{"id":"W4311680959","doi":"10.22215/etd/2020-15260","title":"Learning Recommender Systems with Deep Structured Low Rank Matrix Approximation","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Recommender system; Low-rank approximation; Deep learning; Rank (graph theory); Artificial intelligence; Computer science; Matrix (chemical analysis); Learning to rank; Matrix completion; Machine learning; Matrix decomposition; Algorithm; Pattern recognition (psychology); Mathematics; Ranking (information retrieval); Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001124211,0.0008275045,0.001094164,0.0004167485,0.0003391867,0.0009729276,0.001130478,0.001262672,0.001712514],"category_scores_gemma":[0.003794259,0.0006408295,0.0007330299,0.0006156299,0.0004720931,0.001766849,0.0008549857,0.002221139,0.001304127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006002125,"about_ca_system_score_gemma":0.0007268203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008198302,"about_ca_topic_score_gemma":0.01445993,"domain_scores_codex":[0.9994426,0.00018877,0.0000287166,0.0001448737,0.0001358728,0.00005912258],"domain_scores_gemma":[0.9985916,0.0007264214,0.0001266614,0.0002210327,0.0002745539,0.0000597868],"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":[0.0002165349,0.0002796558,0.002851025,0.0001804905,0.0003344866,0.0001129422,0.0001886487,0.6939881,0.009985182,0.02276313,0.006088091,0.2630117],"study_design_scores_gemma":[0.000005469048,0.00003137998,0.0001102786,0.000003933883,0.0000095572,0.00001074572,0.000004792723,0.9960182,0.0004075554,0.003018961,0.0003737396,0.000005307616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01957166,0.0006273774,0.9771558,0.0003079128,0.00006066846,0.0000333153,0.0001114974,0.0008532344,0.001278513],"genre_scores_gemma":[0.5815148,0.0008996447,0.4047908,0.0006017656,0.000229041,0.0001432026,0.000787459,0.000116078,0.01091712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008198302,"threshold_uncertainty_score":0.01630116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009977118373057465,"score_gpt":0.2464960187748538,"score_spread":0.2365189004017964,"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."}}