{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:H63AJXIGQ4RNKZR4FTDP4V564Z","short_pith_number":"pith:H63AJXIG","schema_version":"1.0","canonical_sha256":"3fb604dd068722d5663c2cc6fe57bee6419761fad81a91eb507536d2267ec214","source":{"kind":"arxiv","id":"1905.01994","version":1},"attestation_state":"computed","paper":{"title":"Review-Driven Answer Generation for Product-Related Questions in E-Commerce","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chenliang Li, Feng Ji, Haiqing Chen, Shiqian Chen, Wei Zhou","submitted_at":"2019-04-27T01:57:28Z","abstract_excerpt":"The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding p"},"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":"1905.01994","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-04-27T01:57:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8a74d4822cb02dac7ba01e49771289cb636a01a7d78b29babd817025bdb770a4","abstract_canon_sha256":"387c3ca7b158e5db33e90dfafc844ecdc258033ed7fcd97f6300288c9371213d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:56.748767Z","signature_b64":"J8oP7mk7M662mrZMtViEXHoFDxTz0o0HP7Kbx43OgIevkGHbKFagJXoDsI+Z5+WeueaG461y97HwALd3uXI4CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3fb604dd068722d5663c2cc6fe57bee6419761fad81a91eb507536d2267ec214","last_reissued_at":"2026-05-17T23:46:56.748184Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:56.748184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Review-Driven Answer Generation for Product-Related Questions in E-Commerce","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chenliang Li, Feng Ji, Haiqing Chen, Shiqian Chen, Wei Zhou","submitted_at":"2019-04-27T01:57:28Z","abstract_excerpt":"The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.01994","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1905.01994","created_at":"2026-05-17T23:46:56.748278+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.01994v1","created_at":"2026-05-17T23:46:56.748278+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.01994","created_at":"2026-05-17T23:46:56.748278+00:00"},{"alias_kind":"pith_short_12","alias_value":"H63AJXIGQ4RN","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"H63AJXIGQ4RNKZR4","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"H63AJXIG","created_at":"2026-05-18T12:33:18.533446+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/H63AJXIGQ4RNKZR4FTDP4V564Z","json":"https://pith.science/pith/H63AJXIGQ4RNKZR4FTDP4V564Z.json","graph_json":"https://pith.science/api/pith-number/H63AJXIGQ4RNKZR4FTDP4V564Z/graph.json","events_json":"https://pith.science/api/pith-number/H63AJXIGQ4RNKZR4FTDP4V564Z/events.json","paper":"https://pith.science/paper/H63AJXIG"},"agent_actions":{"view_html":"https://pith.science/pith/H63AJXIGQ4RNKZR4FTDP4V564Z","download_json":"https://pith.science/pith/H63AJXIGQ4RNKZR4FTDP4V564Z.json","view_paper":"https://pith.science/paper/H63AJXIG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.01994&json=true","fetch_graph":"https://pith.science/api/pith-number/H63AJXIGQ4RNKZR4FTDP4V564Z/graph.json","fetch_events":"https://pith.science/api/pith-number/H63AJXIGQ4RNKZR4FTDP4V564Z/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/H63AJXIGQ4RNKZR4FTDP4V564Z/action/timestamp_anchor","attest_storage":"https://pith.science/pith/H63AJXIGQ4RNKZR4FTDP4V564Z/action/storage_attestation","attest_author":"https://pith.science/pith/H63AJXIGQ4RNKZR4FTDP4V564Z/action/author_attestation","sign_citation":"https://pith.science/pith/H63AJXIGQ4RNKZR4FTDP4V564Z/action/citation_signature","submit_replication":"https://pith.science/pith/H63AJXIGQ4RNKZR4FTDP4V564Z/action/replication_record"}},"created_at":"2026-05-17T23:46:56.748278+00:00","updated_at":"2026-05-17T23:46:56.748278+00:00"}