, we propose a novel Multimodal Attentive Metric Learning (MAML) method to model user diverse preferences for various items. In particular, for each user-item pair, we propose an attention neural network, which exploit","authors_text":"Changchang Sun, Fan Liu, Liqiang Nie, Mohan Kankanhalli, Yinglong Wang, Zhiyong Cheng","cross_cats":["cs.MM"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-08-21T07:52:39Z","title":"User Diverse Preference Modeling by Multimodal Attentive Metric Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.07738","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":"sha256:521d319d8b1f1c24b023af6054a3032ce15e8e1fa02b05c40e8e5991b809d328","target":"record","created_at":"2026-07-04T23:59:00Z","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":"266396bf6250bfa8eae0ce1dbfd5bce09ffc87010da4fc562a506ed5d408c6ed","cross_cats_sorted":["cs.MM"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2019-08-21T07:52:39Z","title_canon_sha256":"4c561f2de17060b4448e15116d957b3e756a55420e36ea94178954911a9e15f4"},"schema_version":"1.0","source":{"id":"1908.07738","kind":"arxiv","version":1}},"canonical_sha256":"83a2165479d8fd0bd4443a21ab4931bb6ac008add60fd1cfd09e23489020a077","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"83a2165479d8fd0bd4443a21ab4931bb6ac008add60fd1cfd09e23489020a077","first_computed_at":"2026-07-04T23:59:00.805881Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:59:00.805881Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A6YnL6PDoJE7C6fiE59yPZM3BrIdEE5t9wwY6jplRoINiw9l+YUG2VBfSTrGjlrJdCcx9GXU9pB/xhFUXUUXBg==","signature_status":"signed_v1","signed_at":"2026-07-04T23:59:00.806234Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.07738","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:521d319d8b1f1c24b023af6054a3032ce15e8e1fa02b05c40e8e5991b809d328","sha256:9e5b883f65932f157a974bc7d2aa32168d6d94d38420aaea246697f5dcedfd85"],"state_sha256":"9808bb68204c6e7c1698e937671f0e288caa242ada5bfbbf5ac20dc2f0536dab"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PYFWvPoBgk1T1UL2tngoSFwArTxhy2wSlX0l8qs0g0FrIAUjsnTF3owMub5VvDdbcnWdWuR1ISq6ktcwlLMlBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T12:43:52.755231Z","bundle_sha256":"f4f8ec6eb65b4856bb33011065ceef8c60d3d3ca274e4e46c3f88fef087220ee"}}