{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5A3PZK37WRWW3CTTTVRIMRU4EW","short_pith_number":"pith:5A3PZK37","schema_version":"1.0","canonical_sha256":"e836fcab7fb46d6d8a739d6286469c25a0ebd2290c64571ab47dcfb1b9e2d9a4","source":{"kind":"arxiv","id":"2505.01255","version":1},"attestation_state":"computed","paper":{"title":"PREMISE: Matching-based Prediction for Accurate Review Recommendation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.IR","cs.MM"],"primary_cat":"cs.CL","authors_text":"Hui Chen, Soujanya Poria, Wei Han","submitted_at":"2025-05-02T13:23:13Z","abstract_excerpt":"We present PREMISE (PREdict with Matching ScorEs), a new architecture for the matching-based learning in the multimodal fields for the multimodal review helpfulness (MRHP) task. Distinct to previous fusion-based methods which obtains multimodal representations via cross-modal attention for downstream tasks, PREMISE computes the multi-scale and multi-field representations, filters duplicated semantics, and then obtained a set of matching scores as feature vectors for the downstream recommendation task. This new architecture significantly boosts the performance for such multimodal tasks whose co"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.01255","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-02T13:23:13Z","cross_cats_sorted":["cs.IR","cs.MM"],"title_canon_sha256":"e1ee2564ad2b28ce262f6fde65a803d4435f36054003406cf6eec13efa6882b2","abstract_canon_sha256":"bd28572bda3846c6416376f71e03b29acbee0586cf9e2fc9522e081da394a5da"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:48.355975Z","signature_b64":"0d70MZw/MFGv41qiwDj8EIF8HcSfW8K8yKAI815mqgG0QIRqNhjSpBg3qCq1bXXoK/aEagi99mqNnowolwakDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e836fcab7fb46d6d8a739d6286469c25a0ebd2290c64571ab47dcfb1b9e2d9a4","last_reissued_at":"2026-07-05T10:57:48.355282Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:48.355282Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PREMISE: Matching-based Prediction for Accurate Review Recommendation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.IR","cs.MM"],"primary_cat":"cs.CL","authors_text":"Hui Chen, Soujanya Poria, Wei Han","submitted_at":"2025-05-02T13:23:13Z","abstract_excerpt":"We present PREMISE (PREdict with Matching ScorEs), a new architecture for the matching-based learning in the multimodal fields for the multimodal review helpfulness (MRHP) task. Distinct to previous fusion-based methods which obtains multimodal representations via cross-modal attention for downstream tasks, PREMISE computes the multi-scale and multi-field representations, filters duplicated semantics, and then obtained a set of matching scores as feature vectors for the downstream recommendation task. This new architecture significantly boosts the performance for such multimodal tasks whose co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.01255","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/2505.01255/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.01255","created_at":"2026-07-05T10:57:48.355375+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.01255v1","created_at":"2026-07-05T10:57:48.355375+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.01255","created_at":"2026-07-05T10:57:48.355375+00:00"},{"alias_kind":"pith_short_12","alias_value":"5A3PZK37WRWW","created_at":"2026-07-05T10:57:48.355375+00:00"},{"alias_kind":"pith_short_16","alias_value":"5A3PZK37WRWW3CTT","created_at":"2026-07-05T10:57:48.355375+00:00"},{"alias_kind":"pith_short_8","alias_value":"5A3PZK37","created_at":"2026-07-05T10:57:48.355375+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW","json":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW.json","graph_json":"https://pith.science/api/pith-number/5A3PZK37WRWW3CTTTVRIMRU4EW/graph.json","events_json":"https://pith.science/api/pith-number/5A3PZK37WRWW3CTTTVRIMRU4EW/events.json","paper":"https://pith.science/paper/5A3PZK37"},"agent_actions":{"view_html":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW","download_json":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW.json","view_paper":"https://pith.science/paper/5A3PZK37","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.01255&json=true","fetch_graph":"https://pith.science/api/pith-number/5A3PZK37WRWW3CTTTVRIMRU4EW/graph.json","fetch_events":"https://pith.science/api/pith-number/5A3PZK37WRWW3CTTTVRIMRU4EW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW/action/storage_attestation","attest_author":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW/action/author_attestation","sign_citation":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW/action/citation_signature","submit_replication":"https://pith.science/pith/5A3PZK37WRWW3CTTTVRIMRU4EW/action/replication_record"}},"created_at":"2026-07-05T10:57:48.355375+00:00","updated_at":"2026-07-05T10:57:48.355375+00:00"}