{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:M6FDAUECVUMWBMJDVQLWPHQBEA","short_pith_number":"pith:M6FDAUEC","schema_version":"1.0","canonical_sha256":"678a305082ad1960b123ac17679e0120363ed88a6156f0d31ecdc41c27c1c2b2","source":{"kind":"arxiv","id":"2606.18814","version":1},"attestation_state":"computed","paper":{"title":"LensKit-Auto","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Anass Amezian El Idrissi, Luca Quade, Max Breit, Rishikesh Giriraj Kulkarni","submitted_at":"2026-06-17T08:34:53Z","abstract_excerpt":"Recommender systems have a wide area of application, e.g. in fields like video streaming, social media, or digital marketplaces. But, for a recommender-system, finding the right algorithm with the right hyperparameters is a reoccurring challenge. There is no one-fits-all solution, since the performance of one algorithm can vary immensely on different data sets. Due to the challenges of finding the right algorithm and the broad use of recommender-systems, it is of interest to create an Automated Recommender System (AutoRecSys) that takes on the task of finding the right algorithm-hyperparameter"},"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":"2606.18814","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2026-06-17T08:34:53Z","cross_cats_sorted":[],"title_canon_sha256":"1767241b4cd8c5ce018e3bd1db69276b8172e9ffedfb288900a42ae958d67dfb","abstract_canon_sha256":"6d3489a2aba978e0b276e2d2d8c9f61b1ae7c39c537204c782b77dc0abbe8a0a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:48.259270Z","signature_b64":"mc2U2f2SY30IjPm4e5bdVo6nA8gQMP27iqj/PzsI5kQdu6D6VswjXmejp8INGadeXD0ezkfPmEi4CyHG4Ta+CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"678a305082ad1960b123ac17679e0120363ed88a6156f0d31ecdc41c27c1c2b2","last_reissued_at":"2026-06-19T16:11:48.258920Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:48.258920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LensKit-Auto","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Anass Amezian El Idrissi, Luca Quade, Max Breit, Rishikesh Giriraj Kulkarni","submitted_at":"2026-06-17T08:34:53Z","abstract_excerpt":"Recommender systems have a wide area of application, e.g. in fields like video streaming, social media, or digital marketplaces. But, for a recommender-system, finding the right algorithm with the right hyperparameters is a reoccurring challenge. There is no one-fits-all solution, since the performance of one algorithm can vary immensely on different data sets. Due to the challenges of finding the right algorithm and the broad use of recommender-systems, it is of interest to create an Automated Recommender System (AutoRecSys) that takes on the task of finding the right algorithm-hyperparameter"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18814","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/2606.18814/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":"2606.18814","created_at":"2026-06-19T16:11:48.258983+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.18814v1","created_at":"2026-06-19T16:11:48.258983+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18814","created_at":"2026-06-19T16:11:48.258983+00:00"},{"alias_kind":"pith_short_12","alias_value":"M6FDAUECVUMW","created_at":"2026-06-19T16:11:48.258983+00:00"},{"alias_kind":"pith_short_16","alias_value":"M6FDAUECVUMWBMJD","created_at":"2026-06-19T16:11:48.258983+00:00"},{"alias_kind":"pith_short_8","alias_value":"M6FDAUEC","created_at":"2026-06-19T16:11:48.258983+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/M6FDAUECVUMWBMJDVQLWPHQBEA","json":"https://pith.science/pith/M6FDAUECVUMWBMJDVQLWPHQBEA.json","graph_json":"https://pith.science/api/pith-number/M6FDAUECVUMWBMJDVQLWPHQBEA/graph.json","events_json":"https://pith.science/api/pith-number/M6FDAUECVUMWBMJDVQLWPHQBEA/events.json","paper":"https://pith.science/paper/M6FDAUEC"},"agent_actions":{"view_html":"https://pith.science/pith/M6FDAUECVUMWBMJDVQLWPHQBEA","download_json":"https://pith.science/pith/M6FDAUECVUMWBMJDVQLWPHQBEA.json","view_paper":"https://pith.science/paper/M6FDAUEC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.18814&json=true","fetch_graph":"https://pith.science/api/pith-number/M6FDAUECVUMWBMJDVQLWPHQBEA/graph.json","fetch_events":"https://pith.science/api/pith-number/M6FDAUECVUMWBMJDVQLWPHQBEA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M6FDAUECVUMWBMJDVQLWPHQBEA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M6FDAUECVUMWBMJDVQLWPHQBEA/action/storage_attestation","attest_author":"https://pith.science/pith/M6FDAUECVUMWBMJDVQLWPHQBEA/action/author_attestation","sign_citation":"https://pith.science/pith/M6FDAUECVUMWBMJDVQLWPHQBEA/action/citation_signature","submit_replication":"https://pith.science/pith/M6FDAUECVUMWBMJDVQLWPHQBEA/action/replication_record"}},"created_at":"2026-06-19T16:11:48.258983+00:00","updated_at":"2026-06-19T16:11:48.258983+00:00"}