{"id":"W3035637689","doi":"10.24963/ijcai.2020/355","title":"Learning Personalized Itemset Mapping for Cross-Domain Recommendation","year":2020,"lang":"en","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Research (Canada)","funders":"Nanyang Technological University; National Research Foundation Singapore; National Research Foundation","keywords":"Computer science; Domain (mathematical analysis); Dual (grammatical number); Construct (python library); Recommender system; Data mining; Space (punctuation); Artificial intelligence; Machine learning; Mathematics","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":[0.0004558867,0.0001038241,0.0001545171,0.00004552503,0.0001891561,0.0003331501,0.0003562559,0.00005014758,0.00007401365],"category_scores_gemma":[0.00005720733,0.00009393346,0.00008778467,0.0001924632,0.00001271951,0.0004508223,0.0001251725,0.000100074,0.00002177823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000279174,"about_ca_system_score_gemma":0.00002114858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001814117,"about_ca_topic_score_gemma":0.000001150506,"domain_scores_codex":[0.9990598,0.0000735252,0.0002440822,0.0003294429,0.00009580963,0.0001973623],"domain_scores_gemma":[0.9995176,0.0001018462,0.0001024536,0.0001266678,0.00006510938,0.00008627456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004672337,0.00007639911,0.01176562,0.000341668,0.0001205936,0.000006797707,0.02063384,0.00003791322,0.009746101,0.4462803,0.1567539,0.3541901],"study_design_scores_gemma":[0.0005274839,0.0001612344,0.000190817,0.00001333983,0.000001101466,0.000005675969,0.0003245543,0.08292022,0.001339897,0.002918184,0.9114091,0.0001884027],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001575751,0.00001647632,0.9739127,0.01512552,0.0001437951,0.0003077333,0.000002092732,0.0006414903,0.008274396],"genre_scores_gemma":[0.4592708,0.00001017035,0.5346879,0.004030225,0.0002544768,0.0001475185,0.00003615172,0.00002038642,0.001542311],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7546551,"threshold_uncertainty_score":0.3830495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06387530711832184,"score_gpt":0.3069043065870841,"score_spread":0.2430289994687622,"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."}}