{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003054491,0.00008082481,0.0001026055,0.00006217609,0.0001402359,0.0001533885,0.0002944017,0.00003292846,0.00004670981],"category_scores_gemma":[0.000006304396,0.00005827961,0.00002265696,0.0001392718,0.00001671456,0.0004050091,0.00006208788,0.00003390995,0.00001411347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002625637,"about_ca_system_score_gemma":0.00001892731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007891731,"about_ca_topic_score_gemma":0.00007147485,"domain_scores_codex":[0.9993374,0.0000306232,0.0001514096,0.0002403497,0.00008788729,0.0001523526],"domain_scores_gemma":[0.9994382,0.00004913121,0.00007898389,0.0002534704,0.0001495194,0.00003075779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004298528,0.00007084037,0.00253114,0.00006219676,0.00003113545,4.316884e-7,0.0007764003,0.000001126598,0.00128253,0.6279395,0.03320357,0.3340581],"study_design_scores_gemma":[0.002088282,0.00293042,0.004984377,0.0002274589,0.00001703004,0.00008026406,0.0001119238,0.1802126,0.06753154,0.04327334,0.6976547,0.000888044],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001606704,0.000004210032,0.9816173,0.001561367,0.0001897807,0.0003709023,0.000001228085,0.0003522238,0.0142963],"genre_scores_gemma":[0.6486614,0.000001898214,0.3506942,0.0001962155,0.00009223189,0.00005923835,0.00000616246,0.000004734513,0.0002838565],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6644511,"threshold_uncertainty_score":0.2376574,"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."}}