{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002087362,0.0003443472,0.0005778768,0.0002217642,0.0002759512,0.001063328,0.0006943814,0.0003975201,0.000008397428],"category_scores_gemma":[0.00008964029,0.0003723522,0.000293053,0.00007723083,0.00002319853,0.000453697,0.0005778157,0.0003257135,0.000001681947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122391,"about_ca_system_score_gemma":0.00005778565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001530135,"about_ca_topic_score_gemma":0.0001137491,"domain_scores_codex":[0.9974446,0.00006030452,0.0006820136,0.00111191,0.00016175,0.0005394768],"domain_scores_gemma":[0.9983084,0.0007123483,0.000259397,0.0003919336,0.0002137102,0.0001142456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004312532,0.000552531,0.001495576,0.01442794,0.002433444,0.000001995719,0.01153213,0.0004568262,0.00748455,0.407906,0.05843269,0.494845],"study_design_scores_gemma":[0.002265128,0.0007277678,0.0002606707,0.0005947729,0.00008165836,0.000004855641,0.0001452203,0.1288366,0.01010117,0.1720818,0.6836865,0.00121386],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001616628,0.00003478802,0.9854646,0.006575114,0.0007655362,0.004067366,0.00007820504,0.001064635,0.0003331209],"genre_scores_gemma":[0.03452384,0.0001602531,0.9542439,0.0005313333,0.0003463967,0.005639955,0.0003940123,0.0001237973,0.004036524],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6252538,"threshold_uncertainty_score":0.9999737,"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."}}