{"id":"W2955825940","doi":"10.24908/iqurcp.13261","title":"Personalizing Chatbot Conversations with IBM Watson","year":2019,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"AI in Service Interactions","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Watson; Chatbot; Computer science; IBM; Personalization; Human–computer interaction; World Wide Web; User interface; Interactivity; Multimedia; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003116943,0.000652577,0.0003811024,0.0006975337,0.001212715,0.001889611,0.0007140083,0.0006286686,0.005912537],"category_scores_gemma":[0.01040483,0.0003514773,0.0001764224,0.0003655818,0.0006137231,0.001607846,0.002201418,0.0007748628,0.001862864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004885584,"about_ca_system_score_gemma":0.0004687385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001219003,"about_ca_topic_score_gemma":0.002034058,"domain_scores_codex":[0.9982772,0.001016746,0.00007631402,0.0002359378,0.0002740664,0.0001198301],"domain_scores_gemma":[0.996124,0.002506128,0.000200689,0.0004540098,0.000355239,0.000359897],"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":[0.004251212,0.001575352,0.02529179,0.000610858,0.00009265562,0.001657927,0.07427926,0.005364596,0.266219,0.012827,0.01980087,0.5880296],"study_design_scores_gemma":[0.0006773057,0.006682207,0.06107653,0.0003984871,0.0005101017,0.00294993,0.04289516,0.2383029,0.3410977,0.01799694,0.2868686,0.0005441798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.805461,0.0003097914,0.1433537,0.000627849,0.0001329752,0.0006689248,0.0001872627,0.01572328,0.03353517],"genre_scores_gemma":[0.9018896,0.0001599942,0.07418516,0.0001849193,0.00007293658,0.0003188341,0.0004315864,0.0007673074,0.02198981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005912537,"threshold_uncertainty_score":0.01977944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07984889944994816,"score_gpt":0.3519050561530785,"score_spread":0.2720561567031303,"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."}}