{"id":"W4385219046","doi":"10.1145/3583133.3596345","title":"Personalized Group Itinerary Recommendation using a Knowledge-based Evolutionary Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Set (abstract data type); Space (punctuation); Normative; Recommender system; Process (computing); Group (periodic table); Artificial intelligence; Collaborative filtering; Evolutionary algorithm; Machine 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002763452,0.0001660765,0.0001499193,0.0003526561,0.0002725607,0.00007340289,0.0003467932,0.0000634608,0.0001061701],"category_scores_gemma":[0.00006712446,0.0001649363,0.00007739571,0.001813004,0.00005778437,0.000824161,0.0001970431,0.0001164944,0.0001342974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001978786,"about_ca_system_score_gemma":0.0001285746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001094694,"about_ca_topic_score_gemma":7.261952e-7,"domain_scores_codex":[0.9986078,0.0001585665,0.000225755,0.0005250427,0.0001872525,0.0002956527],"domain_scores_gemma":[0.9992095,0.000142127,0.00008826532,0.0002877059,0.0001792212,0.00009314531],"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":[0.00008228725,0.001553731,0.0009510149,0.000145629,0.0001168977,0.00002988597,0.002532921,0.6862819,0.004202152,0.1216017,0.01462134,0.1678805],"study_design_scores_gemma":[0.0008607939,0.00002329397,0.0004457129,0.000009028529,0.000003546625,0.00001113677,0.00007913499,0.9943886,0.00008573789,0.0005591988,0.003320048,0.0002137933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002290929,0.00003933493,0.9887761,0.000409145,0.0002994156,0.0002688924,0.000005660316,0.001052544,0.008919802],"genre_scores_gemma":[0.01304733,0.000007392812,0.9847397,0.0003008294,0.00009408442,0.00005802723,0.000200102,0.00002318688,0.001529312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3081067,"threshold_uncertainty_score":0.6725908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04963183026447662,"score_gpt":0.3080158847591299,"score_spread":0.2583840544946533,"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."}}