{"id":"W4412030713","doi":"10.26599/tst.2024.9010216","title":"Learning Fine-Grained User Preference for Personalized Recommendation","year":2025,"lang":"en","type":"article","venue":"Tsinghua Science & Technology","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Preference; Computer science; Information retrieval; Human–computer interaction; Statistics; 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":[0.001035617,0.001023642,0.001299193,0.002265557,0.0003635228,0.0007469239,0.001078837,0.001125888,0.001237125],"category_scores_gemma":[0.004043942,0.0005343154,0.0009860368,0.003120473,0.0004000858,0.002464268,0.0007358477,0.001428408,0.0008660804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007916213,"about_ca_system_score_gemma":0.0005785534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01171073,"about_ca_topic_score_gemma":0.02935775,"domain_scores_codex":[0.9991108,0.0003117575,0.00004522867,0.0002890283,0.0001694067,0.00007383712],"domain_scores_gemma":[0.9981349,0.0009977644,0.0001712164,0.0004118329,0.0002107846,0.00007353595],"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":[0.0008099561,0.0007339093,0.0325677,0.0004588911,0.0008032619,0.0002267507,0.0004452389,0.3419496,0.01258827,0.01070299,0.01212886,0.5865846],"study_design_scores_gemma":[0.00001766277,0.000105957,0.003325046,0.00001767777,0.00007013946,0.00009198343,0.00005962925,0.9812124,0.00136365,0.01238912,0.001321686,0.00002491905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1260492,0.00191708,0.8659765,0.0003440973,0.00005881277,0.00009539085,0.001431493,0.001927124,0.00220029],"genre_scores_gemma":[0.8706129,0.0007535528,0.1240359,0.0002254772,0.00006068728,0.00006183999,0.002355886,0.00009010957,0.001803586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01171073,"threshold_uncertainty_score":0.02328515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03092815123050258,"score_gpt":0.3062304388535652,"score_spread":0.2753022876230626,"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."}}