{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006242726,0.001577844,0.002615435,0.001713207,0.0007032499,0.002001859,0.003837182,0.002550542,0.001702233],"category_scores_gemma":[0.02383775,0.001238022,0.001472527,0.001455193,0.001222674,0.003730196,0.001649165,0.003397266,0.0007264702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00194306,"about_ca_system_score_gemma":0.001328899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005299134,"about_ca_topic_score_gemma":0.007422202,"domain_scores_codex":[0.9968149,0.001486357,0.0002602243,0.0006479812,0.000612473,0.0001781083],"domain_scores_gemma":[0.9871778,0.009233765,0.0008007491,0.00129914,0.001243928,0.0002446622],"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":[0.0001772794,0.0001501703,0.002339135,0.0002208215,0.0002644266,0.00009314055,0.0001829776,0.8545013,0.001152124,0.02417118,0.001664406,0.115083],"study_design_scores_gemma":[0.00001173249,0.00003313739,0.000105392,0.00001292414,0.00002123312,0.0000178901,0.000006369287,0.9913031,0.000333804,0.00773535,0.0004091678,0.000009887258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008387997,0.001187463,0.9884448,0.0003345836,0.00005477289,0.00006568555,0.00008874787,0.0005100796,0.0009259734],"genre_scores_gemma":[0.6516968,0.001173513,0.3426536,0.0004130294,0.0002850431,0.0004654076,0.0004257457,0.0001613472,0.002725558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006242726,"threshold_uncertainty_score":0.03301507,"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."}}