{"id":"W4309869671","doi":"10.1145/3539597.3570379","title":"One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Domain (mathematical analysis); Recommender system; Human–computer interaction; World Wide Web; 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":[0.001245525,0.001004819,0.0009213611,0.0006815244,0.0004410351,0.0008306471,0.001375362,0.00114731,0.002056277],"category_scores_gemma":[0.003803761,0.0004210125,0.0009783105,0.0008790814,0.0005489612,0.002657575,0.00134716,0.001659336,0.00196501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004187969,"about_ca_system_score_gemma":0.0008032508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006142154,"about_ca_topic_score_gemma":0.01162237,"domain_scores_codex":[0.9991757,0.0002734961,0.00004079638,0.0002934355,0.0001508294,0.00006567556],"domain_scores_gemma":[0.9985123,0.0003977356,0.00008564805,0.0006376575,0.0002729108,0.00009360582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005216065,0.0005606531,0.007785869,0.000316458,0.0004350414,0.000263334,0.0003732056,0.10855,0.02075261,0.005337999,0.0224896,0.8326137],"study_design_scores_gemma":[0.00005045892,0.0002222012,0.001595035,0.00002922997,0.00007927552,0.0002735038,0.00008973752,0.9770094,0.01036158,0.004328278,0.005908638,0.00005270237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.086468,0.002353346,0.8974589,0.000747899,0.0002576583,0.0001591256,0.0006084773,0.007734501,0.004212135],"genre_scores_gemma":[0.5880678,0.001100922,0.3963079,0.000846051,0.0001588277,0.0001452633,0.002683255,0.0003824527,0.01030739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006142154,"threshold_uncertainty_score":0.01221275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.116430890884641,"score_gpt":0.3754429046735688,"score_spread":0.2590120137889278,"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."}}