{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:344KNJIBLF2S42FVDMH4LD2HXM","short_pith_number":"pith:344KNJIB","schema_version":"1.0","canonical_sha256":"df38a6a50159752e68b51b0fc58f47bb1aac9eb502639b5d12b716b4fe84076a","source":{"kind":"arxiv","id":"2505.00953","version":1},"attestation_state":"computed","paper":{"title":"Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Bradley Green, Devora Berlowitz, Karan Singhal, Lin Ning, Neo Wu, Philip Andrew Mansfield, Sushant Prakash, Yuhan Liu","submitted_at":"2025-05-02T02:04:52Z","abstract_excerpt":"User sequence modeling is crucial for modern large-scale recommendation systems, as it enables the extraction of informative representations of users and items from their historical interactions. These user representations are widely used for a variety of downstream tasks to enhance users' online experience. A key challenge for learning these representations is the lack of labeled training data. While self-supervised learning (SSL) methods have emerged as a promising solution for learning representations from unlabeled data, many existing approaches rely on extensive negative sampling, which c"},"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":"2505.00953","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2025-05-02T02:04:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7a2e9b8338f8c2fee66b8407fe5a0f9a3ebbef1d5a3561457a8d98405c85f36f","abstract_canon_sha256":"cfeb1f07dec743f53f346ff8a235ff9ff894d8b27b3e718628dad069c505d404"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:57:42.455746Z","signature_b64":"aNsZT5w2fVAG/DqEfBF8EnJcvr+e6AeoMP92YaX7SLHLPc3mwy4dVXTHueM6/zSUwkPpJ+y8kSWzIIheKlZwBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df38a6a50159752e68b51b0fc58f47bb1aac9eb502639b5d12b716b4fe84076a","last_reissued_at":"2026-07-05T10:57:42.455164Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:57:42.455164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.IR","authors_text":"Bradley Green, Devora Berlowitz, Karan Singhal, Lin Ning, Neo Wu, Philip Andrew Mansfield, Sushant Prakash, Yuhan Liu","submitted_at":"2025-05-02T02:04:52Z","abstract_excerpt":"User sequence modeling is crucial for modern large-scale recommendation systems, as it enables the extraction of informative representations of users and items from their historical interactions. These user representations are widely used for a variety of downstream tasks to enhance users' online experience. A key challenge for learning these representations is the lack of labeled training data. While self-supervised learning (SSL) methods have emerged as a promising solution for learning representations from unlabeled data, many existing approaches rely on extensive negative sampling, which c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.00953","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/2505.00953/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":"2505.00953","created_at":"2026-07-05T10:57:42.455232+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.00953v1","created_at":"2026-07-05T10:57:42.455232+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.00953","created_at":"2026-07-05T10:57:42.455232+00:00"},{"alias_kind":"pith_short_12","alias_value":"344KNJIBLF2S","created_at":"2026-07-05T10:57:42.455232+00:00"},{"alias_kind":"pith_short_16","alias_value":"344KNJIBLF2S42FV","created_at":"2026-07-05T10:57:42.455232+00:00"},{"alias_kind":"pith_short_8","alias_value":"344KNJIB","created_at":"2026-07-05T10:57:42.455232+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/344KNJIBLF2S42FVDMH4LD2HXM","json":"https://pith.science/pith/344KNJIBLF2S42FVDMH4LD2HXM.json","graph_json":"https://pith.science/api/pith-number/344KNJIBLF2S42FVDMH4LD2HXM/graph.json","events_json":"https://pith.science/api/pith-number/344KNJIBLF2S42FVDMH4LD2HXM/events.json","paper":"https://pith.science/paper/344KNJIB"},"agent_actions":{"view_html":"https://pith.science/pith/344KNJIBLF2S42FVDMH4LD2HXM","download_json":"https://pith.science/pith/344KNJIBLF2S42FVDMH4LD2HXM.json","view_paper":"https://pith.science/paper/344KNJIB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.00953&json=true","fetch_graph":"https://pith.science/api/pith-number/344KNJIBLF2S42FVDMH4LD2HXM/graph.json","fetch_events":"https://pith.science/api/pith-number/344KNJIBLF2S42FVDMH4LD2HXM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/344KNJIBLF2S42FVDMH4LD2HXM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/344KNJIBLF2S42FVDMH4LD2HXM/action/storage_attestation","attest_author":"https://pith.science/pith/344KNJIBLF2S42FVDMH4LD2HXM/action/author_attestation","sign_citation":"https://pith.science/pith/344KNJIBLF2S42FVDMH4LD2HXM/action/citation_signature","submit_replication":"https://pith.science/pith/344KNJIBLF2S42FVDMH4LD2HXM/action/replication_record"}},"created_at":"2026-07-05T10:57:42.455232+00:00","updated_at":"2026-07-05T10:57:42.455232+00:00"}