{"id":"W4382239737","doi":"10.1609/aaai.v37i4.25595","title":"Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Recommender system; Forgetting; Machine learning; Artificial intelligence; Imitation; Data mining","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":[0.0015356,0.001084867,0.001397001,0.0007076039,0.0005253141,0.0006112637,0.002718176,0.001308595,0.001403884],"category_scores_gemma":[0.007876774,0.0006523607,0.0006805135,0.0007522732,0.0006430869,0.002066014,0.0009321114,0.00188499,0.0006644508],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007816874,"about_ca_system_score_gemma":0.0008981969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0118848,"about_ca_topic_score_gemma":0.01824782,"domain_scores_codex":[0.999361,0.0002076641,0.00004274317,0.0001599007,0.000169532,0.0000592244],"domain_scores_gemma":[0.9974516,0.00151779,0.0001492842,0.0003683431,0.0004310157,0.00008204295],"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":[0.0002712552,0.0004602125,0.003481166,0.0002318622,0.0002110409,0.0001622505,0.0002079514,0.6342378,0.007077671,0.004872955,0.004402549,0.3443832],"study_design_scores_gemma":[0.00001795039,0.00006946563,0.0002430617,0.000005374385,0.00002174827,0.00002856078,0.000008753264,0.9959608,0.001123279,0.001965465,0.000546048,0.000009597667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08181622,0.002406886,0.908878,0.000537223,0.0001567892,0.0001894219,0.0001798876,0.002938573,0.002897025],"genre_scores_gemma":[0.8015294,0.0006388837,0.1939272,0.0003689511,0.0001222135,0.0002154966,0.0004588669,0.0001069674,0.002631981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0118848,"threshold_uncertainty_score":0.02363127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0907367597237587,"score_gpt":0.3040456835329652,"score_spread":0.2133089238092065,"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."}}