{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:I5RU6MIQC3Y4KTWGNTAQXMLQJR","short_pith_number":"pith:I5RU6MIQ","schema_version":"1.0","canonical_sha256":"47634f311016f1c54ec66cc10bb1704c6c034724e2d2aa38873a2987a4fe5d22","source":{"kind":"arxiv","id":"2606.06779","version":1},"attestation_state":"computed","paper":{"title":"Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Martin Wang, Nimesh Sinha, Raghav Saboo, Sudeep Das","submitted_at":"2026-06-04T23:39:52Z","abstract_excerpt":"In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation. A key challenge lies in solving the \"cold start\" problem for users. This paper introduces a novel framework for enhancing recommendation quality by transferring knowledge from data-rich verticals (e.g., restaurants at DoorDash) to data-sparse ones. We leverage Large Language Models (LLMs) to perform generative inference, synthesizing sparse, high-dimensional features that encapsulate latent user affinities. Specifi"},"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":"2606.06779","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-04T23:39:52Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"96a7846ff055dcbc9ab6452c178f33e4009ea519a04ce1071966e57493da6ab4","abstract_canon_sha256":"c2c29e91640bad9749262d5ac96cec5a493476488da62175596d860a71e12ff2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-08T01:04:27.831851Z","signature_b64":"BCHFobDnMYuihmV6ZWmQGjuBFCzarWGnADAZVSAB6RTLqJfARSDti9GK04heVPerh47r5B5bU92jgXy8CNwKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"47634f311016f1c54ec66cc10bb1704c6c034724e2d2aa38873a2987a4fe5d22","last_reissued_at":"2026-06-08T01:04:27.830980Z","signature_status":"signed_v1","first_computed_at":"2026-06-08T01:04:27.830980Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mind the Gap: Bridging Behavioral Silos with LLMs in Multi-Vertical Recommendations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.IR","authors_text":"Martin Wang, Nimesh Sinha, Raghav Saboo, Sudeep Das","submitted_at":"2026-06-04T23:39:52Z","abstract_excerpt":"In multi-vertical e-commerce platforms like DoorDash, relatively newer product verticals such as grocery and retail present a significant opportunity for personalization innovation. A key challenge lies in solving the \"cold start\" problem for users. This paper introduces a novel framework for enhancing recommendation quality by transferring knowledge from data-rich verticals (e.g., restaurants at DoorDash) to data-sparse ones. We leverage Large Language Models (LLMs) to perform generative inference, synthesizing sparse, high-dimensional features that encapsulate latent user affinities. Specifi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06779","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/2606.06779/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":"2606.06779","created_at":"2026-06-08T01:04:27.831114+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06779v1","created_at":"2026-06-08T01:04:27.831114+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06779","created_at":"2026-06-08T01:04:27.831114+00:00"},{"alias_kind":"pith_short_12","alias_value":"I5RU6MIQC3Y4","created_at":"2026-06-08T01:04:27.831114+00:00"},{"alias_kind":"pith_short_16","alias_value":"I5RU6MIQC3Y4KTWG","created_at":"2026-06-08T01:04:27.831114+00:00"},{"alias_kind":"pith_short_8","alias_value":"I5RU6MIQ","created_at":"2026-06-08T01:04:27.831114+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/I5RU6MIQC3Y4KTWGNTAQXMLQJR","json":"https://pith.science/pith/I5RU6MIQC3Y4KTWGNTAQXMLQJR.json","graph_json":"https://pith.science/api/pith-number/I5RU6MIQC3Y4KTWGNTAQXMLQJR/graph.json","events_json":"https://pith.science/api/pith-number/I5RU6MIQC3Y4KTWGNTAQXMLQJR/events.json","paper":"https://pith.science/paper/I5RU6MIQ"},"agent_actions":{"view_html":"https://pith.science/pith/I5RU6MIQC3Y4KTWGNTAQXMLQJR","download_json":"https://pith.science/pith/I5RU6MIQC3Y4KTWGNTAQXMLQJR.json","view_paper":"https://pith.science/paper/I5RU6MIQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06779&json=true","fetch_graph":"https://pith.science/api/pith-number/I5RU6MIQC3Y4KTWGNTAQXMLQJR/graph.json","fetch_events":"https://pith.science/api/pith-number/I5RU6MIQC3Y4KTWGNTAQXMLQJR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I5RU6MIQC3Y4KTWGNTAQXMLQJR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I5RU6MIQC3Y4KTWGNTAQXMLQJR/action/storage_attestation","attest_author":"https://pith.science/pith/I5RU6MIQC3Y4KTWGNTAQXMLQJR/action/author_attestation","sign_citation":"https://pith.science/pith/I5RU6MIQC3Y4KTWGNTAQXMLQJR/action/citation_signature","submit_replication":"https://pith.science/pith/I5RU6MIQC3Y4KTWGNTAQXMLQJR/action/replication_record"}},"created_at":"2026-06-08T01:04:27.831114+00:00","updated_at":"2026-06-08T01:04:27.831114+00:00"}