{"id":"W4388304181","doi":"10.1007/s41060-023-00467-9","title":"Tackling cold-start with deep personalized transfer of user preferences for cross-domain recommendation","year":2023,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Bridge (graph theory); Cold start (automotive); Domain (mathematical analysis); Recommender system; Task (project management); Deep learning; Domain knowledge; Code (set theory); Transfer of learning; Quality (philosophy); Artificial intelligence; Machine learning; Engineering","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":[0.002255512,0.00007535856,0.000159184,0.0003569767,0.00009756526,0.0003747098,0.001803146,0.00002352714,0.000005869271],"category_scores_gemma":[0.00008315797,0.00005351792,0.00003392113,0.0004885642,0.0001949528,0.002777387,0.0001952129,0.0000708252,4.428927e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003509264,"about_ca_system_score_gemma":0.0002408011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001301619,"about_ca_topic_score_gemma":0.00001799756,"domain_scores_codex":[0.9985549,0.00001839593,0.0003925744,0.0002184275,0.0006785326,0.0001371487],"domain_scores_gemma":[0.9982377,0.0001101996,0.0002211872,0.0002016964,0.001163617,0.00006563099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008088915,0.0008674036,0.1362819,0.0004441098,0.001718235,0.0001368978,0.01130135,0.001006381,0.04269309,0.5168454,0.01879655,0.2690998],"study_design_scores_gemma":[0.007255695,0.002545363,0.02353417,0.001408224,0.0001552784,0.0005455073,0.003728948,0.6786541,0.03340212,0.02549145,0.2221707,0.001108462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1078916,0.00003141669,0.8895956,0.00187871,0.0002856955,0.0001003142,0.00007260539,0.00001745402,0.0001266007],"genre_scores_gemma":[0.9301793,0.0002422054,0.06931373,0.0001161424,0.00008520843,0.000002361282,0.00001866461,0.000004180969,0.00003818909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8222877,"threshold_uncertainty_score":0.3613335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08551935414361735,"score_gpt":0.367103597124484,"score_spread":0.2815842429808667,"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."}}