{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:3AKMFAEJJVVXPPX7GMRR2AAOU5","short_pith_number":"pith:3AKMFAEJ","schema_version":"1.0","canonical_sha256":"d814c280894d6b77beff33231d000ea7684b984bb29bffffaaf1865f7cf546e1","source":{"kind":"arxiv","id":"1708.09088","version":2},"attestation_state":"computed","paper":{"title":"A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Haekyu Park, Jinhong Jung, U Kang","submitted_at":"2017-08-30T02:08:14Z","abstract_excerpt":"Between matrix factorization or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? Does additional information help recommendation? Recommender systems play an important role in many e-commerce services such as Amazon and Netflix to recommend new items to a user. Among various recommendation strategies, collaborative filtering has shown good performance by using rating patterns of users. Matrix factorization and random walk with restart are the most representative collaborative filtering methods. Ho"},"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":"1708.09088","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2017-08-30T02:08:14Z","cross_cats_sorted":[],"title_canon_sha256":"048f1f323e0a13257273cb90c72f017a87f1bb16edd8056cbe2598ca861096cb","abstract_canon_sha256":"792d4a7c7f0afd2b747d9b2aedca4c4478c0024e62867ea2c8c74aba1a4ea309"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:18.820951Z","signature_b64":"2Xs+YT0Mgp6fniRiXDnexxCZU2M6ZurHh2eZHFPO7pj+FzX/KyfqOl+CS1E5poYqttDfQALQzBZfJokCEFtABA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d814c280894d6b77beff33231d000ea7684b984bb29bffffaaf1865f7cf546e1","last_reissued_at":"2026-05-18T00:31:18.820255Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:18.820255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comparative Study of Matrix Factorization and Random Walk with Restart in Recommender Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Haekyu Park, Jinhong Jung, U Kang","submitted_at":"2017-08-30T02:08:14Z","abstract_excerpt":"Between matrix factorization or Random Walk with Restart (RWR), which method works better for recommender systems? Which method handles explicit or implicit feedback data better? Does additional information help recommendation? Recommender systems play an important role in many e-commerce services such as Amazon and Netflix to recommend new items to a user. Among various recommendation strategies, collaborative filtering has shown good performance by using rating patterns of users. Matrix factorization and random walk with restart are the most representative collaborative filtering methods. Ho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.09088","kind":"arxiv","version":2},"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":"1708.09088","created_at":"2026-05-18T00:31:18.820371+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.09088v2","created_at":"2026-05-18T00:31:18.820371+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.09088","created_at":"2026-05-18T00:31:18.820371+00:00"},{"alias_kind":"pith_short_12","alias_value":"3AKMFAEJJVVX","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"3AKMFAEJJVVXPPX7","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"3AKMFAEJ","created_at":"2026-05-18T12:30:58.224056+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/3AKMFAEJJVVXPPX7GMRR2AAOU5","json":"https://pith.science/pith/3AKMFAEJJVVXPPX7GMRR2AAOU5.json","graph_json":"https://pith.science/api/pith-number/3AKMFAEJJVVXPPX7GMRR2AAOU5/graph.json","events_json":"https://pith.science/api/pith-number/3AKMFAEJJVVXPPX7GMRR2AAOU5/events.json","paper":"https://pith.science/paper/3AKMFAEJ"},"agent_actions":{"view_html":"https://pith.science/pith/3AKMFAEJJVVXPPX7GMRR2AAOU5","download_json":"https://pith.science/pith/3AKMFAEJJVVXPPX7GMRR2AAOU5.json","view_paper":"https://pith.science/paper/3AKMFAEJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.09088&json=true","fetch_graph":"https://pith.science/api/pith-number/3AKMFAEJJVVXPPX7GMRR2AAOU5/graph.json","fetch_events":"https://pith.science/api/pith-number/3AKMFAEJJVVXPPX7GMRR2AAOU5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3AKMFAEJJVVXPPX7GMRR2AAOU5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3AKMFAEJJVVXPPX7GMRR2AAOU5/action/storage_attestation","attest_author":"https://pith.science/pith/3AKMFAEJJVVXPPX7GMRR2AAOU5/action/author_attestation","sign_citation":"https://pith.science/pith/3AKMFAEJJVVXPPX7GMRR2AAOU5/action/citation_signature","submit_replication":"https://pith.science/pith/3AKMFAEJJVVXPPX7GMRR2AAOU5/action/replication_record"}},"created_at":"2026-05-18T00:31:18.820371+00:00","updated_at":"2026-05-18T00:31:18.820371+00:00"}