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Source ablations do not prove nonlinear features useless in all recommender settings. Original template Conference17/placeholderDOI is not actual2017publication.", "domain": "Graph-based recommendation" }, "sources": [ { "citation": "LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation", "url": "https://arxiv.org/abs/2002.02126v1" } ], "statement": "# LightGCN: linear embedding propagation for collaborative filtering\n\nSimplified graph collaborative filtering to normalized linear neighborhood embedding propagation and weighted combination across depths, omitting intermediate feature transforms and nonlinear activations.\n\n## Scientific scope\n\nDistinct from SGC's attributed-node linear classifier; here learned ID embeddings and user–item interaction graphs serve collaborative filtering. Source ablations do not prove nonlinear features useless in all recommender settings. Original template Conference17/placeholderDOI is not actual2017publication.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nXiangnan He; Kuan Deng; Xiang Wang; Yan Li; Yongdong Zhang; Meng Wang\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://arxiv.org/abs/2002.02126v1)\n", "summary": "Simplified graph collaborative filtering to normalized linear neighborhood embedding propagation and weighted combination across depths, omitting intermediate feature transforms and nonlinear activations.", "title": "LightGCN: linear embedding propagation for collaborative filtering" }, "record_id": "urn:uuid:c137ee6f-e1eb-4dca-a98d-be615e70dffb", "version": 1, "source_ref": { "record_id": "urn:uuid:c137ee6f-e1eb-4dca-a98d-be615e70dffb", "version": 1, "content_hash": "sha256:6a044f1ab2a0c7cf37d54a88602e3a0f73d268ebbf93d3ac44f1115e3af0b8e7" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:2f692aa7335dce54dde410e2be42d206d85e7df6ab7d65bca5eb353088594b1c" }, "target": { "record_id": "urn:uuid:c137ee6f-e1eb-4dca-a98d-be615e70dffb", "version": 1, "content_hash": "sha256:6a044f1ab2a0c7cf37d54a88602e3a0f73d268ebbf93d3ac44f1115e3af0b8e7" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "he-et-al-lightgcn-2020", "entry_id": "he-et-al-lightgcn-2020", "revision": 1, "title": "LightGCN: linear embedding propagation for collaborative filtering", "context": "Graph-based recommendation", "track": "Human-designed research method or resource", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Simplified graph collaborative filtering to normalized linear neighborhood embedding propagation and weighted combination across depths, omitting intermediate feature transforms and nonlinear activations.", "scope": "Simplified graph collaborative filtering to normalized linear neighborhood embedding propagation and weighted combination across depths, omitting intermediate feature transforms and nonlinear activations. Distinct from SGC's attributed-node linear classifier; here learned ID embeddings and user–item interaction graphs serve collaborative filtering. Source ablations do not prove nonlinear features useless in all recommender settings. Original template Conference17/placeholderDOI is not actual2017publication.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Distinct from SGC's attributed-node linear classifier; here learned ID embeddings and user–item interaction graphs serve collaborative filtering. Source ablations do not prove nonlinear features useless in all recommender settings. 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Names alone do not establish resolved identities." } ], "target": { "record_id": "urn:uuid:c137ee6f-e1eb-4dca-a98d-be615e70dffb", "version": 1, "content_hash": "sha256:6a044f1ab2a0c7cf37d54a88602e3a0f73d268ebbf93d3ac44f1115e3af0b8e7" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "130000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }