{"id":"W4403181748","doi":"10.1007/978-3-031-63821-3_9","title":"Affective Computing for Health Management via Recommender Systems: Exploring Challenges and Opportunities","year":2024,"lang":"en","type":"book-chapter","venue":"The Springer series in applied machine learning","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute; Toronto Metropolitan University","funders":"","keywords":"Recommender system; Computer science; Data science; Human–computer interaction; Knowledge management; World Wide Web","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":[0.002889345,0.0005283099,0.0008396347,0.0005276491,0.0004545808,0.00501554,0.001136428,0.001227589,0.005882251],"category_scores_gemma":[0.004903765,0.0002967095,0.0004044005,0.00133385,0.0008596477,0.003507681,0.001163224,0.002608015,0.001668218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006518961,"about_ca_system_score_gemma":0.000604449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001271505,"about_ca_topic_score_gemma":0.002529642,"domain_scores_codex":[0.9991952,0.0004466749,0.00002991356,0.00008328324,0.000194862,0.0000501031],"domain_scores_gemma":[0.9962668,0.003086655,0.00006459231,0.0001669534,0.0002934005,0.0001216633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007309339,0.0002193135,0.001350063,0.0008687588,0.0001120905,0.0000904344,0.0007009184,0.006069757,0.001755322,0.1883602,0.06162398,0.7387761],"study_design_scores_gemma":[0.00004444093,0.0003133696,0.003909216,0.001434458,0.0001668562,0.0004921249,0.001811558,0.2179955,0.002287739,0.4647977,0.3066173,0.0001297054],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.02523285,0.3126934,0.4594239,0.06841455,0.005589602,0.0001875796,0.0003476862,0.0008164649,0.127294],"genre_scores_gemma":[0.4954251,0.1796106,0.2661761,0.006262165,0.008762093,0.0002321347,0.0004152655,0.0002120729,0.04290453],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005882251,"threshold_uncertainty_score":0.01967812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07936457719655586,"score_gpt":0.2709405005705462,"score_spread":0.1915759233739903,"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."}}