{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007314987,0.0008197215,0.0005746349,0.0008831418,0.001323033,0.003194074,0.0009338786,0.001476162,0.002149452],"category_scores_gemma":[0.05855549,0.0006083582,0.0005633141,0.0004443887,0.001483872,0.002904301,0.002093023,0.001534345,0.0005394809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007906216,"about_ca_system_score_gemma":0.0004904951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00284519,"about_ca_topic_score_gemma":0.002241224,"domain_scores_codex":[0.9895507,0.00765399,0.0004342579,0.0008420296,0.001141997,0.000377122],"domain_scores_gemma":[0.9337642,0.05468008,0.003079301,0.003327331,0.003659057,0.001490033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002656491,0.000886703,0.1808166,0.001465074,0.0004499497,0.001870929,0.5604505,0.007088447,0.09229328,0.003383446,0.002915453,0.1457231],"study_design_scores_gemma":[0.0005843108,0.01032942,0.3876644,0.0008289036,0.001120728,0.007030564,0.2967841,0.1819645,0.04463888,0.01618702,0.05099239,0.001874793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677712,0.0002873636,0.02535444,0.0003224175,0.00002664171,0.0001695487,0.00007707757,0.0004816802,0.005509746],"genre_scores_gemma":[0.9914809,0.00008590572,0.007057459,0.0001157822,0.00001421937,0.00007783847,0.00007764281,0.00006851314,0.00102165],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007314987,"threshold_uncertainty_score":0.0386858,"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."}}