{"id":"W7132926928","doi":"","title":"FCRC: A Fully Connected Recurrent Convolutional Network for Recommendation","year":2023,"lang":"","type":"dissertation","venue":"TSpace","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Pooling; Knowledge graph; Graph; Convolutional neural network; Recommender system; Feature learning; Recurrent neural network; Deep learning","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":[0.0003921825,0.0007240294,0.0004824568,0.0004214184,0.0002978738,0.0006061457,0.001605934,0.001067982,0.002581555],"category_scores_gemma":[0.001381172,0.0004248508,0.0004878052,0.0006309039,0.000388248,0.001079843,0.0004914587,0.001435669,0.00120509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001131833,"about_ca_system_score_gemma":0.0009465042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03366956,"about_ca_topic_score_gemma":0.04415755,"domain_scores_codex":[0.9997955,0.00002816305,0.000009661479,0.0000733633,0.00005366437,0.00003968069],"domain_scores_gemma":[0.9996785,0.0001065556,0.00003711973,0.00006315591,0.00009209819,0.00002258684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002164188,0.0001604888,0.001192258,0.0001928082,0.0001975547,0.000242372,0.0001203628,0.439843,0.02381321,0.01700579,0.01966944,0.4973463],"study_design_scores_gemma":[0.000004225121,0.00002311214,0.000129944,0.000006139641,0.00001113466,0.00002032892,0.000004058558,0.9944495,0.002026777,0.002000167,0.001318166,0.000006438088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02421744,0.001606074,0.9632033,0.0005098324,0.0001665601,0.0000576254,0.0004828587,0.004739097,0.005017129],"genre_scores_gemma":[0.569873,0.001362505,0.3990273,0.0005892113,0.0001370879,0.0001381415,0.002017776,0.0003336814,0.02652119],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03366956,"threshold_uncertainty_score":0.06694716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0661672324596729,"score_gpt":0.3773195355604196,"score_spread":0.3111523031007467,"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."}}