{"id":"W2807848202","doi":"10.24963/ijcai.2018/498","title":"Matrix completion with Preference Ranking for Top-N Recommendation","year":2018,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Ranking (information retrieval); Preference; Recommender system; Matrix completion; Computer science; Rank (graph theory); Matrix (chemical analysis); Learning to rank; Collaborative filtering; Sparse matrix; Information retrieval; Function (biology); Minification; Data mining; Machine learning; Artificial intelligence; Mathematics; Statistics; World Wide Web; Combinatorics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002286548,0.001365521,0.001845903,0.0009024032,0.0007055387,0.0008162265,0.001465876,0.001262821,0.003733618],"category_scores_gemma":[0.007167001,0.0006116375,0.001315455,0.001845644,0.0009700426,0.002080346,0.001103661,0.002233817,0.002442039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005989849,"about_ca_system_score_gemma":0.00154676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005365802,"about_ca_topic_score_gemma":0.006898378,"domain_scores_codex":[0.9984714,0.0006475713,0.00008476987,0.0002868502,0.000431818,0.00007756962],"domain_scores_gemma":[0.9969896,0.001558474,0.0002539059,0.0005328238,0.000521157,0.0001440833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002321364,0.0003378828,0.0009596188,0.0006605239,0.0001796762,0.0001797739,0.0002135553,0.5509443,0.009218287,0.03495821,0.01732047,0.3847955],"study_design_scores_gemma":[0.00001972847,0.00006829936,0.0001106019,0.00001160404,0.00001224925,0.0000653699,0.00002006855,0.9829353,0.001281801,0.01363643,0.001821742,0.0000168597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002177691,0.0002647824,0.9965849,0.0000821689,0.00003013867,0.00003912818,0.00005127308,0.0002321342,0.0005376802],"genre_scores_gemma":[0.1506189,0.0009584072,0.8424118,0.0002337314,0.0002432747,0.0002597368,0.0007767409,0.000163326,0.004333999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005365802,"threshold_uncertainty_score":0.01249015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06122193530950577,"score_gpt":0.3046535257941431,"score_spread":0.2434315904846374,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}