{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YJR4RW3YEL64DTKJB3SJGOF2RH","short_pith_number":"pith:YJR4RW3Y","schema_version":"1.0","canonical_sha256":"c263c8db7822fdc1cd490ee49338ba89eb4601f05224b15e2b77e461f2ee2f23","source":{"kind":"arxiv","id":"1904.09037","version":1},"attestation_state":"computed","paper":{"title":"ProductNet: a Collection of High-Quality Datasets for Product Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chu Wang, Da Zhang, Hirohisa Fujita, Lei Tang, Shujun Bian, Yang Lu, Yongning Wu, Zuohua Zhang","submitted_at":"2019-04-18T23:17:07Z","abstract_excerpt":"ProductNet is a collection of high-quality product datasets for better product understanding. Motivated by ImageNet, ProductNet aims at supporting product representation learning by curating product datasets of high quality with properly chosen taxonomy. In this paper, the two goals of building high-quality product datasets and learning product representation support each other in an iterative fashion: the product embedding is obtained via a multi-modal deep neural network (master model) designed to leverage product image and catalog information; and in return, the embedding is utilized via ac"},"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":"1904.09037","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-04-18T23:17:07Z","cross_cats_sorted":["cs.CL","cs.CV","stat.ML"],"title_canon_sha256":"a54e7801b65db4dbb3f0d7fc569238d6c765b79f3981f3f4df2b87310bb1956f","abstract_canon_sha256":"e65c422bf32be5013218da3638d558000b51f450215b47744ecc7ac3607e2fa8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:08.538754Z","signature_b64":"jhifYaomEIhnimjsTV6mhtjLTQtkr4C0YS3QoXIuLIbxLUefbEmSVMHBLbt8wSsW6b4vsH2/8UlAkyrXlvIuAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c263c8db7822fdc1cd490ee49338ba89eb4601f05224b15e2b77e461f2ee2f23","last_reissued_at":"2026-05-17T23:48:08.537872Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:08.537872Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ProductNet: a Collection of High-Quality Datasets for Product Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.CV","stat.ML"],"primary_cat":"cs.LG","authors_text":"Chu Wang, Da Zhang, Hirohisa Fujita, Lei Tang, Shujun Bian, Yang Lu, Yongning Wu, Zuohua Zhang","submitted_at":"2019-04-18T23:17:07Z","abstract_excerpt":"ProductNet is a collection of high-quality product datasets for better product understanding. Motivated by ImageNet, ProductNet aims at supporting product representation learning by curating product datasets of high quality with properly chosen taxonomy. In this paper, the two goals of building high-quality product datasets and learning product representation support each other in an iterative fashion: the product embedding is obtained via a multi-modal deep neural network (master model) designed to leverage product image and catalog information; and in return, the embedding is utilized via ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.09037","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":"1904.09037","created_at":"2026-05-17T23:48:08.537972+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.09037v1","created_at":"2026-05-17T23:48:08.537972+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.09037","created_at":"2026-05-17T23:48:08.537972+00:00"},{"alias_kind":"pith_short_12","alias_value":"YJR4RW3YEL64","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"YJR4RW3YEL64DTKJ","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"YJR4RW3Y","created_at":"2026-05-18T12:33:33.725879+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/YJR4RW3YEL64DTKJB3SJGOF2RH","json":"https://pith.science/pith/YJR4RW3YEL64DTKJB3SJGOF2RH.json","graph_json":"https://pith.science/api/pith-number/YJR4RW3YEL64DTKJB3SJGOF2RH/graph.json","events_json":"https://pith.science/api/pith-number/YJR4RW3YEL64DTKJB3SJGOF2RH/events.json","paper":"https://pith.science/paper/YJR4RW3Y"},"agent_actions":{"view_html":"https://pith.science/pith/YJR4RW3YEL64DTKJB3SJGOF2RH","download_json":"https://pith.science/pith/YJR4RW3YEL64DTKJB3SJGOF2RH.json","view_paper":"https://pith.science/paper/YJR4RW3Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.09037&json=true","fetch_graph":"https://pith.science/api/pith-number/YJR4RW3YEL64DTKJB3SJGOF2RH/graph.json","fetch_events":"https://pith.science/api/pith-number/YJR4RW3YEL64DTKJB3SJGOF2RH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YJR4RW3YEL64DTKJB3SJGOF2RH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YJR4RW3YEL64DTKJB3SJGOF2RH/action/storage_attestation","attest_author":"https://pith.science/pith/YJR4RW3YEL64DTKJB3SJGOF2RH/action/author_attestation","sign_citation":"https://pith.science/pith/YJR4RW3YEL64DTKJB3SJGOF2RH/action/citation_signature","submit_replication":"https://pith.science/pith/YJR4RW3YEL64DTKJB3SJGOF2RH/action/replication_record"}},"created_at":"2026-05-17T23:48:08.537972+00:00","updated_at":"2026-05-17T23:48:08.537972+00:00"}