{"id":"W4415536448","doi":"10.1145/3746027.3758172","title":"RQ-Rec: Residual Quantized Hierarchical Preference Modeling for Cross-Domain Recommendation","year":2025,"lang":"","type":"article","venue":"","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Overfitting; Recommender system; Rewriting; Domain (mathematical analysis); User modeling; Generative grammar; Generative model; Representation (politics)","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":[0.001816421,0.0008763361,0.00121307,0.0006475726,0.0003029905,0.0007779629,0.002082445,0.0008637251,0.002314782],"category_scores_gemma":[0.006345064,0.0005718357,0.001123999,0.001175116,0.0005049466,0.00193001,0.001072513,0.001513918,0.001156612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006643843,"about_ca_system_score_gemma":0.0008383156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009535086,"about_ca_topic_score_gemma":0.0165151,"domain_scores_codex":[0.9986297,0.0005499779,0.00007391955,0.000354357,0.0002968547,0.00009522367],"domain_scores_gemma":[0.9978518,0.001107414,0.0001721915,0.0004014885,0.0003931827,0.00007384311],"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":[0.0003413419,0.0002531007,0.003542447,0.0003765372,0.000344504,0.0001852967,0.0004373959,0.5861048,0.008657746,0.02376449,0.008355532,0.3676369],"study_design_scores_gemma":[0.000009437896,0.000041303,0.0002239994,0.000007093718,0.00001479699,0.00003268526,0.00001716049,0.9943469,0.0006526716,0.003849342,0.0007934956,0.00001113109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01000894,0.0004574631,0.9878119,0.0001083756,0.00003239624,0.00005091413,0.0001875646,0.0006195756,0.0007229249],"genre_scores_gemma":[0.5837884,0.0007800437,0.404375,0.0005405123,0.0001381797,0.0002973063,0.001774683,0.0002936067,0.00801221],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009535086,"threshold_uncertainty_score":0.01895916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1029613557791601,"score_gpt":0.3677786675873321,"score_spread":0.264817311808172,"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."}}