{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:N755CBYXSUUE7CTJDAC2SKFPHK","short_pith_number":"pith:N755CBYX","schema_version":"1.0","canonical_sha256":"6ffbd1071795284f8a691805a928af3ab07b9d1d249e6eee1c18a9263a3d7fa0","source":{"kind":"arxiv","id":"1807.06839","version":1},"attestation_state":"computed","paper":{"title":"Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.SI","authors_text":"Dominik Kowald, Elisabeth Lex, Emanuel Lacic, Tomislav Duricic","submitted_at":"2018-07-18T10:05:44Z","abstract_excerpt":"User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Su"},"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":"1807.06839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SI","submitted_at":"2018-07-18T10:05:44Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"ee0152f5a46d1f2446d79f0200424170497e959f36a8fc33ff85635e87a01080","abstract_canon_sha256":"9b0e8d334fa96b5596aba0c6ddee58037014841e6857ef46b7245a13d3df1c0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:25.586081Z","signature_b64":"H3uM+0PPRKqto1sgtvD+RBscbSQAfbvR/69KyH4USzA6Yi+sry2yVkUZCi/UgfvqQRSMwIZmCpw/LzBKLjv3Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ffbd1071795284f8a691805a928af3ab07b9d1d249e6eee1c18a9263a3d7fa0","last_reissued_at":"2026-05-18T00:10:25.585414Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:25.585414Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.SI","authors_text":"Dominik Kowald, Elisabeth Lex, Emanuel Lacic, Tomislav Duricic","submitted_at":"2018-07-18T10:05:44Z","abstract_excerpt":"User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. This approach is based on finding the most relevant k users from whose rating history we can extract items to recommend. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation process such as explicit trust scores that are assigned by users to others or implicit trust relationships that result from social connections between users. Su"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.06839","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":"1807.06839","created_at":"2026-05-18T00:10:25.585522+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.06839v1","created_at":"2026-05-18T00:10:25.585522+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.06839","created_at":"2026-05-18T00:10:25.585522+00:00"},{"alias_kind":"pith_short_12","alias_value":"N755CBYXSUUE","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_16","alias_value":"N755CBYXSUUE7CTJ","created_at":"2026-05-18T12:32:40.477152+00:00"},{"alias_kind":"pith_short_8","alias_value":"N755CBYX","created_at":"2026-05-18T12:32:40.477152+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/N755CBYXSUUE7CTJDAC2SKFPHK","json":"https://pith.science/pith/N755CBYXSUUE7CTJDAC2SKFPHK.json","graph_json":"https://pith.science/api/pith-number/N755CBYXSUUE7CTJDAC2SKFPHK/graph.json","events_json":"https://pith.science/api/pith-number/N755CBYXSUUE7CTJDAC2SKFPHK/events.json","paper":"https://pith.science/paper/N755CBYX"},"agent_actions":{"view_html":"https://pith.science/pith/N755CBYXSUUE7CTJDAC2SKFPHK","download_json":"https://pith.science/pith/N755CBYXSUUE7CTJDAC2SKFPHK.json","view_paper":"https://pith.science/paper/N755CBYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.06839&json=true","fetch_graph":"https://pith.science/api/pith-number/N755CBYXSUUE7CTJDAC2SKFPHK/graph.json","fetch_events":"https://pith.science/api/pith-number/N755CBYXSUUE7CTJDAC2SKFPHK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N755CBYXSUUE7CTJDAC2SKFPHK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N755CBYXSUUE7CTJDAC2SKFPHK/action/storage_attestation","attest_author":"https://pith.science/pith/N755CBYXSUUE7CTJDAC2SKFPHK/action/author_attestation","sign_citation":"https://pith.science/pith/N755CBYXSUUE7CTJDAC2SKFPHK/action/citation_signature","submit_replication":"https://pith.science/pith/N755CBYXSUUE7CTJDAC2SKFPHK/action/replication_record"}},"created_at":"2026-05-18T00:10:25.585522+00:00","updated_at":"2026-05-18T00:10:25.585522+00:00"}