{"id":"W4377021837","doi":"10.1145/3597499","title":"User Experience and the Role of Personalization in Critiquing-Based Conversational Recommendation","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on the Web","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Mitacs; Ontario Centres of Excellence","keywords":"Personalization; Computer science; Recommender system; Conversation; Matching (statistics); World Wide Web; User experience design; Information retrieval; Human–computer interaction; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0005128519,0.00005783578,0.00007373097,0.000109102,0.0001291687,0.00004465543,0.0003300958,0.00002866002,0.00004293626],"category_scores_gemma":[0.00003453591,0.00003682182,0.00003321183,0.0004199841,0.00006965822,0.0001930388,0.000009751087,0.00007655665,0.000004082262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000186348,"about_ca_system_score_gemma":0.00002816439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001167425,"about_ca_topic_score_gemma":0.00004102857,"domain_scores_codex":[0.9993499,0.0001597725,0.0001532547,0.000133358,0.0001223145,0.00008143113],"domain_scores_gemma":[0.9991286,0.0004928885,0.00005036633,0.0002772284,0.00003777705,0.00001315259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003038589,0.0005003345,0.01199814,0.0001420354,0.0001331073,0.000003528664,0.07464401,0.002871702,0.009626993,0.5764834,0.002645124,0.3206478],"study_design_scores_gemma":[0.001689218,0.0001049667,0.005612497,0.0001204038,0.00001128227,0.000007635435,0.004839964,0.8967054,0.03556776,0.04401372,0.01109109,0.0002359973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03938607,0.00002682001,0.9238668,0.03559226,0.0001620201,0.0003794958,0.00000871565,0.0001505155,0.0004272886],"genre_scores_gemma":[0.9981503,0.00003845664,0.001281284,0.0003579711,0.0000063255,0.0001229831,0.000002771564,0.000003878177,0.00003597456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9587643,"threshold_uncertainty_score":0.150155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02208286072897469,"score_gpt":0.2623580399459307,"score_spread":0.240275179216956,"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."}}