{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001582058,0.0009123954,0.001141623,0.001248275,0.0003740093,0.000603668,0.001738651,0.001207607,0.001786568],"category_scores_gemma":[0.004384488,0.0006377848,0.0009151637,0.001632743,0.0004641352,0.00217738,0.001030735,0.001700215,0.0007682563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007904914,"about_ca_system_score_gemma":0.0008020232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00648167,"about_ca_topic_score_gemma":0.01229427,"domain_scores_codex":[0.9992827,0.0002220308,0.00003506159,0.0002978241,0.0001103189,0.00005214217],"domain_scores_gemma":[0.9985274,0.0007261476,0.00009702214,0.000369245,0.0002046558,0.00007558153],"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":[0.0003583575,0.0004587065,0.008398443,0.0001693118,0.0002902344,0.0002016563,0.0002124181,0.5507958,0.003603464,0.007752467,0.006352511,0.4214066],"study_design_scores_gemma":[0.00001453829,0.00003815742,0.0004565569,0.000006179385,0.00001491208,0.00004469724,0.00001643548,0.9933803,0.0005255727,0.004803036,0.0006903531,0.000009245111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06333437,0.0009656165,0.931776,0.000230509,0.00006918654,0.00009797248,0.0003833262,0.001401406,0.001741601],"genre_scores_gemma":[0.720317,0.000610031,0.2704695,0.0003378336,0.00007039911,0.000229471,0.002198101,0.0001580568,0.005609542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00648167,"threshold_uncertainty_score":0.0128879,"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."}}