{"id":"W4226336143","doi":"10.1609/aaai.v36i4.20324","title":"Meta-Learning for Online Update of Recommender Systems","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Recommender system; Computer science; Flexibility (engineering); Machine learning; Artificial intelligence; Focus (optics); Meta learning (computer science); Information retrieval","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001322558,0.000201911,0.0005003147,0.0001662025,0.0003101524,0.0001274485,0.002220531,0.00004988306,0.00006309994],"category_scores_gemma":[0.0001271445,0.0001484502,0.0002815686,0.000534404,0.00007630832,0.0002499374,0.0007372509,0.0003440751,0.000002774172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005150626,"about_ca_system_score_gemma":0.00006862049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009985062,"about_ca_topic_score_gemma":0.000003730942,"domain_scores_codex":[0.9980116,0.0000640362,0.0007992853,0.0004266533,0.0004181975,0.000280192],"domain_scores_gemma":[0.9980558,0.0001520565,0.0008655195,0.000310951,0.0005636643,0.00005201388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003766206,0.0002158684,0.00004799702,0.0001053868,0.0001993358,9.986078e-8,0.0006896653,0.0004183034,0.006230823,0.9648668,0.001136943,0.02605119],"study_design_scores_gemma":[0.0000986685,0.001513595,0.00003437552,0.0001868671,0.0002806403,0.0000212957,0.003644303,0.3504885,0.3924499,0.2212039,0.02943627,0.0006416303],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05973756,0.0007627083,0.8900856,0.02637023,0.004255547,0.005067886,0.0001683283,0.0007728128,0.01277935],"genre_scores_gemma":[0.9909035,0.00003645289,0.008242825,0.000154069,0.0000413427,0.0002780164,0.000002025901,0.00001555492,0.0003262181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9311659,"threshold_uncertainty_score":0.6053625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2111784939014505,"score_gpt":0.3256859105684825,"score_spread":0.1145074166670319,"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."}}