{"id":"W4297816431","doi":"10.1145/3555371","title":"Learning to Ask: Conversational Product Search via Representation Learning","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Ask price; Product (mathematics); Computer science; Representation (politics); Human–computer interaction; Artificial intelligence; Mathematics; Business; Political science","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":[0.001042643,0.00106098,0.0009862751,0.000621932,0.0004391285,0.001042161,0.002306223,0.001687946,0.003452076],"category_scores_gemma":[0.004002404,0.0005706332,0.001493454,0.0007875132,0.0008026054,0.002994692,0.001738356,0.002523792,0.001115208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072702,"about_ca_system_score_gemma":0.0009276223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009805598,"about_ca_topic_score_gemma":0.009735475,"domain_scores_codex":[0.9991812,0.0003223028,0.00002996814,0.0002755819,0.0001007071,0.00009020438],"domain_scores_gemma":[0.9987633,0.0008390284,0.00006993748,0.0001678164,0.00009557387,0.00006429695],"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":[0.0008658821,0.0006403365,0.004779772,0.0004941431,0.0003405413,0.0004860185,0.001205004,0.3966978,0.009943069,0.02691868,0.01865506,0.5389736],"study_design_scores_gemma":[0.00001771933,0.00005439547,0.0002626104,0.00001083325,0.00002952384,0.00005529994,0.00004523589,0.9850971,0.001154058,0.01199265,0.001267197,0.00001336506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06594338,0.001795451,0.9203028,0.001040565,0.00009737394,0.0001968795,0.0008254966,0.004315357,0.005482703],"genre_scores_gemma":[0.8743323,0.0006822589,0.113629,0.0007440915,0.00009779903,0.0002696196,0.0021521,0.0002390297,0.007853879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009805598,"threshold_uncertainty_score":0.01949704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03150861734433247,"score_gpt":0.2849817503115045,"score_spread":0.2534731329671721,"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."}}