{"id":"W4416922765","doi":"10.1109/iscipt67144.2025.11265095","title":"Unified Representation Learning for Multi-Intent Diversity and Behavioral Uncertainty in Recommender Systems","year":2025,"lang":"","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Ambiguity; Recommender system; Representation (politics); Adaptability; Stability (learning theory); Preference; User modeling; Covariance","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":[0.00366527,0.001306472,0.002042806,0.001695949,0.0007316498,0.001412436,0.002190747,0.00157646,0.0009576242],"category_scores_gemma":[0.01006366,0.0008523941,0.001690347,0.001904458,0.0008187341,0.003409322,0.002156258,0.002921589,0.0004845285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001119931,"about_ca_system_score_gemma":0.001069072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007544466,"about_ca_topic_score_gemma":0.00788852,"domain_scores_codex":[0.9974446,0.001130982,0.0001519879,0.0005815889,0.0004809861,0.0002098269],"domain_scores_gemma":[0.9953529,0.002870655,0.0003617363,0.00070078,0.0005732116,0.0001406565],"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":[0.0002438277,0.0003056637,0.004805335,0.0002336469,0.0004941357,0.0001617819,0.0006085089,0.6266955,0.003696195,0.02743712,0.004013425,0.3313049],"study_design_scores_gemma":[0.000008118554,0.00004417045,0.0002806963,0.00000983789,0.00002764759,0.00002395972,0.00001479178,0.9899023,0.0003049197,0.009062462,0.0003082076,0.00001291169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01217886,0.0003577179,0.9866239,0.000153843,0.00001754895,0.00002567525,0.00006638765,0.0002804126,0.0002956591],"genre_scores_gemma":[0.7233662,0.0006110725,0.2721755,0.0003582373,0.0001671607,0.0002516135,0.000948885,0.0001079224,0.002013398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007544466,"threshold_uncertainty_score":0.01938403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1505153536855738,"score_gpt":0.3659494912632424,"score_spread":0.2154341375776686,"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."}}